Prescription of remote patient management based on biomarkers

Abstract

To provide a method of determining whether a subject diagnosed with a cardiovascular disease should be prescribed a remote patient management.SOLUTION: The present invention relates to a method for therapy guidance, stratification, and / or monitoring of a remote patient management for a patient diagnosed with a cardiovascular disease, the method comprising providing at least one sample of a patient, determining the level of at least one biomarker selected from a group consisting of proADM, proBNP, and proANP or fragment(s) thereof, and comparing the level of the at least one biomarker to one or more reference values, where the level indicates whether to prescribe a remote patient management for the patient.SELECTED DRAWING: None

Description

[Technical field]

[0001] explanation The present invention relates to the field of medical diagnostics, in particular to prognosis and therapy guidance based on molecular biomarkers.

[0002] The present invention relates to a method for determining whether a subject diagnosed with cardiovascular disease should be prescribed remote patient management, the method comprising measuring certain biomarkers in a sample from the patient. Thus, the present invention relates to a method for therapy guidance, stratification and / or monitoring of remote patient management for a patient diagnosed with cardiovascular disease, the method comprising providing at least one sample from the patient, determining the level of at least one biomarker or fragment(s) thereof selected from the group consisting of proADM, proBNP and proANP, and comparing the level of the at least one biomarker with one or more reference values, the level being indicative of prescribing or not prescribing remote patient management for the patient. In some embodiments, a low benefit level of the at least one biomarker is indicative of not prescribing remote patient management, while in some embodiments, a high benefit level of the at least one biomarker is indicative of prescribing remote patient management. In some embodiments, the cardiovascular disease is heart failure, in particular chronic heart failure that has resulted in hospitalization within the past 12 months. [Background technology]

[0003] Remote patient management, also known as telemedicine, allows health care providers to remotely diagnose and treat patients using telecommunications as a replacement for or in parallel with in-person visits (Cowie et al. 2016), thus increasing patients' access to care due to distance-independent adaptation. This bridges the gap of lack of access to health care for outpatients who are ill or at risk of developing complications related to prevalent diseases. Telemedicine has the potential to streamline and enable real-time consultations between caregivers through the same techniques, facilitating the delivery of timely, high-quality, personalized care to patients with diagnoses of chronic diseases. Remote patient management includes a wide range of interventions, including appropriate up-titration of medications in the outpatient setting, patient education, and management of prevalent or comorbid diseases, as well as early detection of critical events. This approach may include more interventions than remote monitoring approaches, which are typically traditionally focused on early detection of clinical deterioration. The closer possibility of interaction and real-time data exchange can improve overall patient outcomes and avoid serious health conditions leading to reduced (re)hospitalization, mortality, and costs for the health care system (Andres et al. 2018).

[0004] Heart failure is a chronic disorder whose management may benefit from remote patient management approaches (Cowie et al. 2014, van Riet EE et al. 2016, Chioncel et al. 2017, Ponikowski et al. 2016). In particular, remote patient management may help to detect early signs and symptoms of cardiac decompensation, and therefore allow appropriate treatment and care to be initiated promptly, before the full manifestation of heart failure (decompensation).

[0005] Heart failure is a common disease in adults and accounts for significant morbidity and mortality worldwide. It is estimated that 1-2% of the population in developed countries has heart failure, and this prevalence increases to 10% in the population over 70 years of age. In Europe, it is estimated that 10 million people have heart failure with ventricular dysfunction and a further 10 million have heart failure with preserved ejection fraction (HFPEF) (Hunt et al. 2009, McMurray et al. 2012).

[0006] Despite the effectiveness of many treatments for patients with heart failure with reduced ejection fraction, including angiotensin-converting enzyme (ACE) inhibitors, angiotensin receptor blockers (ARBs), β-blockers, and mineralocorticoid receptor antagonists, as well as advanced device therapies, the prevalence of heart failure is increasing due to an aging population and improvements in the treatment of acute cardiovascular events (Marco et al. 2017).

[0007] Chronic heart failure results in poor life expectancy, impaired quality of life, repeated hospitalizations and represents a considerable economic burden for society. Over the past few years, the combination of an ageing population and rising healthcare costs has amplified the need for alternative care strategies for these patients.

[0008] Given the prevalence of the disease and the complexity of the treatment approach, one of the most challenging problems in managing patients with heart failure is reducing the rates of hospitalization and readmission for worsening heart failure (Cowie et al. 2014).

[0009] Contemporary heart failure care programs focus on improving outpatient heart failure care to reduce the risk of recurrent hospitalizations due to heart failure. The readmission rate in the year following hospitalization for heart failure is approximately 50%, with a 1-year mortality rate of 15-20% (Cowie et al. 2014, Van Riet et al. 2016). The cost of hospitalization for decompensated heart failure amounts to approximately 60% of the total expenditures due to the treatment of heart failure (Gheorghiade et al. 2005). The current concept of telemedicine heart failure is a holistic program that includes remote monitoring and telemedicine interventions, guideline-based outpatient care, and grouped and structured patient education known as telepatient management (Anker et al. 2011, Andres et al. 2018).

[0010] A number of randomized controlled trials have investigated the impact of remote patient management of patients with heart failure on various clinical outcomes, including BEAT-HF (Ong et al. 2016), CardioBBEAT (Hofmann et al. 2015), TIM-HF (Koehler et al. 2011, Koehler et al. 2012a), REM-HF (Morgan et al. 2017), OptiLink HF (Bohm et al. 2016), IN-TIME (Hindricks et al. 2014) and CHAMPION (Abraham et al. 2011).

[0011] The results of these trials are not entirely consistent with regard to morbidity and mortality. This may be explained by the differences in the remote interventions used and the nature of the heterogeneous patient populations included in the trials. Despite the differences in trial designs and the remote patient management interventions used (including invasive or noninvasive remote monitoring), one common indication is that patients with unstable heart failure who were recently (i.e., ≤12 months) hospitalized for heart failure before initiating remote patient management appear to have lower rates of subsequent heart failure readmissions, reduced mortality, and improved quality of life.

[0012] A recent meta-analysis suggests that home care and disease management clinics can reduce all-cause mortality and rehospitalization after recent hospitalization for heart failure (Van Spall et al. 2017).

[0013] In 2016, the European Society of Cardiology (ESC) recommended classification IIb for remote monitoring using invasive telemedicine devices in its guidelines for the treatment of acute and chronic heart failure (Ponikowski et al. 2016). A meta-analysis of data from completed clinical trials evaluating hemodynamically guided care in patients with heart failure concluded that hemodynamically guided heart failure management using permanently implanted sensors and frequent assessment of filling pressures is superior to traditional clinical management strategies in reducing the risk of hospitalization in patients with persistent symptoms (Adamson et al. 2017).

[0014] Recently, the prospective, randomized, controlled, open (unmasked), multicenter trial, Telemedical Interventional Management in Heart Failure II (TIM-HF2), was completed and demonstrated that the use of a structured remote patient management intervention in a well-defined heart failure population can reduce the rate of days lost to unplanned cardiovascular hospitalizations and all-cause mortality (Koehler et al. 2018a, Koehler et al. 2018b).

[0015] Thus, the prior art demonstrates that remote patient management is beneficial in improving the quality and longevity of life of patients suffering from heart failure.

[0016] However, effective remote patient management is associated with substantial technical equipment, personal effort and therefore economic burden, combining remote monitoring with remote expertise and remote medical consultation. Given the prevalence of cardiovascular diseases such as heart failure in modern society, simply assigning remote patient management to each and every patient suffering from cardiovascular disease may impose a significant economic burden and technical challenge on healthcare providers.

[0017] Very few prior art documents discuss the criteria for determining which patients would benefit from remote patient management.

[0018] Koehler et al. 2012b suggest that subpopulations of patients with heart failure may benefit differently from remote patient management. In particular, median left ventricular ejection fraction (LVEF), PHQ-9 depression score, and previous HF decompensation have been proposed as clinical scores that may help guide the prescription of remote patient management.

[0019] Xiang et al. 2013, a meta-study on the benefits of remote patient management for patients with heart failure, suggested greater effectiveness in patients with higher NYHA scores and younger age.

[0020] Melilo et al. 2014 proposed a model for selecting a target group of heart failure patients who would benefit from remote patient management. Benefits were reported for patients with NYHA 2 or 3, ejection fraction (EF) <40, and age >68.

[0021] Currently, only a few such standards are known and may be useful in guiding prescriptions for remote patient management.

[0022] In light of the prior art, there is a need to provide additional, stronger guidance for determining for which subjects suffering from cardiovascular disease remote patient management would be beneficial and should be prescribed, and which may be safely excluded if remote patient management does not result in significant improvement. Summary of the Invention [Problem to be solved by the invention]

[0023] In light of the difficulties in the prior art, the technical problem underlying the present invention is to provide improved or alternative means for therapy guidance, stratification, and / or monitoring of remote patient management of patients with cardiovascular disease. Other objects of the present invention may relate to providing means for providing guidance towards whether a patient would benefit from remote patient management and / or providing guidance towards whether to prescribe remote patient management to such a patient.

[0024] The present invention therefore seeks to provide methods, kits and further means for therapy guidance, stratification and / or monitoring of remote patient management, including an indication as to whether prescribing remote patient management would be beneficial for a patient suffering from heart failure.

[0025] One object of the present invention is the use of a biomarker or a combination of biomarkers to guide decisions regarding whether a patient with cardiovascular disease should undergo remote patient management. [Means for solving the problem]

[0026] The solutions to the technical problem of the present invention are provided in the independent claims. Preferred embodiments of the invention are provided in the dependent claims.

[0027] The present invention relates to a method for therapy guidance, stratification and / or monitoring of remote patient management for patients diagnosed with cardiovascular disease, the method comprising: - providing at least one sample of said patient; - determining in said at least one sample the level of at least one biomarker or fragment(s) thereof selected from the group consisting of pro-adrenomedullin (proADM), pro-brain natriuretic peptide (proBNP) and / or pro-atrial natriuretic peptide (proANP); - comparing the level of at least one biomarker or fragment(s) thereof with one or more reference values, wherein the level of at least one biomarker or fragment(s) thereof indicates to prescribe or not prescribe remote patient management for the patient.

[0028] The patient of the method of the present invention is diagnosed with cardiovascular disease, such as heart failure, at the time of taking the sample.In principle, this patient group can benefit from remote patient management.Determining the level of biomarkers proADM, proBNP, and / or proANP can evaluate the therapeutic benefit of remote patient management, thereby guiding the decision on whether it is appropriate to prescribe remote patient management.

[0029] This method may be very useful and beneficial for large scale therapy guidance, stratification and / or monitoring of patients suffering from cardiovascular disease. Determining the levels of biomarkers provides a powerful tool that allows reliable treatment decisions.

[0030] As the data below demonstrate, the biomarkers proADM, proBNP, and / or proANP indicate, with high statistical confidence, whether remote patient management is therapeutically recommended or can be safely excluded without withholding a necessary and beneficial therapeutic approach from the patient.

[0031] It was a surprising discovery that a single measurement determining at least one biomarker or fragment(s) thereof selected from the group of proADM, proBNP and proANP allows accurate and reliable conclusions to be made as to whether a patient is likely to benefit from remote patient management or whether remote patient management will only incur additional costs without bringing significant therapeutic benefits. This prognostic ability of proADM, proBNP and / or proANP is, to the best of the inventors' knowledge, novel and surprising in terms of deciding whether to prescribe remote patient management or not.

[0032] The beneficial effect on reducing the burden on the healthcare system and ensuring the allocation of healthcare resources to those who truly need it can be illustrated by examples arising from the data detailed below.

[0033] With the use of appropriate reference values ​​for the biomarkers proADM, proBNP, and / or proANP, about one third of patients suffering from heart failure can be safely excluded from undergoing remote patient management. For these patients, the levels of the biomarkers proADM, proBNP, and / or proANP reliably predict that remote patient management will not provide a significant therapeutic benefit. Regardless of whether remote patient management is prescribed or usual care is adopted without remote patient management, patients have a statistically similar number of adverse events, including acute decompensation of chronic heart failure or death from any cause. Also, the number of hospital stays of patients identified early by the levels of the biomarkers as not benefiting from remote patient management is not shortened. This one third of patients therefore does not benefit in terms of either disease progression or quality of life. The costs and efforts associated with additionally adopted remote patient management can therefore be safely excluded without risking any disadvantage to the patient.

[0034] For example, in the TIM-HF2 study, on average, patients receiving remote patient management spend 143 minutes per year on telephone contact with a medical professional. For 1,000 patients, safely excluding nearly 30% of patients from unnecessary remote patient management could save over 700 hours of telephone effort per year. This represents time that could be used to efficiently assist and care for patients who actually need it. Furthermore, costs associated with providing devices for remote patient management, maintaining those devices, and the infrastructure to transmit and analyze the data could be significantly reduced, streamlining healthcare resources to those individuals who would benefit most.

[0035] To the best of the inventors' knowledge, the use of biomarkers selected from the group of proADM, proBNP and proANP to make a decision as to whether to prescribe remote patient management has not been disclosed or suggested by prior art studies and approaches.

[0036] In this respect, it is a further surprising finding that the biomarkers proADM, proBNP and proANP show similar potential as markers for therapy guidance, stratification or monitoring of remote patient management of patients diagnosed with cardiovascular diseases such as heart failure.

[0037] Adrenomedullin (ADM), a peptide containing 52 amino acids, was originally isolated from human pheochromocytoma (Kitamura K et al. 1993). ADM has been shown to have blood pressure lowering, immunomodulatory, metabolic and vascular effects. It is a potent vasodilator, and its widespread production in tissues helps to maintain the blood supply to individual organs. ADM stabilizes the microcirculation and protects against endothelial permeability and the resulting organ failure, showing considerable promise in the field of other diseases, especially sepsis (Andaluz-Ojeda et al. 2015) or lower respiratory tract infections (Hartmann et al. 2012, Albrich et al. 2013), hypertension, chronic kidney disease (Jougasaki et al. 2000), liver cirrhosis (Kojima et al. 1998), cancer, especially heart failure (Pousset et al. 2000, Albrecht et al. 2009).

[0038] Brain natriuretic peptide (BNP) is a polypeptide originally isolated from porcine brain by T. Sudoh and coworkers (Nature 1988;332:78-81). After cloning and sequence analysis of the cDNA encoding the peptide (T. Sudoh et al. 1989), human BNP was shown to be produced in the human heart. The ventricles produce B-type natriuretic peptide (BNP) in response to increased mechanical load and wall stretch. BNP protects the heart from the deleterious effects of overload by increasing sodium excretion and diuresis, relaxing vascular smooth muscle, inhibiting the renin-angiotensin-aldosterone system, and counteracting cardiac hypertrophy and fibrosis. BNP is synthesized by human cardiomyocytes as a 108 amino acid prohormone (proBNP) and cleaved into the 32-residue BNP of proBNP and the 76-residue N-terminal fragment of proBNP (NTproBNP).

[0039] In patients suffering from cardiac disease leading to heart failure, BNP plasma concentrations are elevated. Cardiac monocytes secrete another factor, namely atrial natriuretic factor (ANF), but the secretory response to heart failure or early heart failure seems to be much greater in the BNP system compared to the ANF system (Mukoyama et al, J Clin Invest 1991;87:1402-12). Today, BNP is recognized as a versatile biomarker of new functional dysfunction, especially left ventricular dysfunction, and as a predictor of myocardial infarction or heart failure (Vuolteenaho et al. 2005).

[0040] Atrial natriuretic polypeptide (ANP) is secreted primarily from the atria of healthy adults and the left ventricle of patients with left ventricular dysfunction. The clinical application of ANP is limited by its short half-life, but its precursor, NTproANP, is more stable in plasma and has a longer half-life. Recently, the central region sequence of pro-A-type natriuretic peptide (MRproANP), an intermediate and more stable form of natriuretic peptide, has been successfully used in clinics as a biomarker for the prognosis and diagnosis of cardiovascular diseases such as acute heart failure or coronary artery disease (Wild et al. 2011, Tzikas et al. 2013, Francis et al. 2016).

[0041] Thus, the biomarkers proADM, proBNP, and proANP show common biological functions with respect to the cardiovascular system and are upregulated during heart failure. Without intending to be bound by theory, the surprising discovery of the common potential of proADM, proBNP, and proANP for remote patient management therapy guidance, stratification, and / or monitoring of patients shown in the data may link their common functions as biomarkers related to cardiovascular disease, particularly heart failure.

[0042] This method may also be useful as a predictor of risk of adverse events and in guiding remote patient management of patients diagnosed with cardiovascular disease, preferably heart failure. If the levels of the biomarkers proADM, proBNP, and proANP indicate an increased likelihood of an adverse event, remote patient management may be prescribed, and preferably the type and / or intensity of remote patient management may be adjusted. Depending on the prognosis of the adverse event, special diagnostic tools may be used in remote patient management, and data regarding the patient's health status may be reviewed more frequently.

[0043] In this regard, the methods are particularly useful for risk assessment or stratification, whereby patients can be grouped or classified into different groups, such as risk groups requiring more frequent monitoring or additional diagnostics, or treatment groups that receive specific different therapeutic treatments depending on their classification.

[0044] Thus, the biomarker potential described herein not only allows for appropriate therapy guidance to improve patient outcomes but also aids in adopting more healthcare resource-efficient strategies.

[0045] In one embodiment, the method comprises comparing the level of at least one biomarker or fragment(s) thereof to one or more reference values ​​to determine whether the level of at least one biomarker or fragment(s) thereof is indicative of prescribing or not prescribing remote patient management for the patient.

[0046] In one embodiment, a low benefit level of at least one biomarker or fragment(s) thereof indicates not to prescribe remote patient management.

[0047] As used herein, "low benefit level" preferably refers to a level of at least one biomarker selected from the group consisting of proADM, proBNP and proANP or a fragment(s) thereof that indicates that remote patient management is not therapeutically effective and does not bring significant improvement to the patient.

[0048] As detailed in the data below, a low benefit level can be reliably established for the biomarkers described herein, indicating that the number of adverse events, such as decompensation due to heart failure or death from any cause, is not significantly reduced when remote patient management is prescribed. Similarly, for patients for whom a low benefit level of the biomarker is determined, the number of days spent in the hospital, and therefore the rate of hospitalization, is not significantly reduced.

[0049] Thus, a low benefit level of the biomarkers described herein allows for the safe exclusion of patients from remote patient management regimens who would not benefit from such a therapeutic approach, and thus the time and effort associated with remote patient management of patients with a determined low benefit level can be more efficiently allocated to patients who are actually in need.

[0050] In one embodiment, a high benefit level of at least one biomarker or fragment(s) thereof indicates prescribing remote patient management.

[0051] As used herein, "high benefit level" preferably refers to a level of at least one biomarker selected from the group consisting of proADM, proBNP and proANP or a fragment(s) thereof that indicates that remote patient management is therapeutically effective and indeed results in improved patient outcomes.

[0052] As detailed in the data below, a high benefit level can be reliably established for the biomarkers described herein, which indicates a significant reduction in the number of adverse events, such as decompensation due to heart failure or death from any cause, when remote patient management is prescribed. Similarly, for patients for whom a high benefit level of the biomarker is determined, the number of days spent in the hospital, and therefore the rate of hospitalization, is reduced.

[0053] Therefore, the high benefit levels of the biomarkers described herein make it possible to prescribe remote patient management to patients where the treatment approach is effective and ensures the best treatment outcome for the patient.

[0054] In one embodiment, a low benefit level of at least one biomarker or fragment(s) thereof indicates not prescribing remote patient management for a period of at least 10 days, preferably at least 30 days, 60 days, 90 days, 150 days, 180 days, 270 days or 365 days.

[0055] In one embodiment, a high benefit level of at least one biomarker or fragment(s) thereof indicates not prescribing remote patient management for a period of at least 10 days, preferably at least 30 days, 60 days, 90 days, 150 days, 180 days, 270 days or 365 days.

[0056] It is quite surprising that based on the measurement of the biomarkers described herein, accurate and reliable conclusions can be made as to whether a patient suffering from cardiovascular disease can benefit from remote patient management, thereby allowing a well-founded decision on whether to prescribe remote patient management or not. This prognostic ability of proADM, proBNP and / or proANP in a particular setting - particularly over a period of at least 10 days, preferably at least 30 days, 60 days, 90 days, 150 days, 180 days, 270 days or 365 days as described herein - is novel and surprising, and allows the medical resource-efficient therapy guidance of remote patient management as a new therapeutic approach to improve the clinical outcome of patients suffering from cardiovascular disease.

[0057] In some embodiments, low and / or high benefit levels of at least one biomarker or fragment(s) thereof indicate not prescribing remote patient management for a period of at least 10 days, preferably at least 30, 60, 90, 150, 180, 270 or 365 days, and it is preferred that a second sample of levels of the biomarkers described herein is determined after a period of 10 days, preferably 30, 60, 90, 150, 180, 270 or 365 days after the initial determination of the levels of the biomarkers, in order to reassess whether it is appropriate to assign remote patient management.

[0058] In some embodiments, if a patient notices that their health condition is deteriorating, it is preferred that the patient consult with their medical professional to reevaluate the decision whether to prescribe remote patient management.

[0059] According to the present invention, the term "indicating" in the context of "indicating to prescribe remote patient management" and "indicating not to prescribe remote patient management" is intended as a measure of likelihood. Preferably, the "indication" relates to the likelihood of the presence or absence of a therapeutic effect, e.g. in relation to the avoidance of adverse events, and typically should not be interpreted restrictively to definitively refer to the absolute presence or absence of a therapeutic effect, e.g. in relation to the sure avoidance of adverse events when prescribing or not prescribing remote patient management.

[0060] With the above in mind, the use of the reference values ​​disclosed herein allows reliable therapy guidance, stratification and / or monitoring of remote patient management, as well as estimation of the risk regarding the occurrence of adverse events, depending on whether remote patient management is prescribed or not, allowing appropriate action by medical professionals.

[0061] In embodiments of the present invention, deviations from the following disclosed possible reference values, for example, ±20%, 19%, 18%, 17%, 16%, 15%, 14%, 13%, 12%, 11%, 10%, 9%, 8%, 7%, 6%, 5%, 4%, 3%, 2%, or 1%, as well as the exact reference values, are also disclosed and claimed.

[0062] The reference values ​​disclosed herein preferably refer to the measurement of the protein levels of proADM, proBNP and / or proANP or fragments thereof in plasma samples obtained from patients by Thermoscientific BRAHMS KRYPTOR assay or assays suitable for automated systems. Thus, the values ​​disclosed herein may vary to some extent depending on the detection / measurement method used, and the specific values ​​disclosed herein are also intended to read corresponding values ​​determined by other methods.

[0063] All reference values ​​disclosed herein for levels of markers or biomarkers such as proADM, proBNP or proANP should be understood as "above" or "below" a particular reference value. For example, an embodiment relating to a level of proADM or fragment(s) thereof above 0.75 nmol / L should be understood as relating to a level of proADM or fragment(s) thereof equal to or greater than 0.75 nmol / L.

[0064] Embodiments Related to Determining proADM In one embodiment, the low benefit level of proADM or its fragment(s) is less than ±20% of the reference value, the reference value being selected from the range of values ​​between 0.75 nmol / L and 1.07 nmol / L. Any value within this range can be considered as a suitable threshold value, for example 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06 or 1.07 nmol / L. A particularly preferred reference value is 0.86 nmol / L ± 20%, preferably 0.75 nmol / L ± 20%.

[0065] In a preferred embodiment, the low benefit level of proADM or its fragment(s) is 1.07 nmol / L ± 20% or less, preferably 0.98 nmol / L ± 20% or less, 0.91 nmol / L ± 20% or less, 0.86 nmol / L ± 20% or less, or 0.75 nmol / L ± 20% or less.

[0066] In one embodiment, the high benefit level of proADM or its fragment(s) is greater than or equal to ±20% of the reference value, the reference value being selected from the range of values ​​between 0.75 nmol / L and 1.07 nmol / L. Any value within this range can be considered as a suitable threshold value, for example 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04, 1.05, 1.06 or 1.07 nmol / L. A particularly preferred reference value is 0.86 nmol / L ± 20%, preferably 0.75 nmol / L ± 20%.

[0067] In a preferred embodiment, the high benefit level of proADM or its fragment(s) is 1.07 nmol / L ± 20% or more, 0.98 nmol / L ± 20% or more, preferably 0.91 nmol / L ± 20% or more, 0.86 nmol / L ± 20% or more, or 0.75 nmol / L ± 20% or more.

[0068] As detailed in the data below, the low and high benefit levels of proADM allow for an accurate determination of whether remote patient management is appropriate, i.e., therapeutically effective and therefore should be prescribed, or whether remote patient management can be safely excluded and not prescribed. The above-mentioned range of reference values ​​of 0.75 nmol / l to 1.07 nmol / l, preferably over a period of at least 10 days, 30 days, most preferably at least 90 days after providing the sample, ensures a maximum sensitivity in the range of 100% to 80%, with the preferred reference value of 0.75 nmol / l for proADM or fragment(s) showing a sensitivity of 100%, while the preferred reference value of 0.86 nmol / l for proADM or fragment(s) showing a sensitivity of at least 95%, while the reference value of 0.91 nmol / l for proADM or fragment(s) showing a sensitivity of at least 91%, while the reference value of 0.98 nmol / l for proADM or fragment(s) showing a sensitivity of at least 86%. With a reference value of 1.07 nmol / l, a sensitivity of at least 80% is established.

[0069] Patients scoring below these cutoffs with sensitivity of 100% to 80% are recommended not to prescribe telemanagement (exclusion). They should be evaluated again with proADM in the usual care setting at another time point in the future. Depending on the assessed risk, patients are newly stratified into non-telemanagement or telemanagement groups.

[0070] Patients above these cut-offs with a sensitivity below 80% are recommended to be prescribed telemanagement (rule in). Evaluation with proADM should be repeated in the usual care setting at another time point in the future. Depending on the assessed risk, patients are newly stratified into telemanagement or non-telemanagement groups.

[0071] It should be noted that the preferred reference value and the associated high and low benefit levels may depend on the time period during which the level proADM indicates to prescribe or not prescribe remote patient management. In general, excluding a patient from receiving remote patient management will require a lower reference value as the upper bound of the low benefit level. In some embodiments, the upper bound of the preferred low benefit level may be as low as a known threshold for diagnosing cardiovascular disease, preferably heart failure.

[0072] The above reference values, as well as the high and low benefit levels, may be particularly preferred as indicating whether or not to prescribe remote patient management over a period of at least 10 days, preferably at least 30 days, 60 days, or at least 90 days; over significantly longer periods, for example at least 150 days, 180 days, 270 days, or 365 days, lower reference values ​​may be preferred.

[0073] In some embodiments, the low benefit level of proADM or its fragment(s) is less than ±20% of the reference value, the reference value being selected from the range of values ​​between 0.60 nmol / L and 0.75 nmol / L, preferably between 0.63 nmol / L and 0.75 nmol / L, or between 0.69 nmol / L and 0.75 nmol / L. Any value within this range can be considered as a suitable threshold value, for example 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74 or 0.75 nmol / L. Particularly preferred reference values ​​are 0.75 nmol / L ± 20%, preferably 0.72 nmol / L ± 20%, 0.69 nmol / L or 0.63 nmol / L ± 20%.

[0074] In a preferred embodiment, the low benefit level of proADM or its fragment(s) is 0.75 nmol / L ± 20% or less, preferably 0.72 nmol / L ± 20% or less, 0.69 nmol / L ± 20% or less, or 0.63 nmol / L ± 20% or less.

[0075] In some embodiments, the high benefit level of proADM or its fragment(s) is greater than or equal to ±20% of the reference value, the reference value being selected from the range of values ​​between 0.60 nmol / L and 0.75 nmol / L, preferably between 0.63 nmol / L and 0.75 nmol / L, or between 0.69 nmol / L and 0.75 nmol / L. Any value within this range can be considered as a suitable threshold value, for example 0.6, 0.61, 0.62, 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74 or 0.75 nmol / L. Particularly preferred reference values ​​are 0.75 nmol / L ± 20%, preferably 0.72 nmol / L ± 20%, 0.69 nmol / L ± 20% or 0.63 nmol / L ± 20%.

[0076] In a preferred embodiment, the high benefit level of proADM or its fragment(s) is 0.75 nmol / L ± 20% or more, preferably 0.72 nmol / L ± 20% or more, 0.69 nmol / L ± 20% or more, or 0.63 nmol / L ± 20% or more.

[0077] The reference value may be particularly preferred as an indication of prescribing or not prescribing remote patient management over a period of at least 150 days, 180 days, 270 days, and most preferably at least 365 days.

[0078] With regard to adverse occurrences or risks, such as acute decompensation due to heart failure or death from any cause, preferably over a period of at least 150 days, 180 days, 270 days, most preferably at least 365 days after providing the sample, the above-mentioned reference value range of 0.69 nmol / l to 0.75 nmol / L ensures a maximum sensitivity in the range of 100% to 95%, while preferred reference values ​​of 0.63 nmol / L or 0.69 nmol / L for proADM or its fragment(s) show a sensitivity of 100%, while preferred reference values ​​of 0.72 nmol / L for proADM or its fragment(s) show a sensitivity of at least 98%, while a reference value of 0.75 nmol / L for proADM or its fragment(s) show a sensitivity of at least 95%.

[0079] As detailed in the Examples, the relationship between sensitivity and reference value may also depend on the consideration of adverse events, i.e. the endpoints of the selected scenarios regarding, for example, days lost per year or death from any cause (see Table 15).

[0080] Embodiments Related to Determination of proBNP In one embodiment, the low benefit level of proBNP or fragment(s) thereof is less than ±20% of the reference value, the reference value being selected from the range of values ​​from 237.6 pg / ml to 1595.8 pg / ml. Any value within this range can be considered as a suitable threshold value, for example, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000, 1025, 1050, 1075, 1100, 1107.9, 1110, 1125, 1150, 1175, 1200, 1225, 1250, 1275, 1300, 1325, 1350, 1375, 1400, 1402.95, 1403, 1425, 1450, 1475, 1500, 1525, 1550 or 1575 pg / ml. Particularly preferred reference values ​​are 1595.8 pg / ml, preferably 1402.95 pg / mol, 1107.9 pg / mol, 609.4 pg / ml, 237.6 pg / ml.

[0081] In preferred embodiments, the low benefit level of proBNP or fragment(s) thereof is 1595.8 pg / ml ± 20% or less, 1402.95 pg / mol ± 20% or less, 1107.9 pg / mol ± 20% or less, 609.4 pg / ml ± 20% or less, 237.6 pg / ml ± 20% or less.

[0082] In one embodiment, the high benefit level of proBNP or fragment(s) thereof is greater than or equal to ±20% of the reference value, the reference value being selected from the range of values ​​from 237.6 pg / ml to 1595.8 pg / ml. Any value within this range can be considered as a suitable threshold value, for example, 250, 275, 300, 325, 350, 375, 400, 425, 450, 475, 500, 525, 550, 575, 600, 625, 650, 675, 700, 725, 750, 775, 800, 825, 850, 875, 900, 925, 950, 975, 1000, 1025, 1050, 1075, 1100, 1107.9, 1110, 1125, 1150, 1175, 1200, 1225, 1250, 1275, 1300, 1325, 1350, 1375, 1400, 1402.95, 1403, 1425, 1450, 1475, 1500, 1525, 1550 or 1575 pg / ml. Particularly preferred reference values ​​are 1595.8 pg / ml, preferably 1402.95 pg / mol, 1107.9 pg / mol, 609.4 pg / ml, 237.6 pg / ml.

[0083] In preferred embodiments, high benefit levels of proBNP or fragment(s) thereof are 1595.8 pg / ml ± 20% or more, 1402.95 pg / mol ± 20% or more, 1107.9 pg / mol ± 20% or more, 609.4 pg / ml ± 20% or more, 237.6 pg / ml ± 20% or more.

[0084] As detailed in the data below, the low and high benefit levels of proBNP allow an accurate determination of whether remote patient management is appropriate, i.e. therapeutically effective and should be prescribed, or whether remote patient management can be safely excluded and not prescribed. The above-mentioned range of reference values ​​of 237.6 pg / ml to 1595.8 pg / ml ensures a maximum sensitivity in the range of 100% to 80%, preferably for a period of at least 10 days, 30 days, most preferably at least 90 days after sample provision, with respect to adverse occurrences or risks such as acute decompensation due to heart failure or death from any cause, with the preferred reference value of 237.6 pg / ml for proBNP indicating a sensitivity of 100%, while the preferred reference value of 609.4 pg / ml for proBNP indicating a sensitivity of 95%. With a reference value of 1595.8 pg / ml, a sensitivity of 80% is established.

[0085] Patients who score below these cutoffs with sensitivity between 100% and 80% are recommended not to prescribe remote patient management (exclusion). They should undergo repeat evaluation with proBNP in the usual care setting at another time point in the future. Depending on the assessed risk, patients are newly stratified into non-telepatient management or remote patient management groups.

[0086] Patients above these cut-offs with a sensitivity below 80% are recommended to receive remote management (accept) and should undergo a repeat proBNP evaluation in the usual care setting at another time point in the future. Depending on the assessed risk, patients are newly stratified into remote management or non-remote management groups.

[0087] It should be noted that the preferred reference value and the associated high and low benefit levels may depend on the period in which the proBNP level indicates to prescribe or not prescribe remote patient management. In general, excluding a patient from receiving remote patient management will require a lower reference value as the upper limit of the low benefit level. In some embodiments, the upper limit of the preferred low benefit level may be as low as the known threshold for diagnosing cardiovascular disease, preferably heart failure (for BNP, for example, 125 pg / mol according to the 2016 ESC Guidelines, Ponikowski et al. 2016).

[0088] The above reference values, as well as the high and low benefit levels, may be particularly preferred as indicating whether or not to prescribe remote patient management for a period of at least 10 days, preferably at least 30 days, 60 days, or at least 90 days. Over a significantly longer period, for example at least 150 days, 180 days, 270 days, or 365 days, lower reference values ​​may be preferred.

[0089] In some embodiments, the low benefit level of proBNP or fragment(s) thereof is less than ±20% of the reference value, the reference value being selected from the range of values ​​125 pg / ml to 383.3 pg / ml or 125 pg / ml to 413.7 pg / ml. Any value within this range can be considered as a suitable threshold value, for example 125, 125.1, 135, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 245, 255, 265, 275, 285, 295, 305, 315, 325, 335, 345, 355, 365, 375, 380, 383.3, 385, 390, 395, 400, 405, 410 or 413.7 pg / ml. Particularly preferred reference values ​​are 413.7 pg / ml ± 20%, preferably 383.3 pg / ml ± 20%, 145.4 pg / ml ± 20% or 125.1 pg / mL ± 20%.

[0090] In a preferred embodiment, the low benefit level of proBNP or fragment(s) thereof is 413.7 pg / ml ± 20% or less, preferably 383.3 pg / ml ± 20% or less, 145.4 pg / ml ± 20% or less or 125.1 pg / mL ± 20% or less.

[0091] In some embodiments, the high benefit level of proBNP or fragment(s) thereof is greater than or equal to ±20% of the reference value, the reference value being selected from the range of values ​​125 pg / ml to 237.6 pg / ml, 125.1 pg / ml to 383.3 pg / ml, or 125.1 pg / ml to 413.7 pg / ml. Any value within this range can be considered as a suitable threshold value. For example, 125, 125.1, 135, 145, 155, 165, 175, 185, 195, 205, 215, 225, 235, 245, 255, 265, 275, 285, 295, 305, 315, 325, 335, 345, 355, 365, 375, 380, 383.3, 385, 390, 395, 400, 405, 410 or 413.7 pg / ml pg / ml. Particularly preferred reference values ​​are 413.7 pg / ml ± 20%, 383.3 pg / ml ± 20%, preferably 145.4 pg / ml ± 20% or 125.1 pg / mL ± 20%.

[0092] In a preferred embodiment, the high benefit level of proBNP or fragment(s) thereof is 413.7 pg / ml ± 20% or more, 383.3 pg / ml ± 20% or more, preferably 145.4 pg / ml ± 20% or more or 125.1 pg / mL ± 20% or more.

[0093] The reference value may be particularly preferred as an indication of prescribing or not prescribing remote patient management over a period of at least 150 days, 180 days, 270 days, and most preferably at least 365 days.

[0094] With regard to adverse occurrences or risks, such as acute decompensation due to heart failure or death from any cause, preferably over a period of at least 150 days, 180 days, 270 days, most preferably at least 365 days after providing the sample, the above-mentioned reference value ranges of 125.1 pg / ml to 383.3 pg / ml or 125.1 pg / ml to 413.7 pg / ml ensure a maximum sensitivity in the range of 100% to 95%, while the preferred reference value of 125.1 pg / mL for proBNP or fragment(s) indicates a sensitivity of 100%, while the preferred reference value of 145.4 pg / mL for proBNP or fragment(s) thereof indicates a sensitivity of at least 98%, while the reference value of 383.3 pg / ml or 413.7 pg / ml for proBNP or fragment(s) thereof indicates a sensitivity of at least 95%.

[0095] As detailed in the Examples, the relationship between sensitivity and reference value may also depend on the consideration of adverse events, i.e. the endpoints of the selected scenarios regarding, for example, days lost per year or death from any cause (see Table 15).

[0096] Embodiments Related to Determination of proANP In one embodiment, the low benefit level of proANP or its fragment(s) is less than ±20% of the reference value, the reference value being selected from the range of values ​​from 106.9 pmol / L to 248.3 pmol / L. Any value within this range can be considered as a suitable threshold value, for example 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 186.2, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 235.6, 240, 245, 250 pmol / L. A particularly preferred reference value is 158.5 pmol / L, preferably 106.9 pmol / L.

[0097] In a preferred embodiment, the low benefit level of proANP or fragment(s) thereof is 158.5 pmol / L ± 20% or less, preferably 106.9 pmol / L ± 20% or less.

[0098] In one embodiment, the high benefit level of proANP or its fragment(s) is greater than or equal to ±20% of the reference value, the reference value being selected from the range of values ​​from 106.9 pmol / L to 248.3 pmol / L. Any value within this range can be considered as a suitable threshold value, for example 110, 115, 120, 125, 130, 135, 140, 145, 150, 155, 160, 165, 170, 175, 180, 185, 190, 195, 200, 205, 210, 215, 220, 225, 230, 235, 240, 245, 250 pmol / L. Particularly preferred reference values ​​are 248.3 pmol / L, preferably 235.6 pmol / L, 186.2 pmol / L, 158.5 pmol / L or 106.9 pmol / L.

[0099] In a preferred embodiment, the high benefit level of proANP or fragment(s) thereof is 248.3 pmol / L ± 20% or more, preferably 235.6 pmol / L ± 20% or more, 186.2 pmol / L ± 20% or more, 158.5 pmol / L or more, 106.9 pmol / L ± 20% or more.

[0100] As detailed in the data below, the low and high benefit levels of proANP allow for an accurate decision whether remote patient management is appropriate, i.e. therapeutically effective and therefore should be prescribed, or whether remote patient management can be safely excluded and not prescribed. With regard to adverse occurrences or risks, such as acute decompensation due to heart failure or death from any cause, preferably over a period of at least 10 days, 30 days, most preferably at least 90 days after sample provision, the above reference value range of 106.9 pmol / L to 248.3 pmol / L ensures a maximum sensitivity in the range of 100% to 80%, while the preferred reference value of 106.9 pmol / L for proANP shows a sensitivity of 100%, while the preferred reference value of 158.5 pmol / L for proANP shows a sensitivity of 95%, while the reference value of 186.2 pmol / L shows a sensitivity of 90% and the reference value of 235.6 pmol / L shows a sensitivity of 85%. With a reference value of 248.3 pmol / L, a sensitivity of 80% is established.

[0101] Patients who score below these cutoffs with a sensitivity of 100% to 80% are recommended to exclude telemanagement and should be evaluated again with proANP in the usual care setting at another time point in the future. Depending on the assessed risk, patients are newly stratified into non-telemanagement or telemanagement groups.

[0102] Patients above these cut-offs with a sensitivity below 80% are recommended to be prescribed telemanagement (accepted) and should undergo a repeat evaluation with proANP in the usual care setting at another time point in the future. Depending on the assessed risk, patients are newly stratified into telemanagement or non-telemanagement groups.

[0103] It should be noted that the preferred reference value and the associated high and low benefit levels may depend on the time period during which the level of proANP indicates whether or not to prescribe remote patient management. In general, excluding a patient from receiving remote patient management will require a lower reference value as the upper bound of the low benefit level. In some embodiments, the upper bound of the preferred low benefit level may be as low as a known threshold for diagnosing cardiovascular disease, preferably heart failure.

[0104] The above reference values, as well as the high and low benefit levels, may be particularly preferred as indicating whether or not to prescribe remote patient management over a period of at least 10 days, preferably at least 30 days, 60 days, or at least 90 days; over significantly longer periods, for example at least 150 days, 180 days, 270 days, or 365 days, lower reference values ​​may be preferred.

[0105] In some embodiments, the low benefit level of proANP or its fragment(s) is less than ±20% of the reference value, the reference value being selected from the range of values ​​between 80 pmol / L and 106.9 pmol / L. Any value within this range can be considered as a suitable threshold value, for example 80, 85, 90, 95, 100, 105 or 106.9 pmol / L.

[0106] In some embodiments, the high benefit level of proANP or its fragment(s) is greater than or equal to ±20% of the reference value, the reference value being selected from the range of values ​​between 80 pmol / L and 106.9 pmol / L. Any value within this range can be considered as a suitable threshold value, for example 80, 85, 90, 95, 100, 105 or 106.9 pmol / L.

[0107] It is a further surprising discovery that the same reference value can be used to define low and high benefit levels of proADM, proBNP, and / or proANP.Herein, low benefit levels correspond to levels below a specified reference value, while high benefit levels correspond to levels above the same specified reference value.Using the same reference value as the threshold for defining low and high benefit levels simplifies the decision-making process and eliminates ambiguous gray areas.

[0108] In some embodiments, a high benefit level of at least one biomarker selected from the group consisting of proADM, proBNP, and proANP is above the disclosed preferred reference values ​​but below the upper threshold value, which indicates a transition of the patient to a high-risk state for which hospitalization may be appropriate.

[0109] In one embodiment, the cardiovascular disease is heart failure.

[0110] In one embodiment, the cardiovascular disease is heart failure and the patient has been hospitalized within the past 12 months as a result of suffering from heart failure.

[0111] The term hospitalization preferably refers to the maintenance of control and / or evaluation of a patient's condition within a hospital, regardless of whether the patient is in the emergency room, a ward, an intensive care unit, or other area of ​​a hospital or clinic.

[0112] In one embodiment, the cardiovascular disease is chronic heart failure.

[0113] In one embodiment, the patient has been diagnosed with cardiovascular disease and has been hospitalized with an adverse cardiovascular outcome at least 3 months, preferably at least 6 months, 9 months or 12 months prior to providing the sample.

[0114] In one embodiment, the cardiovascular disease is congestive heart failure.

[0115] In one embodiment, the cardiovascular disease is class II or class III heart failure according to the New York Heart Association (NYHA) heart failure classification system.

[0116] In one embodiment, the cardiovascular disease is heart failure with reduced ejection fraction. (HFrEF) and / or heart failure with preserved ejection fraction (HFpEF).

[0117] In one embodiment, the cardiovascular disease is heart failure with an increased risk of adverse outcomes, preferably selected from the group consisting of acute decompensation and / or death.

[0118] In one embodiment, the cardiovascular disease is heart failure associated with a high risk of adverse outcomes including unplanned hospitalization due to a cardiovascular event, preferably decompensated heart failure, coronary artery disease, stroke or transient ischemic attack (TIA), arrhythmia, pulmonary embolism, endocarditis, or other cardiovascular event.

[0119] In a preferred embodiment of the invention, the sample is selected from the group consisting of a blood sample, such as a whole blood sample, a serum sample or a plasma sample, a saliva sample and a urine sample.

[0120] In one embodiment, determining the level of proADM or its fragment(s) comprises determining the level of MRproADM in the sample. As shown in the examples below, MRproADM, preferably determined using an established immunoassay product from BRAHMS GmbH (Hennigsdorf, Germany), indicates a reliable and effective prognosis by the methods described herein. Alternative ADM molecules, such as PAMP or mature adrenomedullin (including biologically active forms, also known as bioADM), including precursors or fragments, can also be used.

[0121] In one embodiment, determining the level of proBNP or its fragment(s) comprises determining the level of NTproBNP in the sample. As shown in the examples below, NTproBNP, preferably determined using established immunoassay products from BRAHMS GmbH (Hennigsdorf, Germany), exhibits a reliable and effective prognosis according to the methods described herein. Alternative BNP molecules, such as mature BNP, including precursors or fragments, can also be used.

[0122] In one embodiment, determining the level of proANP or its fragment(s) comprises determining the level of MRproANP in the sample. As shown in the examples below, MRproANP, preferably determined using an established immunoassay product from BRAHMS GmbH (Hennigsdorf, Germany), shows a reliable and effective prognosis by the methods described herein. Alternative ANP molecules, such as NTproANP, including precursors or fragments, can also be used.

[0123] In one embodiment, a first sample is isolated from a patient at a first time point and a second sample is isolated from a patient at a second time point.

[0124] In the context of the present invention, determining a lower level of a marker in a second sample compared to a first sample may indicate a decrease in the level of the respective marker in the patient over the observation period. Conversely, an increase in the level of the second sample compared to the first sample may indicate an increase in the level of the marker over the observation period.

[0125] It may be preferred that at least 1 day, 1 week, 2 weeks, 1 month, or 3 months have elapsed between the first and second time points, and it may be preferred that up to 12 months, 6 months, 3 months have elapsed, such that the preferred period between the two sample isolations is, for example, 1 week to 3 months, 1 month to 3 months, 1 week to 1 month, or any other combination of the above preferred periods.

[0126] In one embodiment, a first sample is isolated from a patient at a first time point and a second sample is isolated from a patient at a second time point, and an absolute difference, ratio, and / or rate of change in level of at least one biomarker or fragment(s) thereof selected from the group consisting of proADM, proBNP, and proANP, between the first and second time points indicates that the patient should or should not be prescribed remote patient management.

[0127] The rate of change of biomarker level(s) for the first and second time points preferably refers to the absolute difference in the level of the biomarker(s) over the time difference between the first and second time points. However, the rate of change may also relate to the relative difference in the level of the biomarker(s), e.g., the rate of increase and / or decrease over the time difference between the first and second time points.

[0128] It should be understood that any combination of the time points disclosed herein for isolating a sample with respect to the first and second time points may be preferred.

[0129] Additionally, in some embodiments, it may be preferable to isolate further samples, such as a third sample, a fourth sample, etc., at corresponding third, fourth, etc. time points, in order to use absolute differences, ratios, and / or percentage changes in biomarker levels for different time points in determining whether or not to prescribe remote patient management.

[0130] In one embodiment, the level of at least one biomarker selected from the group consisting of proADM, proBNP and proANP is indicative of prescribing a type of remote patient management, preferably indicative of the range or type of repeatedly collected data regarding the patient's health status, the frequency of review of the data by a medical professional or automated medical system, the frequency of dosage revisions, and / or the frequency of remote patient consultations.

[0131] As disclosed herein, a high benefit level of a biomarker indicates a therapeutic effect and justifies the prescription of remote patient management, in some embodiments, the level of the biomarker not only indicates the prescription of remote patient management, but also allows for further specification of the kind and / or type of remote patient management.

[0132] Biomarker levels at the lower end of the high benefit level preferably indicate less intensive remote patient management, which may be characterized by less collection of data regarding the patient's health status, less frequent review of the data by medical personnel or automated medical systems, and less frequent dosage revisions or remote patient visits, as compared to biomarker levels at the upper end of the high benefit level.

[0133] Such embodiments thus further streamline remote patient management, allowing for optimal support to be provided to each patient with appropriate efforts, reflecting the patient's likely benefit from the prescribed remote patient management remote monitoring and / or telemedicine intervention.

[0134] In one embodiment, the levels of two or three biomarkers or fragment(s) thereof selected from the group consisting of proADM, proBNP and proANP are determined and compared to one or more reference values ​​to determine whether the levels indicate whether or not to prescribe remote patient management for the patient.

[0135] In one embodiment, the levels of proADM and proBNP are determined and compared to one or more reference values ​​to determine whether the levels indicate whether or not to prescribe remote patient management for the patient.

[0136] In one embodiment, the levels of proADM and proANP are determined and compared to one or more reference values ​​to determine whether the levels indicate whether or not to prescribe remote patient management for the patient.

[0137] In one embodiment, the levels of proBNP and proANP are determined and compared to one or more reference values ​​to determine whether the levels indicate whether or not to prescribe remote patient management for the patient.

[0138] In one embodiment, the levels of proADM, proBNP and proANP are determined and compared to one or more reference values ​​to determine whether the levels indicate whether or not to prescribe remote patient management for the patient.

[0139] In some embodiments, levels of two of the biomarkers or fragment(s) thereof selected from the group consisting of proADM, proBNP and proANP are determined, and low benefit levels of both of the determined biomarkers indicate not to prescribe remote patient management for the patient.

[0140] In some embodiments, levels of two of the biomarkers or fragment(s) thereof selected from the group consisting of proADM, proBNP and proANP are determined, and high benefit levels of both of the determined biomarkers indicate that remote patient management should be prescribed for the patient.

[0141] In some embodiments, levels of two or three of the biomarkers or fragment(s) thereof selected from the group consisting of proADM, proBNP and proANP are determined, and a low benefit level of at least one of the determined biomarkers indicates that remote patient management should be prescribed for the patient.

[0142] Determination of two or three of the biomarkers or fragment(s) thereof selected from the group consisting of proADM, proBNP and proANP allows statistically specific and reliable therapy guidance, stratification and monitoring of remote patient management, which ensures that the maximum number of patients who may benefit from remote patient management are treated, while the maximum number of patients who will not benefit can be safely excluded in order to conserve medical resources.

[0143] Upon determining the levels of proADM and proBNP or fragment(s) thereof, particularly reliable therapy guidance of remote patient management can be achieved.

[0144] Embodiments relating to the determination of proADM and proBNP In one embodiment, the levels of proADM and proBNP are determined in a patient and compared to one or more reference values ​​to determine whether the levels indicate whether or not to prescribe remote patient management for the patient.

[0145] In one embodiment, levels of proADM or fragment(s) thereof below the reference value ±20%, where the reference value is selected from the range of values ​​from 0.63 nmol / L to 0.75 nmol / L, and levels of proBNP or fragment(s) thereof below the reference value ±20%, where the reference value is selected from the range of values ​​from 125 pg / ml to 413.7 pg / ml, indicate that remote patient management is not prescribed.

[0146] Any value within this range can be considered as a suitable threshold value for the level of proADM or fragment(s), for example 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74 or 0.75 nmol / L. Particularly preferred reference values ​​are 0.63 nmol / L ± 20%, preferably 0.69 nmol / L ± 20%, 0.72 nmol / L ± 20% or 0.75 nmol / L ± 20%.

[0147] For the level of proBNP or fragment(s), any value within this range can be considered as a suitable threshold, for example 125, 125.1, 135, 145, 145.4, 155, 165, 175, 185, 195, 205, 215, 225, 235, 245, 255, 265, 275, 285, 295, 305, 315, 325, 335, 345, 355, 365, 375, 380, 383.3, 385, 390, 395, 400, 405, 410 or 413.7 pg / ml. Particularly preferred reference values ​​are 413.7 pg / ml ± 20%, 383.3 pg / ml ± 20%, preferably 145.4 pg / ml ± 20% or 125.1 pg / mL ± 20%.

[0148] Thus, based on proADM and proBNP determinations, the cut-offs for selection at which patients should not be recommended for remote patient management can indicate levels of 125.1 and 413.7 pg / ml for NTproBNP and 0.63 and 0.75 nmol / L for MRproADM, depending on the desired safety (sensitivity 100%, 98%, 95%) and patient inclusion criteria (unplanned CV hospitalization or death from any cause; at least 30 days lost / year due to death from any cause). In general, the lower the desired sensitivity, the higher the cut-off for the important biomarkers and the higher the proportion of patients not recommended for remote patient management (see Table 15).

[0149] In one embodiment, a level of proADM or a fragment(s) thereof less than or equal to a reference value of 0.63 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof less than or equal to a reference value of 125.1 ± 20% indicate not to prescribe remote patient management.

[0150] In one embodiment, a level of proADM or a fragment(s) thereof less than or equal to a reference value of 0.69 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof less than or equal to a reference value of 125.1 ± 20% indicate not to prescribe remote patient management.

[0151] In one embodiment, a level of proADM or a fragment(s) thereof below a reference value of 0.72 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof below a reference value of 145.4 ± 20% indicate not to prescribe remote patient management.

[0152] In one embodiment, a level of proADM or a fragment(s) thereof below a reference value of 0.75 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof below a reference value of 383.3 ± 20% indicate not to prescribe remote patient management.

[0153] In one embodiment, a level of proADM or a fragment(s) thereof below a reference value of 0.75 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof below a reference value of 413.7 ± 20% indicate not to prescribe remote patient management.

[0154] With regard to adverse events or risks such as acute decompensation due to heart failure, death from any cause, or days lost due to unplanned hospitalization or death, the combination of proADM and proBNP levels mentioned above ensures a maximum sensitivity ranging from 100% to 95% (see Table 15).

[0155] Compared with single biomarkers, the combination of proADM and proBNP may further safely reduce the number of patients who would benefit from remote patient management.

[0156] The reduction in the population recommended for RPM with the combination of proBNP and proADM allows the exclusion of asymptomatic patients with fairly high proBNP levels but fairly low proADM levels, and vice versa, who would be prescribed based on the use of only one of the biomarkers for risk stratification.

[0157] For example, as detailed in the examples below, with a maximum sensitivity of 100%, therapy guidance based on proBNP levels alone can reduce the population using remote patient management by 3.4% (in terms of death from any cause). Using a combination of proBNP and proADM, the population with recommended RPM can be safely reduced by 13.9% (in terms of death from any cause). Thus, the combination of biomarkers in this case allows a reduction in medical resources by a factor of three without compromising patient safety.

[0158] Furthermore, the disclosed reference values ​​for therapy guidance using a combination of proBNP and proADM similarly allow for the safe inclusion of patients who should undergo remote patient management.

[0159] In one embodiment, a level of proADM or a fragment(s) thereof greater than or equal to a reference value ±20%, where the reference value is selected from the range of values ​​from 0.63 nmol / L to 0.75 nmol / L, and a level of proBNP or a fragment(s) thereof greater than or equal to a reference value ±20%, where the reference value is selected from the range of values ​​from 125 pg / ml to 413.7 pg / ml, indicate that remote patient management is prescribed.

[0160] Any value within this range can be considered as a suitable threshold value for the level of proADM or fragment(s), for example 0.63, 0.64, 0.65, 0.66, 0.67, 0.68, 0.69, 0.7, 0.71, 0.72, 0.73, 0.74 or 0.75 nmol / L. Particularly preferred reference values ​​are 0.63 nmol / L ± 20%, preferably 0.69 nmol / L ± 20%, 0.72 nmol / L ± 20% or 0.75 nmol / L ± 20%.

[0161] For the level of proBNP or fragment(s), any value within this range can be considered as a suitable threshold, for example 125, 125.1, 135, 145, 145.4, 155, 165, 175, 185, 195, 205, 215, 225, 235, 245, 255, 265, 275, 285, 295, 305, 315, 325, 335, 345, 355, 365, 375, 380, 383.3, 385, 390, 395, 400, 405, 410 or 413.7 pg / ml. Particularly preferred reference values ​​are 413.7 pg / ml ± 20%, 383.3 pg / ml ± 20%, preferably 145.4 pg / ml ± 20% or 125.1 pg / mL ± 20%.

[0162] In one embodiment, a level of proADM or a fragment(s) thereof equal to or greater than a reference value of 0.63 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof equal to or greater than a reference value of 125.1 ± 20% indicate that remote patient management is prescribed.

[0163] In one embodiment, a level of proADM or a fragment(s) thereof equal to or greater than the reference value of 0.69 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof equal to or greater than the reference value of 125.1 ± 20% indicate that remote patient management is prescribed.

[0164] In one embodiment, a level of proADM or a fragment(s) thereof equal to or greater than a reference value of 0.72 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof equal to or greater than a reference value of 145.4 ± 20% indicate the prescription of remote patient management.

[0165] In one embodiment, a level of proADM or a fragment(s) thereof equal to or greater than a reference value of 0.75 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof equal to or greater than a reference value of 383.3 ± 20% indicate that remote patient management is prescribed.

[0166] In one embodiment, a level of proADM or a fragment(s) thereof equal to or greater than a reference value of 0.75 nmol / L ± 20% and a level of proBNP or a fragment(s) thereof equal to or greater than a reference value of 413.7 ± 20% indicate prescribing remote patient management.

[0167] In a further embodiment, the method for therapy guidance, stratification and / or monitoring of remote patient management for a patient diagnosed with cardiovascular disease additionally comprises: determining the level of at least one further biomarker, preferably a cardiovascular biomarker such as troponin, C-reactive protein (CRP), proendothelin-1, provasopressin or fragments thereof, and / or a renal function marker such as creatinine, urea, uric acid, cystatin C, beta-trace protein (BTB) or inulin, - the level of at least one further biomarker, preferably a cardiovascular biomarker and / or a renal function marker, and the level of at least one biomarker or fragment(s) thereof selected from the group consisting of proADM, proBNP and proANP, is compared with one or more reference values, said levels being indicative for prescribing or not prescribing remote patient management for the patient.

[0168] In one embodiment, the method for therapy guidance, stratification and / or monitoring of remote patient management for a patient diagnosed with cardiovascular disease additionally comprises: - determining at least one clinical parameter, preferably age, weight, body mass index, sex, ethnic background, blood creatinine, left ventricular ejection fraction (LVEF), right ventricular ejection fraction (LVEF), NYHA classification, MAGGIC heart failure risk score, status of medical treatment, blood pressure (systolic / diastolic), heart rate, cardiac rhythm according to electrocardiogram (ECG), peripheral blood oxygen content (SpO2), self-assessed health status (scale) or a parameter indicating renal function, preferably creatinine clearance rate and / or glomerular filtration rate (GFR), - the level of the at least one clinical parameter and the at least one biomarker or fragment thereof is compared to one or more reference values, and the level of the at least one clinical parameter and the at least one biomarker or fragment(s) thereof indicates whether or not to prescribe remote patient management for the patient.

[0169] In one embodiment, at least one additional biomarker and / or at least one additional clinical parameter is determined before providing the sample, within 2 weeks, preferably within 1 week, 1 day before and 2 weeks after providing the sample, preferably within 1 week after providing the sample, 1 day after providing the sample.Most preferably, at least one additional biomarker and / or at least one additional clinical parameter is determined simultaneously with the biomarker selected from the group consisting of proADM, proBNP and proANP.For example, for the additional marker, the same sample can be used and / or different samples can be provided simultaneously.

[0170] Determining levels of additional biomarkers and / or additional clinical parameters can further increase the statistical reliability of therapy guidance, stratification and monitoring of remote patient management and improve the advantages described herein, particularly with regard to medical resource efficiency allocation of therapeutic measures, while ensuring positive patient outcomes.

[0171] In one embodiment, the method for therapy guidance, stratification and / or monitoring of remote patient management for a patient diagnosed with cardiovascular disease additionally comprises: determining at least one parameter indicative of renal function, preferably the creatinine clearance rate and / or the glomerular filtration rate (GFR), - at least one parameter indicative of renal function, preferably creatinine clearance rate and / or glomerular filtration rate, and the level of the at least one biomarker or fragment thereof are compared with one or more reference values ​​to determine whether the at least one parameter is indicative of renal function, preferably creatinine clearance rate and / or glomerular filtration rate, and whether the level of the at least one biomarker or fragment(s) thereof is indicative for prescribing or not prescribing remote patient management for the patient.

[0172] The inventors have recognised that in addition to determining the biomarkers described herein, the therapy guidance described herein can be optimised by determining markers or parameters indicative of renal function, such as creatinine clearance rate and / or glomerular filtration rate.

[0173] As the data below show, when renal function declines, an increase in the level of at least one biomarker selected from the group consisting of proADM, proBNP, and proANP may be partially attributable to renal impairment, independent of the biomarker's prognostic ability in guiding cardiovascular-specific remote patient management.

[0174] Thus, if determined parameters such as creatinine clearance rate and / or glomerular filtration rate indicate that the kidneys are not functioning properly, reference values ​​of biomarkers and associated low or high benefit levels for deciding whether to prescribe remote patient management are preferably adapted.

[0175] When the kidneys are not functioning properly, it is preferable to expand the low benefit level and raise the reference value to include larger determined levels of the biomarker. This indication may preferably explain the above findings that elevated levels of the biomarkers described herein may be partially assigned to renal dysfunction rather than to heart failure related processes. For example, as detailed in the data below, preferred low benefit levels of proADM or a fragment(s) thereof represent an extension of 95% sensitivity to biomarker levels of less than 1.06 nmol / l ± 20% in patients without confirmed renal impairment, compared with a low benefit level of less than 0.86 nmol / l ± 20% in patients without confirmed renal impairment.

[0176] By expanding the low benefit level, more patients could be safely excluded from receiving remote patient management, thereby further optimizing the allocation of healthcare resources to those patients who actually need it.

[0177] Embodiments relating to proADM and GFR determination In one embodiment, the at least one additionally determined clinical parameter is GFR, preferably GFR based on CKD-EPI, and when the GFR is below 50, the low benefit level of proADM or its fragment(s) is less than or equal to ±20% of the reference value, where the reference value is selected from the range of values ​​between 0.98 nmol / L and 1.06 nmol / L. Any value within this range can be considered as a suitable threshold value, for example 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04, 1.05 or 1.06 nmol / L. With regard to adverse occurrences or risks such as acute decompensation due to heart failure or death from any cause, the above reference value range of proADM of 0.98 nmol / l to 1.06 nmol / L in case of a GFR based on CKD-EPI below 50 ensures a maximum sensitivity in the range of 100% to 95%, where a preferred reference value of 0.98 nmol / L indicates a sensitivity of 100%, while a preferred reference value of 1.06 nmol / L provides a sensitivity of at least 95% (see Table 10).

[0178] In a preferred embodiment, the GFR, preferably a GFR based on CKD-EPI, is below 50 and the low benefit level of proADM or its fragment(s) is below 0.98 nmol / L ± 20%, preferably below 1.06 nmol / L ± 20%.

[0179] In one embodiment, the at least one additionally determined clinical parameter is GFR, preferably GFR based on CKD-EPI, and when the GFR is below 60, the low benefit level of proADM or its fragment(s) is less than or equal to ±20% of the reference value, where the reference value is selected from the range of values ​​between 0.97 nmol / L and 1.05 nmol / L. Any value within this range can be considered as a suitable threshold value, for example 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04 or 1.05 nmol / L. With regard to adverse occurrences or risks such as acute decompensation due to heart failure or death from any cause, the above reference value range of proADM of 0.97 nmol / l to 1.05 nmol / L in case of a GFR based on CKD-EPI below 60 ensures a maximum sensitivity in the range of 100% to 95%, where a preferred reference value of 0.97 nmol / L indicates a sensitivity of 100%, while a preferred reference value of 1.05 nmol / L provides a sensitivity of at least 95%.

[0180] In a preferred embodiment, the GFR, preferably a GFR based on CKD-EPI, is below 60 and the low benefit level of proADM or its fragment(s) is below 0.97 nmol / L ± 20%, preferably below 1.05 nmol / L ± 20% (see Table 10).

[0181] In one embodiment, the at least one additionally determined clinical parameter is GFR, preferably GFR based on Cockroft-Gault, and when the GFR is below 50, the low benefit level of proADM or fragment(s) is less than or equal to ±20% of the reference value, where the reference value is selected from the range of values ​​between 0.84 nmol / L and 1.05 nmol / L. Any value within this range can be considered as a suitable threshold value, for example 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91, 0.92, 0.93, 0.94, 0.95, 0.96, 0.97, 0.98, 0.99, 1, 1.01, 1.02, 1.03, 1.04 or 1.05 nmol / L. With regard to adverse occurrences or risks such as acute decompensation due to heart failure or death from any cause, the above reference value range of proADM of 0.62 nmol / l to 1.05 nmol / L for a GFR according to Cockroft-Gault below 50 ensures a maximum sensitivity in the range of 100% to 95%, whereby a preferred reference value of 0.84 nmol / L indicates a sensitivity of 99%, while a preferred reference value of 1.05 nmol / L provides a sensitivity of at least 95%.

[0182] In a preferred embodiment, the GFR, preferably a GFR based on CKD-EPI, is below 50 and the low benefit level of proADM or its fragment(s) is below 0.84 nmol / L ± 20%, preferably below 1.05 nmol / L ± 20%.

[0183] In one embodiment, the at least one additionally determined clinical parameter is GFR, preferably GFR based on Cockroft-Gault, and when the GFR is below 60, the low benefit level of proADM or fragment(s) is less than or equal to ±20% of the reference value, where the reference value is selected from the range of values ​​between 0.75 nmol / L and 0.92 nmol / L. Any value within this range can be considered as a suitable threshold value, for example 0.75, 0.76, 0.77, 0.78, 0.79, 0.8, 0.81, 0.82, 0.83, 0.84, 0.85, 0.86, 0.87, 0.88, 0.89, 0.9, 0.91 or 0.92 nmol / L.

[0184] In a preferred embodiment, the GFR, preferably GFR according to Cockroft-Gault, is below 60 and the low benefit level of proADM or its fragment(s) is 0.75 nmol / L ± 20% or less, preferably 0.92 nmol / L ± 20% or less. With regard to adverse occurrences or risks such as acute decompensation due to heart failure or death from any cause, the above reference value range of proADM from 0.62 nmol / l to 0.92 nmol / L in case of a GFR according to Cockroft-Gault below 60 ensures a maximum sensitivity in the range of 100% to 95%, where the preferred reference value of 0.75 nmol / L shows a sensitivity of 99%, while the preferred reference value of 0.92 nmol / L provides a sensitivity of at least 95% (see Table 11).

[0185] In a preferred embodiment, the GFR, preferably GFR based on Cockroft-Gault, is below 30 and the low benefit level of proADM or fragment(s) thereof is 1.14 nmol / L ± 20% or less.

[0186] In a preferred embodiment, parameters such as creatinine clearance rate and / or glomerular filtration rate indicate renal impairment and the low benefit level of proADM or its fragment(s) is 0.84 nmol / L ± 20% or less, preferably 0.98 nmol / L ± 20% or less, 1.05 nmol / L ± 20% or less, 1.06 nmol / L ± 20% or less, or 1.14 nmol / L ± 20% or less.

[0187] It should be noted that the disclosed indications for the preferred low benefit levels of proADM or fragment(s) thereof similarly apply to the preferred high benefit levels of proADM or fragment(s) thereof.

[0188] For example, in preferred embodiments, parameters such as creatinine clearance rate and / or glomerular filtration rate indicate renal impairment and a high benefit level of proADM or a fragment(s) thereof is 0.84 nmol / L ± 20% or more, preferably 0.98 nmol / L ± 20% or more, 1.05 nmol / L ± 20% or more, 1.06 nmol / L ± 20% or more, or 1.14 nmol / L ± 20% or more.

[0189] Embodiments relating to determination of proBNP and GFR In one embodiment, the at least one additionally determined clinical parameter is GFR, preferably GFR based on CKD-EPI, and when the GFR is below 50, the low benefit level of proBNP or fragment(s) is less than or equal to ±20% of the reference value, where the reference value is selected from the range of values ​​from 237.6 pg / ml to 273.7 pg / ml. Any value within this range can be considered as a suitable threshold value, for example 240, 250, 260 or 270 pg / ml.

[0190] In a preferred embodiment, the GFR, preferably a GFR based on CKD-EPI, is below 50 and the low benefit level of proBNP or fragment(s) thereof is below 237.6 pg / ml ± 20%, preferably below 273.7 pg / ml ± 20%.

[0191] In one embodiment, the at least one clinical parameter additionally determined is GFR, preferably GFR based on CKD-EPI, and when GFR is below 60, the low benefit level of proBNP or fragment(s) is less than or equal to ±20% of the reference value, where the reference value is selected from the range of values ​​from 237.6 pg / ml to 402.6 pg / ml. Any value within this range can be considered as a suitable threshold value, for example 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390 or 400 pg / ml. With regard to adverse occurrences or risks such as acute decompensation due to heart failure or death from any cause, the above reference value range for proBNP of 237.6 pg / ml to 402.6 pg / ml in case of a GFR based on CKD-EPI below 60 ensures a maximum sensitivity in the range of 100% to 95%, where the preferred reference value of 237.6 pg / ml shows a sensitivity of 100%, while the preferred reference value of 402.6 pg / ml provides a sensitivity of at least 95%.

[0192] In a preferred embodiment, the GFR, preferably a GFR based on CKD-EPI, is below 60 and the low benefit level of proBNP or fragment(s) thereof is below 237.6 pg / ml ± 20%, preferably below 402.6 pg / ml ± 20%.

[0193] In one embodiment, the at least one clinical parameter additionally determined is GFR, preferably GFR based on Cockroft-Gault, and when the GFR is below 50, the low benefit level of proBNP or fragment(s) is less than or equal to ±20% of the reference value, where the reference value is selected from the range of values ​​from 237.6 pg / ml to 454.4 pg / ml. Any value within this range can be considered as a suitable threshold value, for example 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400, 410, 420, 430, 440, 450 pg / ml. With regard to adverse occurrences or risks such as acute decompensation due to heart failure or death from any cause, the above reference value range for proBNP of 237.6 pg / ml to 454.4 pg / ml in the case of a GFR according to Cockroft-Gault below 50 ensures a maximum sensitivity in the range of 100% to 95%, where the preferred reference value of 237.6 pg / ml indicates a sensitivity of 100%, while the preferred reference value of 454.4.6 pg / ml provides a sensitivity of at least 95%.

[0194] In a preferred embodiment, the GFR, preferably the Cockroft-Gault based GFR, is below 50 and the low benefit level of proBNP or fragment(s) thereof is below 237.6 pg / ml ± 20%, preferably below 454.4 pg / ml ± 20%.

[0195] In one embodiment, the at least one additionally determined clinical parameter is GFR, preferably GFR based on Cockroft-Gault, and when the GFR is below 60, the low benefit level of proBNP or fragment(s) is less than ±20% of the reference value, where the reference value is selected from the range of values ​​from 137.7 pg / ml to 402.6 pg / ml. Any value within this range can be considered as a suitable threshold value, for example 140, 150, 160, 170, 180, 190, 200, 210, 220, 230, 240, 250, 260, 270, 280, 290, 300, 310, 320, 330, 340, 350, 360, 370, 380, 390, 400 pg / ml. With regard to adverse occurrences or risks such as acute decompensation due to heart failure or death from any cause, the above reference value range for proBNP of 137.7 pg / ml to 402.6 pg / ml in case of a GFR according to Cockroft-Gault below 60 ensures a maximum sensitivity in the range of 100% to 95%, whereby a preferred reference value of 137.7 pg / ml indicates a sensitivity of 100%, while a preferred reference value of 402.6 pg / ml provides a sensitivity of at least 95%.

[0196] In a preferred embodiment, the GFR, preferably the Cockroft-Gault based GFR, is below 60 and the low benefit level of proBNP or fragment(s) thereof is below 137.7 pg / ml ± 20%, preferably below 203.9 pg / ml ± 20%, more preferably below 402.6 pg / ml ± 20%.

[0197] In a preferred embodiment, the GFR, preferably the Cockroft-Gault based GFR, is below 30 and the low benefit level of proBNP or fragment(s) thereof is below 237.6 pg / ml ± 20%.

[0198] In preferred embodiments, parameters such as creatinine clearance rate and / or glomerular filtration rate are indicative of renal impairment and the low benefit level of proBNP or a fragment(s) thereof is 203.9 pg / ml ± 20% or less, preferably 237.6 pg / ml ± 20% or less, 273.7 pg / ml ± 20% or less, 402.6 pg / ml or less, or 454.4 pg / ml ± 20% or less.

[0199] As detailed in the data below, combining only any one of the biomarkers with the measurement of GFR can enhance the predictive power of this method. However, combining two or more of the biomarkers with a parameter indicative of renal function, such as GFR, using, for example, proBNP and proADM, achieves a certain high enhancement of the predictive power of this method. As shown in Tables 10 and 11 (columns 6 and 9) and Table 13, by combining proADM and proBNP with GFR, more than two or three times as many patients can be safely excluded from receiving remote patient management compared to using only proBNP in combination with GFR. Therefore, this embodiment allows a certain efficient allocation of medical resources to those patients who will benefit from remote patient management.

[0200] It should be noted that the disclosed indications for the preferred low benefit levels of proBNP or fragment(s) thereof similarly apply to the preferred high benefit levels of proBNP or fragment(s) thereof.

[0201] For example, in preferred embodiments, parameters such as creatinine clearance rate and / or glomerular filtration rate are indicative of renal impairment and high benefit levels of proBNP or a fragment(s) thereof are 203.9 pg / ml ± 20% or more, preferably 237.6 pg / ml ± 20% or more, 273.7 pg / ml ± 20% or more, 402.6 pg / ml or more, or 454.4 pg / ml ± 20% or more.

[0202] In a further aspect, the present invention provides a kit for carrying out the method of the present invention, comprising: - a detection reagent for determining at least one biomarker or a fragment thereof selected from the group consisting of proADM, proBNP and pro proANP in a sample from a patient, and - reference data, such as reference values ​​for determining whether the level of at least one biomarker selected from the group consisting of proADM, proBNP and pro proANP indicates the prescription or non-prescription of remote patient management, in particular for a low benefit level of at least one biomarker and for a high benefit level of at least one biomarker, said reference data being preferably stored on a computer readable medium and / or used in the form of a computer executable code configured to compare the determined at least one biomarker with the reference value; - optionally a detection reagent for determining the level of at least one additional biomarker or fragment(s) thereof in a sample from the patient and / or at least one clinical parameter, preferably age, weight, body mass index, sex, ethnic background, blood creatinine, left ventricular ejection fraction (LVEF), right ventricular ejection fraction (LVEF), NYHA classification, MAGGIC heart failure risk score, status of medical treatment, blood pressure (systolic / diastolic), heart rate, cardiac rhythm by electrocardiogram (ECG), peripheral blood oxygen content (SpO2), self-assessed health status (scale), or glomerular filtration rate (GFR) and reference data, such as reference values, for determining whether the level of at least one additional biomarker or fragment(s) thereof and / or at least one clinical parameter is indicative for prescribing or not prescribing remote patient management, said reference data being preferably stored on a computer readable medium and / or used in the form of a computer executable code configured for comparing the determined level of said at least one biomarker or fragment(s) thereof and / or said at least one clinical parameter with the reference value. (drawing) The invention is further described by reference to the following drawings, which illustrate non-limiting and potentially preferred embodiments presented to further illustrate the invention. [Brief description of the drawings]

[0203] [Figure 1] 1 is a trial flow chart of the study. [Diagram 2] This is a trial profile. [Diagram 3] Kaplan-Meier cumulative event curves for all-cause mortality. [Figure 4-1] Forest plot of subgroup analysis of the proportion of days lost due to unplanned cardiovascular hospitalization and death from any cause. [Figure 4-2] Forest plot of subgroup analysis of the proportion of days lost due to unplanned cardiovascular hospitalization and death from any cause. [Diagram 5] ROC curve of proADM for death or acute decompensation within 90 days after first measurement (usual care patients). [Figure 6] ROC curve of proBNP for death or acute decompensation within 90 days after first measurement (usual care patients). [Figure 7] ROC curve of proANP for death or acute decompensation within 90 days after first measurement (usual care patients). [Figure 8] Kaplan-Meier analysis of the RPM and usual care groups showing splitting of the curves at 90 days and a significant advantage of the RPM group (log-rank test: 0.017). [Figure 9]Illustration of a biomarker-based selection algorithm for patients recommended for RPM based on whether they lost at least 1 month (i.e., at least 30 days out of 365 study days) in a study year due to death from any cause or unplanned CV hospitalization (red, star symbol) or not (blue, rectangular symbol). Patients in the shaded green area are recommended for RPM based on biomarker guidance. (A) Using NTproBNP can reduce the number of patients recommended for RPM for a given desired safety (95% sensitivity). (B) Recommendation for RPM based on both NTproBNP and MRproADM allows for a more accurate and efficient selection of patients in this setting for the same desired safety. [Figure 10] Comparison of (A) the incidence of emergency situations and (B) the medical effort expended by telemedicine centers between the group that would have been recommended for RPM based on the presented biomarker guidance scenario (i.e., only patients with NTproBNP ≥ 413.7 and MRproADM ≥ 0.75) and the group that was not recommended for RPM (those below the cutoff). [Figure 11] Kaplan-Meier curves and log-rank tests to compare the effect of RPM on time to death from any cause in the TIM-HF2 subpopulation reduced by the biomarker guidance scenario (i.e., only patients with NTproBNP ≥ 413.7 and MRproADM ≥ 0.75 nmol / L are considered). [Figure 12] Raw data for the relationship between the primary TIM-HF2 study endpoint of days lost and the biomarkers (A) NTproBNP and (B) MRproADM. For the specification of the biomarker cutoffs for selecting patients for the RPM recommendation, an event was defined as at least 1 month lost during the 1-year follow-up period, i.e. at least 30 days / year lost, corresponding to a proportion of at least 8.2% of days lost (dashed line). [Figure 13-1]ROC analysis of all TIM-HF2 patients characterizing the prognostic potential of the biomarkers MRproADM, LVEF and MAGGIC score in addition to and compared with NTproBNP in relation to the primary endpoint % days lost (classified as 1 month lost during 1 year follow-up, upper panel, rows) and the secondary endpoint death from any cause (lower panel, columns). To facilitate comparability, the same set of patients with complete data for all required variables was used for all ROC analyses shown in panels A-F (N=1522). [Figure 13-2] ROC analysis of all TIM-HF2 patients characterizing the prognostic potential of the biomarkers MRproADM, LVEF and MAGGIC score in addition to and compared with NTproBNP in relation to the primary endpoint % days lost (classified as 1 month lost during 1 year follow-up, upper panel, rows) and the secondary endpoint death from any cause (lower panel, columns). To facilitate comparability, the same set of patients with complete data for all required variables was used for all ROC analyses shown in panels A-F (N=1522). [Figure 13-3] ROC analysis of all TIM-HF2 patients characterizing the prognostic potential of the biomarkers MRproADM, LVEF and MAGGIC score in addition to and compared with NTproBNP in relation to the primary endpoint % days lost (classified as 1 month lost during 1 year follow-up, upper panel, rows) and the secondary endpoint death from any cause (lower panel, columns). To facilitate comparability, the same set of patients with complete data for all required variables was used for all ROC analyses shown in panels A-F (N=1522). [Figure 14-1]ROC analysis of SOC patients characterizing the prognostic potential of the biomarkers MRproADM, LVEF and MAGGIC score in addition to and in comparison with NTproBNP in relation to the primary endpoint % days lost (classified as 1 month lost during 1 year follow-up, upper panel, rows) and the secondary endpoint death from any cause (lower panel, columns). To facilitate comparability, the same population of SOC patients with complete data for all required variables was used for all ROC analyses shown in panels A-F (N=767). [Figure 14-2] ROC analysis of SOC patients characterizing the prognostic potential of the biomarkers MRproADM, LVEF and MAGGIC score in addition to and in comparison with NTproBNP in relation to the primary endpoint % days lost (classified as 1 month lost during 1 year follow-up, upper panel, rows) and the secondary endpoint death from any cause (lower panel, columns). To facilitate comparability, the same population of SOC patients with complete data for all required variables was used for all ROC analyses shown in panels A-F (N=767). [Figure 14-3] ROC analysis of SOC patients characterizing the prognostic potential of the biomarkers MRproADM, LVEF and MAGGIC score in addition to and in comparison with NTproBNP in relation to the primary endpoint % days lost (classified as 1 month lost during 1 year follow-up, upper panel, rows) and the secondary endpoint death from any cause (lower panel, columns). To facilitate comparability, the same population of SOC patients with complete data for all required variables was used for all ROC analyses shown in panels A-F (N=767). [Figure 15-1] Box plot visualization of quarterly biomarker distributions of (A) NTproBNP and (B) MRproADM in the two study arms RPM and SOC in the TIM-HF2 study. Panel numbers "00" to "12" indicate the month of study visit. Vertical axis is in logarithmic scale. [Figure 15-2]Box plot visualization of quarterly biomarker distributions of (A) NTproBNP and (B) MRproADM in the two study arms RPM and SOC in the TIM-HF2 study. Panel numbers "00" to "12" indicate the month of study visit. Vertical axis is in logarithmic scale. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0204] In one embodiment, the present invention relates to a method for therapy guidance, stratification and / or monitoring of remote patient management for patients diagnosed with cardiovascular disease. As is evident from the data presented herein, the potential benefit of prescribing remote patient management to patients suffering from cardiovascular disease can be predicted based on the levels of proADM, proBNP and / or proANP or fragment(s) thereof, thus providing valuable information for therapy guidance, stratification and / or monitoring.

[0205] The present invention has the following advantages over conventional methods: the methods and kits of the present invention are rapid, objective, easy to use, reliable and accurate for therapy guidance, stratification and / or monitoring of remote patient management. Employing the methods and kits described herein allows for efficient allocation of medical resources in prescribing remote patient management to patients who are likely to benefit. Substantial medical effort and costs can be saved by not prescribing remote patient management to individuals based on early identified biomarkers who are likely not to benefit from remote patient management.

[0206] The methods and kits of the present invention relate to markers that are easily measurable routinely in a hospital or similar clinical setting, since the levels of proADM, proBNP and proANP can be determined in routinely obtained blood samples or in further body fluids or biological samples obtained from a subject. Thus, the methods and kits described herein can be used in a routine setting to guide remote patient management of patients diagnosed with cardiovascular disease.

[0207] As used herein, a "patient" or "subject" may be a vertebrate. In the context of the present invention, the term "subject" or "patient" includes both humans and animals, particularly mammals, and other organisms.

[0208] As used herein, "cardiovascular disease" is characterized by impaired function of the myocardium, or the vascular system that supplies the heart, brain, and other vital organs. The term "cardiovascular disease" encompasses a wide range of disorders, including arteriosclerosis, coronary artery disease, valvular heart disease, arrhythmias, heart failure, hypertension, orthostatic hypotension, shock, endocarditis, diseases of the aorta and its branches, disorders of the peripheral vascular system, congenital heart disease, or stroke.

[0209] In the context of the present invention, the patient is preferably diagnosed with cardiovascular disease before determining the level of biomarker.For this purpose, any means known in the art for diagnosing cardiovascular disease can be used.The adoption of the present invention does not depend on the type of pre-diagnosis of cardiovascular disease.

[0210] In some embodiments, diagnosing a cardiovascular disease event may include detecting an abnormal electrocardiogram (ECG) or electrogram.

[0211] The term "electrocardiogram" is defined as the cardiac electrical signal from one or more skin surface electrode(s) placed in locations indicative of the electrical activity (depolarization and repolarization) of the heart. An electrocardiogram segment refers to the recording of electrocardiogram data for either a particular period of time, such as 10 seconds, or a particular number of heart rates, such as 10 beats. The PQ segment of a patient's electrocardiogram is the flat segment of the electrocardiogram beat that typically occurs immediately before the R wave.

[0212] The term "electrogram" is defined as the cardiac electrical signal from one or more implanted electrode(s) placed in a position indicative of the cardiac electrical activity (depolarization and repolarization). An electrogram segment refers to a recording of electrogram data for either a particular time period, such as 10 seconds, or a particular heart rate, such as 10 beats. The PQ segment of a patient's electrogram is the flat segment of the electrogram that typically occurs just before the R wave. A beat is a subsegment of an electrocardiogram or electrogram segment that preferably contains exactly one R wave.

[0213] Cardiac signal parameters relate to measurements or calculations made during processing of one or more beats of electrogram data and can be used to determine whether a cardiovascular event has occurred. Cardiac signal parameters include PQ segment mean, ST segment mean, R wave peak value, ST deviation, ST shift, mean signal strength, T wave peak height, T wave mean, T wave deviation, heart rate, and RR interval.

[0214] The term "heart failure" as used herein, as known to those skilled in the art, relates to impaired systolic and / or diastolic function of the heart with obvious signs of heart failure. Preferably, the heart failure referred to herein is also "chronic heart failure", which thus refers to a condition that does not appear suddenly, but appears over a long period of time.

[0215] Heart failure according to the present invention includes overt and / or advanced heart failure. In overt heart failure, the subject exhibits symptoms of heart failure as known to those skilled in the art.

[0216] The term heart failure encompasses congestive heart failure and / or chronic heart failure as defined by the American College of Cardiology and the American Heart Association as described in the report of the American College of Cardiology / American Heart Association Task Force on Practice Guidelines (Yancy et al. 2013: ACCF / AHA Guidelines for the Management of Heart Failure).

[0217] In some embodiments, the term heart failure as used herein refers to stages C and D of the ACC / AHA classification, in which the subject exhibits typical symptoms of heart failure, i.e., the subject is clearly not healthy. A subject suffering from heart failure and classified as stage C or D has undergone permanent and irreversible structural and / or functional changes in his myocardium, and as a result of these changes, a complete recovery to health is not possible.

[0218] Subjects who reach stage C or even D of the ACC / AHA classification usually cannot regress to stage B or even A.

[0219] Heart failure can also be classified into a functional classification system according to the New York Heart Association (NYHA). Patients in NYHA class I have no obvious symptoms of cardiovascular disease, but already have objective evidence of functional impairment. Physical activity is not limited, and ordinary physical activity does not cause excessive fatigue, palpitations, or dyspnea (shortness of breath). Patients in NYHA class II have slight limitations in physical activity. They are comfortable at rest, but ordinary physical activity results in fatigue, palpitations, or dyspnea. Patients in NYHA class III show significant limitations in physical activity. They are comfortable at rest, but even less than ordinary activity causes fatigue, palpitations, or dyspnea. Patients in NYHA class IV are unable to perform any physical activity without discomfort. They show symptoms of heart failure at rest. For details of the NYHA classification system, see "The Criteria Committee of the New York Heart Association. (1994). Nomenclature and Criteria for Diagnosis of Diseases of the Heart and Great Vessels (9th ed.). Boston: Little, Brown & Co. pp. 253-256".

[0220] Heart failure as a disorder of the systolic and / or diastolic function of the heart can also be determined, for example, by echocardiography, angiography, scintigraphy, or magnetic resonance imaging. This disorder may be accompanied by symptoms of heart failure as outlined above (NYHA classification II to IV), but some patients may present without significant symptoms (NYHA I). Furthermore, heart failure is also evident by a decrease in left ventricular ejection fraction (LVEF). More preferably, heart failure as used herein is accompanied by a left ventricular ejection fraction (LVEF) of less than 60%, preferably less than 45%.

[0221] The permanent structural or functional damage to the myocardium typical of heart failure is known to those skilled in the art and includes various molecular cardiac remodeling processes such as interstitial fibrosis, inflammation, infiltration, scar formation, apoptosis or necrosis.

[0222] Stiffening of the ventricular wall due to interstitial fibrosis causes insufficient ventricular inflow in diastolic dysfunction. Permanent structural or functional damage to the myocardium is caused by dysfunction or destruction of myocardial cells. Myocytes and their components can be damaged by inflammation or infiltration. Toxins and drugs (ethanol, cocaine, amphetamines, etc.) cause intracellular damage and oxidative stress. A common mechanism of injury is ischemia, which causes infarction and scar formation. After myocardial infarction, dead myocytes are replaced by scar tissue, adversely affecting myocardial function. On an echocardiogram, this is manifested by abnormal or absent wall motion.

[0223] The manifestations of heart failure are dyspnea, fatigue, and fluid retention, which may lead to pulmonary congestion and peripheral edema, and typical signs on physical examination are edema and rales. There is no single diagnostic test, as heart failure is a clinical diagnosis based primarily on a careful history and physical examination. The clinical syndrome of heart failure can result from disorders of the pericardium, myocardium, endocardium, or great vessels, but the majority of patients with heart failure suffer from impaired left ventricular (LV) myocardial function.

[0224] Heart failure can be associated with a wide range of LV dysfunction, ranging, for example, from maintaining normal LV size and ejection fraction (EF) to severe dilation and / or severely reduced EF. In most patients, abnormalities in systolic and diastolic dysfunction coexist. Patients with normal EF have a different natural history and may require different therapeutic strategies than patients with reduced EF.

[0225] The various changes in systolic and diastolic function seen in LVH can progress to overt congestive heart failure (CHF).

[0226] Systolic and diastolic heart failure can be diagnosed by methods known to those skilled in the art, preferably echocardiography, in particular tissue Doppler echocardiography (TD).

[0227] Generally, systolic heart failure is manifested by a decrease in left ventricular ejection fraction (LVEF). In one embodiment of the present invention, heart failure as used herein is accompanied by a left ventricular ejection fraction (LVEF) of less than 60%, preferably less than 45%.

[0228] Diastolic heart failure (DHF) is believed to account for more than 50% of all heart failure patients and is also referred to as heart failure with normal LVEF ejection fraction (HFNEF). The diagnosis of HFNEF requires the following: (i) signs or symptoms of heart failure; (ii) normal or mildly abnormal systolic LV function; and (iii) evidence of diastolic LV dysfunction. Normal or mildly abnormal systolic LV function is defined as LVEF >50% and LV end-diastolic volume index (LVEDVI) <97 mL / m 2Diastolic LV dysfunction is preferably diagnosed by tissue Doppler (TD), where a ratio EIE'>15 is considered diagnostic evidence of diastolic LV dysfunction (E is the initial mitral flow velocity and E' is the initial TD advance velocity), and if TD results in an E / E' ratio suggestive of diastolic LV dysfunction (15>EIE'>8), diagnostic evidence of diastolic LV dysfunction requires additional non-invasive investigations (e.g. lateral mitral annular Doppler, mitral valve or pulmonary vein Doppler, echo measurement of LV myocardial mass index or left atrial volume index, electrocardiographic evidence of atrial fibrillation).

[0229] For more information on diastolic LV dysfunction, see the Consensus statement on the diagnosis of heart failure with normal left ventricular ejection fraction by the Heart Failure and Echocardiography Associations of the European Society of Cardiology, European Heart Journal 2007, 28, 2359-2550.

[0230] "Acute heart failure (AHF)", also called "acute decompensated heart failure", is defined as the rapid onset of symptoms and signs secondary to abnormal cardiac function. It can occur with or without previous cardiac disease. Cardiac dysfunction can be related to systolic or diastolic dysfunction, abnormalities in cardiac rhythm, or mismatch between preload and afterload. AHF can present as acute de novo (new onset of AHF in patients without previously known cardiac dysfunction) or acute decompensation of chronic heart failure (Nieminen et al. 2005. Eur Heart J 26:384-416; Dickstein et al. 2008. Eur Hear J 29:2388-442).

[0231] Cardiac dysfunction may be related to systolic or diastolic dysfunction, abnormalities in cardiac rhythm, or mismatch between preload and afterload. It is often life-threatening and requires urgent treatment. According to established classification, AHF includes several different clinical conditions that patients may present with: (I) acute decompensated congestive heart failure, (II) AHF with hypertension / hypertensive crisis, (III) AHF with pulmonary edema, (IVa) cardiogenic shock / low output syndrome, (IVb) severe cardiogenic shock, (V) high output heart failure, and (VI) acute right-sided heart failure. For a detailed clinical description, classification, and diagnosis of AHF, and an overview of additional AHF classification systems including the Killip, Forrester, and "clinical severity" classifications, see, inter alia, Nieminen et al. 2005 ("Executive summary of the guidelines on the diagnosis and treatment of acute heart failure: the Task Force on Acute Heart Failure of the European Society of Cardiology". Eur Heart J 26:384-416) and references therein.

[0232] Preferably, the cardiac dysfunction is a systolic dysfunction, more preferably characterized by a decrease in left ventricular ejection fraction (LVEF), preferably wherein the LVEF is less than 55% or less than 50% or less than 45%, and / or by an increase in cardiac filling pressures.

[0233] The term "systolic dysfunction" as used herein has its art-established meaning. With further guidance, the term "systolic dysfunction" may be used interchangeably with synonyms known to those of skill in the art, such as "systolic ventricular dysfunction or failure" or "systolic cardiac dysfunction or failure." Essentially, "systolic dysfunction" refers to an impairment of the pumping function of the heart due to a decrease in ventricular contractility.

[0234] The term "diastolic dysfunction" as used herein has its art-established meaning. With further guidance, the term "diastolic dysfunction" may be used interchangeably with synonyms known to those of skill in the art, such as "diastolic ventricular dysfunction or failure" or "diastolic cardiac dysfunction or failure." Essentially, "diastolic dysfunction" refers to an impairment of the heart's pumping function by ventricular filling of the blood vessels.

[0235] As used herein, the term "(left) ventricular ejection fraction" refers to the stroke volume of the (left) ventricle during systole, which represents the percentage of blood pumped out of the (left) ventricle with each heartbeat. By definition, the amount of blood in the ventricle just before contraction is known as the end-diastolic volume. Similarly, the amount of blood left in the ventricle at the end of contraction is the end-systolic volume. The difference between the end-diastolic volume and the end-systolic volume is the stroke volume, i.e., the amount of blood ejected with each beat. The ejection fraction (EF) is the percentage of the end-diastolic volume ejected with each beat, i.e., the stroke volume (SV) divided by the end-diastolic volume (EDV): EF=SV / EDV=(EDV-ESV) / EDV.

[0236] "Heart failure with reduced ejection fraction (HFrEF)" preferably refers to heart failure with an ejection fraction of less than 50%, preferably less than 45%, more preferably less than 40%.

[0237] "Heart failure with preserved ejection fraction (HFpEF)" preferably refers to heart failure with an ejection fraction of 50% or more.

[0238] Ejection fraction (EF) preferably refers to left ventricular ejection fraction (LVEF).

[0239] The method of the invention may also be used for monitoring, therapy monitoring, therapy guidance and / or therapy control. "Monitoring" relates to following a patient and potentially arising complications, for example to analyze the progress of a healing process or the impact of a particular treatment or therapy on the patient's health status.

[0240] The term "therapy monitoring" or "therapy control" in the context of the present invention refers to monitoring and / or adjustment of the therapeutic treatment of the patient, for example, by obtaining feedback on the effectiveness of the therapy. As used herein, the term "therapy guidance" refers to the application of a particular therapy, therapeutic action, or medical intervention based on the value / level of one or more biomarkers and / or clinical parameters and / or clinical scores. This includes the decision to prescribe or not prescribe a therapy, adjust a therapy, or discontinue a therapy. In the context of the present invention, "therapy monitoring" in a heart failure patient can be achieved by using remote patient management or by usual care without remote patient management, depending on the predetermined risk of the patient having an adverse outcome by using biomarkers.

[0241] In some embodiments, the methods described herein may also be relevant to the diagnosis, prognosis, risk assessment, and / or risk stratification of adverse events in the health of patients diagnosed with cardiovascular disease.

[0242] In the context of the present invention, an "adverse event" refers to an event that indicates a complication or deterioration of the health status of a patient diagnosed with a cardiovascular disease. Such adverse events include, but are not limited to, cardiac events, cardiovascular events, acute heart failure, acute decompensation, or deterioration of the patient's general clinical signs or symptoms, such as hypotension or hypertension, tachycardia or bradycardia, or death of the patient.

[0243] Adverse events may further include unplanned hospitalization due to cardiovascular events, i.e. unplanned hospitalization due to, in particular, decompensated heart failure, coronary artery disease, stroke or transient ischemic attack (TIA), arrhythmia, pulmonary embolism, endocarditis or other cardiovascular events.

[0244] "Cardiovascular event" is used interchangeably herein with the term "cardiac event" and can refer to sudden cardiac death, acute coronary syndromes such as, but not limited to, plaque rupture, myocardial infarction, unstable angina, as well as non-cardiac acute arterial vascular events such as leg thrombus, aneurysm, stroke, and other cardiovascular ischemic events in which blood flow and oxygenation in arterial vessels is interrupted. Cardiovascular events may also refer to elevated heart rate, bradycardia, tachycardia, or arrhythmias such as atrial fibrillation, atrial flutter, ventricular fibrillation, and premature ventricular contractions or atrial extrasystoles (PVCs or PACs). Preferably, the cardiovascular event is acute heart failure (AHF) or acute decompensated heart failure (ADHF).

[0245] As used herein, "non-fatal cardiac arrest" preferably refers to the absence of cardiac rhythm or the presence of a disorganized rhythm that requires any component of basic or advanced cardiac life support. "Acute myocardial infarction" preferably refers to an increase and gradual decrease, or a faster increase and decrease, in the case of creatine kinase isoenzyme as a marker of myocardial necrosis, accompanied by at least one of the following: ischemic symptoms, abnormal Q waves on ECG, ST segment elevation or depression, coronary intervention (coronary angioplasty), or an atypical decrease in the peak detected increase in troponin level after the patient's surgery. "Congestive heart failure" preferably refers to new in-hospital signs or symptoms of dyspnea or fatigue, orthopnea, paroxysmal nocturnal dyspnea, elevated jugular venous pressure, pulmonary rales on physical examination, cardiac hypertrophy or pulmonary vascular congestion. "New cardiac arrhythmia" preferably refers to atrial flutter, atrial fibrillation, or second or third degree atrioventricular conduction block, which may be evidenced by electrocardiogram. "Angina" preferably refers to a dull, diffuse substernal chest discomfort brought on by physical exertion or emotion and relieved by rest or nitroglycerin.

[0246] As used herein, "diagnosis" in the context of the present invention relates to the recognition and (early) detection of a clinical condition. The assessment of severity may also be encompassed by the term "diagnosis".

[0247] "Prognosis" refers to the prediction of a subject's outcome or risk. It can also include the estimation of the chances of recovery or chance of an adverse outcome for said subject.

[0248] In terms of diagnosis or prognosis of adverse events, this method is particularly suitable for risk assessment or stratification.This means that the method of the present invention can distinguish high-risk patients who are more likely to suffer (more) complications or whose condition will become more severe in the future from low-risk patients whose health condition is stable or even improving, and therefore they are not expected to suffer from or suffer from certain adverse events that may require certain therapeutic measures.Preferably, risk can be related to death or hospitalization due to acute decompensated heart failure.

[0249] In the present invention, the terms "risk assessment" and "risk stratification" refer to classifying subjects into different risk groups according to further prognosis. Risk assessment also refers to stratification for applying preventive and / or therapeutic measures. The term "therapy stratification" particularly refers to grouping or classifying patients into different groups, such as risk groups or therapy groups that receive certain different therapeutic measures according to their classification. The term "therapy stratification" also refers to grouping or classifying patients into groups that need or do not need to receive certain therapeutic measures.

[0250] The methods described herein relate to therapy guidance, stratification, and / or monitoring of remote patient management of patients diagnosed with cardiovascular disease based on the determination of the level of at least one biomarker selected from the group consisting of proADM, proBNP and proANP.

[0251] In particular, the method includes comparing the level of the biomarker to one or more reference values ​​(such as a threshold or cutoff value and / or a population mean value) to determine whether the level of the biomarker indicates whether or not to prescribe remote patient management to the patient.

[0252] As used herein, the term "remote patient management" preferably refers to a therapeutic approach for remotely managing a patient with cardiovascular disease, in which data regarding the patient's health status is repeatedly collected at the patient's (i.e., outpatient) location and the data is transmitted to a remote (geographically distant) medical professional or automated system, which may or may not act on the data to contact the patient, provide advice to the patient, initiate or modify concomitant treatment, or perform any other medical intervention to improve and / or stabilize the patient's health status.

[0253] Remote patient management preferably encompasses remote monitoring of a patient's health status as well as remote medical intervention, guideline-based outpatient care, and / or structured patient education.

[0254] Remote monitoring preferably refers to repeated data collection at the patient's location and its remote transmission to a monitoring system or device that allows review by medical personnel or automated medical systems.

[0255] "Medical personnel" as used herein may relate to, but is not limited to, a medical professional or a team of medical professionals, including a doctor, a nurse, or a paramedic. "Automated medical system" preferably means a computer-based system that allows for the automatic processing of medical data and the generation of an output accordingly, for example generating an alert message or advice for a remotely managed patient. The automated medical system may preferably employ a computer-implemented method based on artificial intelligence and / or artificial neural networks that can access medical data to generate an output.

[0256] In a preferred embodiment, data collection on-site with the patient takes place in their normal home environment, thus minimizing interference with the patient's daily routine.

[0257] Remote transmission means that data is transmitted to a remote medical personnel or automated medical system (i.e., a geographically separated personnel or system) that is not at the patient's site. For example, in a typical setting, data is collected from various patients while these patients are at home, at work, or at other locations they attend (e.g., outpatient), and the data is transmitted to a central medical center equipped to monitor and / or process the remotely transmitted data.

[0258] In preferred embodiments, the distance between the patient's location and the medical personnel or automated medical system to which the data is remotely transmitted is at least 100 m, at least 1 km, at least 5 km, or more.

[0259] In some embodiments, the patient may also be undergoing outpatient care or hospitalized when the data is collected. In these cases, too, the data is transmitted remotely to a medical practitioner or automated medical system. For example, the patient may be undergoing outpatient care or hospitalized in a facility that does not specialize in the treatment of cardiovascular diseases such as heart failure, and the data is transmitted to a medical center where specially trained medical practitioners or automated medical practitioners can act on the data and initiate appropriate medical interventions.

[0260] Health status data refers to any data suitable for monitoring a patient's health status, preferably with respect to the status or progression of cardiovascular disease, most preferably heart failure.

[0261] In one embodiment, the data relating to the patient's health condition includes vital signs or parameters selected from the group consisting of temperature, blood pressure, pulse (heart rate), breathing rate (respiratory rate), oxygen saturation, or blood glucose level.

[0262] In a preferred embodiment, the data regarding the patient's health condition includes blood pressure.

[0263] In a preferred embodiment, the data regarding the patient's health condition includes an electrocardiogram (ECG).

[0264] In a preferred embodiment, the data regarding the patient's health status includes peripheral blood oxygen saturation (SpO2).

[0265] In a preferred embodiment, the data regarding the patient's health condition includes weight.

[0266] In a preferred embodiment, the data regarding the patient's health status includes a self-assessed health status. The self-assessed health status may, for example, relate to a grading system or scale covering a subjective assessment of good to poor health status, i.e. "very good" to "very bad" with different intermediate grades. A typical number of grades may be between 3 and 10, preferably 5.

[0267] In a preferred embodiment, the data regarding the patient's health status comprises self-assessed indications of typical symptoms associated with the progression of cardiovascular disease, preferably heart failure, including aching, shortness of breath, burning sensation, cramping, discomfort, bloating, tingling, indigestion, lightheadedness, nausea, numbness, tingling, pain, pressure, shortness of breath, sweating, dizziness, squeezing, tension, vomiting, irregular heartbeat, palpitations, chest pounding, fatigue, weakness, the presence of peripheral edema and / or other suitable symptoms.

[0268] In a preferred embodiment, the data regarding the patient's health status includes two or more, preferably three, four, five or more data types selected from the group consisting of blood pressure, ECG, SpO2, weight, self-assessed health status, and / or symptoms related to the progression of cardiovascular disease.

[0269] In one embodiment, data regarding the patient's health status is collected and periodically transmitted to a remote medical practitioner or automated medical system. The time interval between data collections may vary significantly depending on the different types of data being collected. For example, when monitoring heart rate with a mobile pulsometer, data collection and transmission may occur in near real-time, with status updated every second, whereas, for example, weight may be measured and transmitted remotely only on a daily basis.

[0270] Any suitable device or system can be used to collect the data, including systems that are standalone or mobile units carried by the patient, located in the patient's home, such as a watch, an electronic probe placed on or implanted in the patient's skin, etc.

[0271] Cardiovascular monitoring devices may include one or more of a cardiac loop recorder (e.g., for recording cardiac rhythm), a heart rate monitor, a blood pressure monitor, a hemodynamic monitor, a vital sign detector device (e.g., electro-oculography, electromyography, electrocardiography, galvanic skin response, magnetoencephalography, etc.), a weight scale (e.g., a smart weight scale), and / or other suitable devices for monitoring cardiovascular activity. In variations, a mobile computing device may perform the functions of a cardiovascular device (e.g., a smart watch that includes heart rate monitoring functionality).

[0272] In the prior art, various monitoring and data transmission devices are known for remotely monitoring data relating to the health status of a patient, in particular with regard to parameters related to cardiovascular diseases.

[0273] For example, early telemetry systems of the type described by Lewis in U.S. Pat. No. 3,943,918 and by Crovella et al. in U.S. Pat. No. 4,121,573 use telemetry technology to transmit data from a sensor device attached to the patient's chest via RF to a radio telemetry receiver for display and / or recording as desired. SSNg described yet another telemetry system for ECG monitoring in an article entitled "Microprocesor-based Telemetry System for ECG Monitoring" (IEEE / Ninth Annual Conference of the Engineering in Medicine and Biology Society, CH2513-0, pages 1492-93 (1987)). Therein, Ng describes a system for providing continuous ECG monitoring and analysis by a PC AT via a wireless link. In the Ng system, the patient requires a transmitter carried by the patient to sense the patient's ECG signal and transmit it via a wireless link to a central base station. At the base station, a receiver recovers and displays the original ECG signals from several patients simultaneously.

[0274] Bornn et al., in U.S. Patents 4,784,162, 4,827,943, 5,214,939, 5,348,008, 5,353,793, and 5,564,429, describe a miniature physiological data monitoring / alarm system in which one or more patients wear a sensor harness containing a microprocessor that detects potentially life-threatening events and automatically calls a central base station via radio telemetry using a wireless modem link. In a home site or alternate site configuration, communication between the base station and the remote units is via commercial telephone lines. Typically, when an abnormality is detected on the ECG monitor, the system automatically calls "911" or a similar emergency response service.

[0275] Segalowitz, in U.S. Patents 4,981,141, 5,168,874, 5,307,818, and 5,511,553, discloses a wireless vital signs monitoring system that includes a precordial strip patch that includes a multi-layer flexible structure for telemetry of data by radio frequency and a single wire to a hardware recorder and display monitor. Microsensors and conductive contact elements (CCEs) are attached to the strip patch, allowing for simultaneous and continuous detection, processing, and transmission of 12-lead ECG, cardiac output, respiratory rate, peripheral blood oximetry, patient temperature, and ECG fetal heart monitoring via a single wavelength of radio frequency transmission.

[0276] Platt et al. also disclose a sensor patch for wireless physiological monitoring of a patient in U.S. Pat. No. 5,634,468. Platt et al. describe a sensor and system for remotely monitoring ECG signals from a patient at a non-hospital location. In this system, a sensor patch including sensing electrodes, signal processing circuitry, and radio or infrared transmission circuitry is attached to the patient's body and worn, preferably for at least one week, after which the power source is exhausted and the sensor patch is discarded. A receiver at a primary site near the patient receives the data transmitted by the sensor patch and stores the sensed data. If the patient experiences discomfort or concern, or if the portable unit sounds an alarm, the patient calls the monitoring station and downloads the stored data from the portable unit via a standard voice communication network. The downloaded ECG data is then monitored and analyzed at the monitoring station. The receiver near the patient can be a portable unit carried by the patient, which includes a receiver, a processor for processing the received data to identify abnormalities, a memory for storing the sensed data, and circuitry for interfacing to a telephone line and transmitting the ECG data signal to the monitoring station. The monitoring station decodes the received ECG signal and performs pulse and rhythm analysis for classification of the ECG data, and notifies medical personnel near the patient if any abnormalities are found.

[0277] Langer et al. in U.S. Pat. No. 5,522,396 disclose a telemetry system for monitoring a patient's heart in which a patient station includes a telemetry device for transmitting the output of the patient electrodes to a Telelink unit connected to the monitoring station by telephone lines. Like the Platt et al. system, Langer et al. transmits ECG data to a central location. However, unlike the Platt et al. system, the Langer et al. system checks the ECO data for predetermined events and automatically pages the monitoring station when such an event is detected. A similar telemetry system is described by Davis et al. in U.S. Pat. No. 5,544,661, which initiates a cellular telephone link from the patient to a central monitoring location when an event is detected.

[0278] US Patent No. 6,416,471 B1 discloses a system for monitoring vital signs and capturing data remotely from a patient using radio telemetry technology. The system uses a cordless, disposable sensor band with sensors for measuring full-waveform ECG, full-waveform respiration, skin temperature, and movement, and a transmitting circuit for detecting and transmitting the patient's vital signs data. A signal transfer unit, which the patient can wear on or place nearby, e.g., on his or her belt, receives the data from the sensor band and transfers it, e.g., by wireless transmission, to a base station designed to connect to a conventional telephone line for transferring the collected data to a remote monitoring station for review by medical personnel. The base station can also capture additional clinical data, such as blood pressure.

[0279] In a proof-of-concept study, Spethmann et al. demonstrated the feasibility of recording, transferring, and analyzing ECGs via mobile phone in marathon runners for remote transmission to a monitoring station (ECG streaming) (Spethmann et al., 2014).

[0280] The above remote monitoring systems, and the references cited therein, are intended to illustrate, without limitation, exemplary devices and systems that can be used for remote patient management for purposes of the methods described herein.

[0281] Remote patient management as used herein relies on the collection of data as described herein and their transmission to a remote medical practitioner or automated medical system using suitable means known in the prior art. Different technical means for achieving these means are known to the skilled person and therefore will not be described in detail herein.

[0282] In a particular preferred embodiment, a device for remote patient management is used based on a Bluetooth® system with a digital tablet (Physio-Gate® PG1000, GETEMED Medizin-und Informationstechnik AG) as the central structural element for transmitting vital measurements from the patient's home to a monitoring station in the medical center. Preferably, different measuring devices are included, preferably a three-channel ECG device for collecting specific periods, e.g. 2 min, or streaming ECG measurements (PhysioMem® PM1000 GETEMED Medizin- und Informationstechnik AG), a device for collecting peripheral capillary oxygen saturation (SpO2; Masimo Signal Extraction Technology (SET®)) and a system for collecting blood pressure (UA767PBT, A&D Ltd.) and a weight scale (Seca 861, seca GmbH &Co KG). Each device is preferably equipped with a Bluetooth® chip and connected to a digital tablet. As software for the monitoring station of the telemedicine center there is “Fontane” (eHealth Connect 2.0, T-Systems International GmbH), which was specially developed for use in the TIM-HF2 study and described by Koehler et al. 2018.

[0283] In a preferred embodiment, data relating to the patient's health status is transmitted to a monitoring station for review by medical personnel or a medical automation system. It is particularly preferred that the monitoring station comprises means for processing the transmitted data, preferably for alerting and / or guiding review by medical personnel.

[0284] In a preferred embodiment, for each type of transmitted data, a warning zone can be assigned. For example, the monitoring station can actively process the transmitted data and issue a warning as soon as a given transmitted data type or combination of data types enters a warning zone indicating a worsening of the disease or an increased risk of an adverse event. As a result, the monitoring station can display, sound or transmit a warning directly to the medical personnel's mobile telecommunication device.

[0285] In a preferred embodiment, remote patient management includes remote medical intervention, which preferably relates to any contact with a patient initiated by a remote medical practitioner or an automated medical system. In a preferred embodiment, contact is initiated via telephone, video call, email, chat program, SMS, or other by other electronic messaging means.

[0286] In some embodiments, remote patient management includes remote medical intervention in the form of a telephone or video call in case health status data enters a warning zone.

[0287] In some embodiments, remote patient management includes a structured consultation, preferably via telephone, between a medical professional and a patient on a regular basis, during which the medical professional preferably discusses the disease state with the patient based on the remotely transmitted data, evaluates symptoms of depression or other illnesses, discusses current treatments such as prescriptions, and provides advice regarding the initiation or modification of current treatments.

[0288] In a preferred embodiment, the structured consultation takes place periodically with a period between contacts ranging from 1 day to 3 months, preferably 1 week to 1 month, and most preferably once a month.

[0289] In some embodiments, a structured consultation is further initiated to verify vital sign measurements or other data regarding the patient's health status when deemed appropriate by a medical professional or automated medical system, for example when remotely transmitted data indicates a change in disease status in the case of an apparent technical problem.

[0290] In some embodiments, remote patient management uses algorithms to prioritize the review of remotely managed patients by medical personnel. To this end, cut-off values ​​can be defined for parameters derivable from the transmitted data, such as bradycardia heart rate, tachycardia heart rate, SpO2, weight, blood pressure, or self-assessed health status.

[0291] Based on the prioritization, remote patient management workload and work flows can be optimized to ensure that patients with the greatest need are attended to promptly.

[0292] In preferred embodiments, review of the transmitted data is performed by medical personnel at regular intervals, preferably with a period between reviews of less than one week, preferably less than three days, less than one day, or less than 12 hours. In some embodiments, review of the transmitted data is performed in real time by medical personnel or a medical automation system. When using an algorithm to prioritize review of remotely managed patients by medical personnel, these periods are selected accordingly, with high priority patients being reviewed more frequently, for example twice a day, and non-priority patients being reviewed once a day or every other day.

[0293] In some embodiments, remote patient management includes consulting, advising or instructing regarding medication or medical treatment of the remotely managed patient.

[0294] In the context of the present invention, "medication" may include various treatments and treatment strategies, including, but not limited to, angiotensin-converting enzyme (ACE) inhibitors, angiotensin II receptor blockers (ARBs), aldosterone antagonists, beta-blockers, diuretics and / or calcium antagonists, with respect to cardiovascular disease, preferably heart failure.

[0295] Remote patient management may preferably include consultation, initiation of hospitalization, initiation of escalation of patient care, advice or instructions regarding type, frequency, and / or dosage of medication regarding the treatment of cardiovascular disease, such as heart failure.

[0296] In some embodiments, for each type or combination of transmitted data, a safety zone or an improvement zone can be assigned. For example, based on medical data about a patient suffering from cardiovascular disease, a zone can be defined in which the patient feels optimally well despite his condition and / or in which an improvement in the condition is expected. In contrast to using a remote patient management system only to prevent acute and adverse events, such remote patient management makes it possible to modify the progression of a disease or condition and to have a significant positive impact on clinical outcomes.

[0297] For example, for a safety or improvement zone for a patient suffering from heart failure, one or more or all of the following goals may be defined: for patients with sinus rhythm, heart rate <75 b.pm; blood pressure control: systolic <140 mmHg and diastolic <90 mmHg; for patients with new onset atrial fibrillation, the use of anticoagulant and antiarrhythmic therapy as long-term treatment; for patients with NYHA classification II-IV, the use of mineralocorticoid receptor antagonists when possible.

[0298] As shown in Koehler et al. 2018, the implementation of telemedicine interventions in addition to the mere remote monitoring described herein, which may be prioritized by appropriate data processing and / or promotion of improvement or safety zones, results in particularly good outcomes in improving the quality and longevity of patients' lives. A surprising additional insight of the method described herein relates to the discovery that early biomarkers can be used to assess the therapeutic benefit of individual patients and to decide whether or not to prescribe remote patient management.

[0299] The methods described herein relate to therapy guidance, stratification, and / or monitoring of remote patient management of a patient based on determining the level of at least one biomarker selected from the group consisting of proADM, proBNP, and proANP.

[0300] The expressions "at least one biomarker selected from the group consisting of proADM, proBNP and proANP" as well as "proADM, proBNP and / or proANP" are to be understood as embracing all possible combinations, preferably including the determination of exactly one of the three biomarkers, i.e. proADM, proBNP or proANP, as well as the determination of a combination of exactly two of the biomarkers, i.e. proADM and proBNP, proADM and proBNP or proBNP and proANP, or the determination of all three biomarkers proADM, proBNP and proANP.

[0301] It is further understood that the phrase does not preclude the additional determination of further biomarkers or parameters.

[0302] In the context of the present invention, "determining the level of proADM or its fragment(s)" and the like is understood to refer to any means of determining proADM or its fragments. The fragment may have any length, for example at least about 5, 10, 20, 30, 40, 50, or 100 amino acids, as long as the fragment allows an unambiguous determination of the level of proADM or its fragment. In a particularly preferred embodiment of the present invention, "determining the level of proADM" refers to determining the level of mid-region pro-adrenomedullin (MRproADM). MRproADM is a fragment and / or region of proADM.

[0303] "ProAdrenomedullin" ("ProADM") refers to pre-proADM without the signal sequence (amino acids 1-21), i.e., amino acid residues 22-285 of pre-proADM. "Mid-region proAdrenomedullin" ("MRproADM") refers to amino acids 42-95 of pre-proADM.

[0304] The amino acid sequence of MRproADM is shown in SEQ ID NO: 4. It is also contemplated herein that peptides of pre-proADM or MRproADM and fragments thereof can be used in the methods described herein. For example, peptides and fragments thereof can include amino acids 22-41 of pre-proADM (PAMP peptide), or amino acids 95-146 of pre-proADM (mature adrenomedullin). The C-terminal fragment of proADM (amino acids 153-185 of pre-proADM) is called adrenotensin. A proADM peptide or a fragment of MRproADM can include, for example, 5 or more amino acids. A fragment of a proADM peptide or a fragment of MRproADM can include, for example, at least about 5, 10, 20, 30 or more amino acids. Thus, a fragment of proADM can be selected from the group consisting of, for example, MRproADM, PAMP, adrenotensin and mature adrenomedullin, and preferably herein the fragment is MRproADM.

[0305] Furthermore, mid-regional pro-adrenomedullin (MRproADM) has been disclosed for diagnostic purposes in EP1488209B1 (Struck J, Tao C, Morgenthaler NG, Bergmann A. Identification of an Adrenomedullin precursor fragment in plasma of sepsis patients. Peptides 2004;25:1369-72; Morgenthaler NG, Struck J, Alonso C, Bergmann A. Measurement of mid-regional pro-adrenomedullin in plasma with an immunoluminometric assay. Clin Chem 2005;51:1823-9; Christ-Crain M, Morgenthaler NG, Stolz D, Muller C, Bingisser R, Harbarth S, et al. Pro-adrenomedullin to predict severity and outcome in community-acquired pneumonia [ISRCTN04176397]. Crit Care 2009; 2006;10:R96;Christ-Crain M,Morgenthaler NG,Struck J,Harbarth S,Bergmann A,Muller B.Mid-regional pro-adrenomedullin as a prognostic marker in sepsis:an observational study.Crit Care 2005;9:R816-24).

[0306] The peptide "adrenomedullin (ADM)" was first described in 1993 as a novel antihypertensive peptide containing 52 amino acids isolated from human pheochromocytoma (Kitamura et al. (1993), Biochem. Biophys. Res. Commun. 192:553-560). In the same year, a precursor peptide containing 185 amino acids and a cDNA encoding the complete amino acid sequence of this precursor peptide were also described (Kitamura et al. (1993), Biochem. Biophys. Res. Commun. 194:720-725). In particular, the precursor peptide containing a 21 amino acid signal sequence at the N-terminus is called "pre-pro-adrenomedullin" (pre-proADM). The ADM peptide contains amino acids 95 to 146 of pre-proADM and is formed from pre-proADM by proteolytic cleavage. Several peptide fragments formed by cleavage of pre-proADM have been characterized in detail, in particular the physiologically active peptide adrenomedullin (ADM) and "PAMP", i.e. a peptide containing 20 amino acids (22-41) following the 21 amino acids of the signal peptide in pre-ProADM. Another fragment of unknown function and high ex vivo stability is the mid-region pro-adrenomedullin (MRproADM) (Struck et al. (2004), Peptides 25(8):1369-72), for which a reliable quantification method has been developed (Morgenthaler et al. (2005), Clin. Chem. 51(10):1823-9). ADM is a potent vasodilator. The antihypertensive effect is associated with peptide moieties, especially in the C-terminal part of ADM.

[0307] On the other hand, the N-terminal peptide sequence of ADM shows hypertensive activity (Kitamura et al. (2001), Peptides 22, 1713-1718). N-terminal fragments of (pre)proadrenomedullin for diagnostic purposes such as PAMP are also described in EP0622458B1 (Hashida S, Kitamura K, Nagatomo Y, Shibata Y, Imamura T, Yamada K, et al. Development of an ultra-sensitive enzyme immunoassay for human pro-adrenomedullin Nterminal peptide and direct measurement of two molecular forms of PAMP in plasma from healthy subjects and patients with cardiovascular disease. Clin Biochem 2004;37:14-21).

[0308] A diagnostic C-terminal fragment of (pre)proadrenomedullin has also been described in EP 2111552 B1, namely CTproADM (adrenotensin).

[0309] Furthermore, the prior art describes methods for determining proadrenomedullin (proADM) and adrenomedullin in diagnosis (EP 0622458 B1, Lewis LK, Smith MW, Yandle TG, Richards AM, Nicholls MG. Adrenomedullin (1-52) measured in human plasma by radioimmunoassay: plasma concentration, absorption, and storage. Clin Chem 1998; 44: 571-7; Ueda S, Nishio K, Minamino N, Kubo A, Akai Y, Kangawa K, et al. Increased plasma levels of adrenomedullin in patients with systemic inflammatory response syndrome. Am J Respir Crit Care Med 1999; 160: 132-6; Kobayashi K, Kitamura K, Etoh T, Nagatomo Y, Takenaga M, Ishikawa T, et al. Increased plasma levels of adrenomedullin in patients with systemic inflammatory response syndrome. Am J Respir Crit Care Med 1999; 160: 132-6; Plasma adrenomedullin levels in chronic congestive heart failure. Am Heart J 1996;131:994-8; Kobayashi K, Kitamura K, Hirayama N, Date H, Kashiwagi T, Ikushima I, et al. Increased plasma adrenomedullin in acute myocardial infarction. Am Heart J 1996;131:676-80.), particularly for the purpose of diagnosing sepsis (EP1121600B1). SEQ ID NO: 1: Amino acid sequence of pre-proADM, 185AS: MKLVSVALMY LGSLAFLGAD TARLDVASEF RKKWNKWALS RGKRELRMSSSYPTGLADVK AGPAQTLIRP QDMKGASRSP EDSSPDAARI RVKRYRQSMNNFQGLRSFGC RFGTCTVQKL AHQIYQFTDK DKDNVAPRSK ISPQGYGRRRRRSLPEAGPG RTLVSSKPQA HGAPAPPSGS APHFL SEQ ID NO: 2: Amino acid sequence of proADM (AS22-185 of pre-proADM): ARLDVASEF RKKWNKWALS RGKRELRMSSSYPTGLADVK AGPAQTLIRP QDMKGASRSP EDSSPDAARI RVKRYRQSMNNFQGLRSFGC RFGTCTVQKL AHQIYQFTDK DKDNVAPRSK ISPQGYGRRRRRSLPEAGPG RTLVSSKPQA HGAPAPPSGS APHFL SEQ ID NO: 3: Amino acid sequence of PAMP (AS22-41 of pre-proADM): ARLDVASEFRKKWNKWALSR SEQ ID NO: 4: Amino acid sequence of MRproADM (AS45-92 of preproADM): ELRMSSSYPTGLADVKAGPAQTLIRPQDMKGASRSPEDSSPDAARIRV SEQ ID NO: 5: Amino acid sequence of mature ADM (AS95-146 of pre-proADM): YRQSMNNFQGLRSFGCRFGTCTVQKLAHQIYQFTDKDKDNVAPRSKISPQGY SEQ ID NO: 6: Amino acid sequence of adrenotensin or C-terminal ADM fragment (AS148-185 of pre-proADM): RRRRRSLPEAGPGRTLVSSKPQAHGAPAPPSGSAPHFL (Adrenomedullin Uniprot number: P353118)

[0310] It is also contemplated herein that peptides of pre-proADM or MRproADM and fragments thereof can be used in the methods described herein. For example, the peptide or fragment thereof can include amino acids 22-41 of pre-proADM (PAMP peptide), or amino acids 95-146 of pre-proADM (mature adrenomedullin, including the biologically active form also known as bio-ADM). The C-terminal fragment of proADM (amino acids 153-185 of pre-proADM) is called adrenotensin. A fragment of the proADM peptide or a fragment of MRproADM can include, for example, at least about 5, 10, 20, 30 or more amino acids. Thus, a fragment of proADM can be selected from the group consisting of, for example, MRproADM, PAMP, adrenotensin and mature adrenomedullin, and preferably, the fragment herein is MRproADM.

[0311] Determination of these various forms of ADM or proADM and fragments thereof also encompasses measuring and / or detecting specific subregions of these molecules, for example, by using antibodies or other affinity reagents directed to specific portions of the molecules, or by determining the presence and / or amount of the molecules by measuring portions of the protein using mass spectrometry. Any one or more of the "ADM peptides or fragments" described herein can be used in the present invention.

[0312] The level of proADM in a subject's sample can be determined by immunoassay as described herein. As used herein, the level of ribonucleic acid or deoxyribonucleic acid encoding "proadrenomedullin" or "proADM" can also be determined. Methods for determining proADM and its fragment(s) are known to those skilled in the art, for example, by using products obtained from Thermo Fisher Scientific / B·R·A·H·M·S GmbH.

[0313] In the context of the present invention, "determining the level of proBNP or a fragment(s) thereof" and the like is understood to refer to any means of determining proBNP or a fragment thereof. The fragment may have any length, for example at least about 5, 10, 20, 30, 40, 50, or 100 amino acids, so long as the fragment allows for an unambiguous determination of the level of proBNP or a fragment thereof. In a particularly preferred embodiment of the present invention, "determining the level of proBNP" refers to determining the level of N-terminal pro-B-type natriuretic peptide (NTproBNP). NTproBNP is a fragment and / or region of proBNP.

[0314] "B-type natriuretic peptide (BNP)" is a quantitative marker of heart failure. The use of B-type natriuretic peptide (BNP) and its amino-terminal fragment, N-terminal pro-B-type natriuretic peptide (NTproBNP), significantly improves the diagnostic accuracy of ED [Januzzi, JL, Jr., et al., Am J Cardiol, 2005.95(8):p.948-54;Maisel, AS, et al., N Engl J Med, 2002.347(3):p.161-7], thereby improving patient evaluation and treatment [Moe, GW, et al., Circulation, 2007.115(24):p.3103-10;Mueller, C., et al., N Engl J Med, 2004.350(7):p.647-54]. The concentration of atrial natriuretic peptide (ANP) in the circulation is approximately 50-100 times higher than that of BNP [Pandey,KN,Peptides,2005.26(6):p.901-32]. Therefore, the biological signal reflected by increased ANP may be pathophysiologically and therefore diagnostically even more important than that of BNP. Nevertheless, little is known about the diagnostic performance of ANP and its precursors [Cowie,MR,et al.,Lancet,1997.350(9088):p.1349-53]. Mature ANP is derived from the precursor N-terminal proANP (NTproANP), which is significantly more stable in the circulation than the mature peptide and is therefore considered to be a more reliable analyte [Vesely,DL,IUBMB Life,2002.53(3):p.153-[Pandey,KN,Peptides,2005.26(6):p.901-32]. Nevertheless, due to the fact that NTproANP may be subject to further fragmentation [Cappellin, E., et al., Clin Chim Acta, 2001.310(1):p.49-52], immunoassays for the measurement of mid-region proANP (MRproANP) may have advantages [Morgenthaler, NG, et al., 2004.50(1):p.234-6].

[0315] The sequence of the 134 amino acid precursor peptide of brain natriuretic peptide (pre-proBNP) is shown in SEQ ID NO: 7. proBNP is related to amino acid residues 27-134 of pro-proBNP. The sequence of proBNP is shown in SEQ ID NO: 8. proBNP is cleaved into N-terminal proBNP (NTproBNP) and mature BNP. NTproBNP contains amino acid residues 27-102 and its sequence is shown in SEQ ID NO: 9. SEQ ID NO: 10 shows the sequence of BNP which contains amino acid residues 103-134 of the pre-proBNP peptide. SEQ ID NO: 7: Amino acid sequence of pre-proBNP): MDPQTAPSRA LLLLLFLHLA FLGGRSHPLG SPGSASDLET SGLQEQRNHL QGKLSELQVE QTSLEPLQES PRPTGVWKSR EVATEGIRGH RKMVLYTLRA PRSPKMVQGS GCFGRKMDRI SSSSGLGCKV LRRH SEQ ID NO:8: (amino acid sequence of proBNP): HPLGSGSAS DLETSGLQEQ RNHLQGKLSE LQVEQTSLEP LQESPRPTGV WKSREVATEG IRGHRKMVLY TLRAPRSPKM VQGSGCFGRK MDRISSSSGL GCKVLRRH SEQ ID NO:9: (Amino acid sequence of NTproBNP): HPLGSPGSAS DLETSGLQEQ RNHLQGKLSE LQVEQTSLEP LQESPRPTGV WKSREVATEG IRGHRKMVLY TLRAPR SEQ ID NO:10: (amino acid sequence of BNP): SPKMVQGSGC FGRKMDRISS SSGLGCKVLR RH (BNP Uniprot number: P16860)

[0316] In a particularly preferred embodiment, NTproBNP is measured to determine the level of proBNP. The length of the proBNP fragment is therefore preferably at least 12 amino acids, preferably more than 20 amino acids, more preferably more than 40 amino acids.

[0317] The determination of these various forms of proBNP and fragments thereof also encompasses measuring and / or detecting specific subregions of these molecules, for example, by using antibodies or other affinity reagents directed to specific portions of the molecules, or by determining the presence and / or amount of the molecules by measuring portions of the protein using mass spectrometry. Any one or more of the "proBNP peptides or fragments" described herein can be used in the present invention.

[0318] The level of proBNP in a subject's sample can be determined by immunoassay as described herein. As used herein, the level of ribonucleic acid or deoxyribonucleic acid encoding "B-type natriuretic peptide" or "proBNP" can also be determined. Methods for determining proBNP and its fragment(s) are known to those skilled in the art, for example by using products obtained from Thermo Fisher Scientific / B·R·A·H·M·S GmbH.

[0319] In the context of the present invention, "determining the level of proANP or its fragment(s)" and the like is understood to refer to any means of determining proANP or its fragments. The fragment may have any length, for example at least about 5, 10, 20, 30, 40, 50, or 100 amino acids, so long as the fragment allows an unambiguous determination of the level of proANP or its fragment. In a particularly preferred embodiment of the present invention, "determining the level of proANP" refers to determining the level of mid-region proANP (MRproANP). MRproANP is a fragment and / or region of proANP.

[0320] The amino acid sequence of "atrial natriuretic peptide (ANP)" is shown in SEQ ID NO: 13. The sequence of 153 amino acids of pre-proANP is shown in SEQ ID NO: 11. Cleavage of the N-terminal signal peptide (25 amino acids) and the two C-terminal amino acids (127 / 128) releases proANP (SEQ ID NO: 12). ANP comprises residues 99-126 from the C-terminus of the precursor prohormone proANP. This prohormone is cleaved into the mature 28 amino acid peptide ANP, also known as ANP(1-28) or α-ANP, and the amino-terminal fragment ANP(1-98) (NTproANP, SEQ ID NO: 14). Mid-region proANP (MRproANP) is defined as NTproANP or any fragment thereof that comprises at least amino acid residues 53-90 of proANP (SEQ ID NO: 15). Since the two C-terminal arginine residues (positions 152 and 153 of pre-proANP, SEQ ID NO: 11) are not present in alternative alleles of the gene encoding pre-proANP, pre-proANP may contain only residues 1 to 151. Of course, this also applies to the respective fragments of pre-proANP, in particular proANP.

[0321] "Atrial natriuretic peptide" or "proANP" refers to a prohormone containing 128 amino acids. As used herein, the peptide containing 28 amino acids (99-126) of the C-terminal part of the 128 amino acid prohormone (proANP) is called the actual hormone ANP. When ANP is released from the prohormone proANP, an equimolar amount of the remaining larger partial peptide of proANP, N-terminal proANP (NTproANP, proANP(1-98)), consisting of 98 amino acids, is released into the circulation. NTproANP has a very long half-life and stability, so that NTproANP can be used as a test parameter for diagnosis, follow-up, and treatment management, see for example Lothar Thomas (Editor), Labor und Diagnose, 5th expanded ed., sub-chapter 2.14 of chapter 2, Kardiale Diagnostik, pages 116-118, and WO2008 / 135571. The level of proANP is preferably measured in the plasma or serum of the subject.

[0322] Atrial natriuretic peptide (ANP), a member of the natriuretic peptide family, regulates several physiological parameters, including diuresis and natriuresis, as well as the reduction of arterial blood pressure (BP). It is produced primarily in the atria of the heart and constitutes 98% of the circulating natriuretic peptides (Vesely DL. Life 2002;53:153-159). ANP is derived from the cleavage of its precursor prohormone, which is significantly more stable in the circulation than the mature peptide. A mid-region fragment of the precursor hormone (amino acids 53-90 of NTproANP), termed mid-region proANP (MRproANP), may be relatively resistant to degradation by exoproteases, unlike the N- or C-terminal epitopes of proANP used in previous immunoassays (Morgenthaler NG et al. Clin Chem 2004;50:234-236; Gerszten RE et al. 2008. Am J Physiol Lung Cell Mol Physiol). SEQ ID NO:11 (amino acid sequence of pre-proANP, 153AS): MSSFSTTTVS FLLLLAFQLL GQTRANPMYN AVSNADLMDF KNLLDHLEEK MPLEDEVVPP QVLSEPNEEA GAALSPLPEV PPWTGEVSPA QRDGGALGRG PWDSSDRSAL LKSKLRALLT APRSLRRSSC FGGRMDRIGA QSGLGCNSFR YRR SEQ ID NO:12: (amino acid sequence of proANP): NPMYNAVSNA DLMDFKNLLD HLEEKMPLED EVVPPQVLSE PNEEAGAALS PLPEVPPWTG EVSPAQRDGG ALGRGPWDSS DRSALLKSKL RALLTAPRSL RRSSCFGGRM DRIGAQSGLG CNSFRY SEQ ID NO: 13: (amino acid sequence of ANP, AS124 to AS151 of pre-proANP): SLRRSSCFGG RMDRIGAQSG LGCNSFRY SEQ ID NO: 14: (amino acid sequence of NTproANP, AS26 to AS123 of preproANP): NPMYNAVSNA DLMDFKNLLD HLEEKMPLED EVVPPQVLSE PNEEAGAALS PLPEVPPWTG EVSPAQRDGG ALGRGPWDSS DRSALLKSKL RALLTAPR SEQ ID NO: 15: (amino acid sequence of MRproANP, AS53-90 of proANP): PEVPPWT GEVSPAQRDG GALGRGPWDS SDRSALLKSK L (Atrial natriuretic peptide Uniprot number: P01160)

[0323] Determination of these various forms of proANP and its fragments also encompasses measuring and / or detecting specific subregions of these molecules, for example, by using antibodies or other affinity reagents directed to specific parts of the molecule, or by determining the presence and / or amount of the molecule by measuring portions of the protein using mass spectrometry. Any one or more of the "proANP peptides or fragments" described herein can be used in the present invention.

[0324] The level of proANP in a subject's sample can be determined by immunoassay as described herein. As used herein, the level of ribonucleic acid or deoxyribonucleic acid encoding "atrial natriuretic peptide" or "proANP" can also be determined. Methods for determining proANP and its fragment(s) are known to those skilled in the art, for example by using products obtained from Thermo Fisher Scientific / B·R·A·H·M·S GmbH.

[0325] The term "fragment" refers to a smaller protein or peptide derivable from a larger protein or peptide, and thus includes a subsequence of the larger protein or peptide. The fragment is derivable from the larger protein or peptide by deletion of one or more amino acids from the larger protein or peptide. "Fragments" of the biomarkers described herein preferably relate to fragments at least 6 amino acids in length, most preferably at least 12 amino acid residues in length. Such fragments are preferably detectable in an immunological assay as described herein.

[0326] Thus, the methods and kits of the invention may also comprise determining at least one further biomarker, marker, clinical score and / or parameter in addition to proADM, proBNP and / or proANP.

[0327] As used herein, a parameter is a characteristic, feature, or measurable factor that can help define a particular system. A parameter is an important factor for the assessment of health and physiology, such as disease / disorder / clinical condition risk, preferably organ dysfunction(s), risk assessment of adverse events, or the need or scalability of remote patient management. Furthermore, a parameter is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic interventions or disease or health conditions, particularly disease or health conditions of patients suffering from cardiovascular disease.

[0328] Exemplary parameters include age, weight, sex, body mass index, sex, smoking, white blood cell count, sodium, potassium, temperature, ethnic background, blood creatinine, left ventricular ejection fraction (LVEF), right ventricular ejection fraction (LVEF), NYHA classification, MAGGIC heart failure risk score, ACC / AHA classification, risk scores for heart failure (e.g., CHARM risk score, CORONA risk score, I-Preserve score, ADHERE classification and regression tree (CART) model, EFFECT risk score), type of medical procedure, frequency of medical procedures, blood pressure (systolic / diastolic), heart rate, cardiac rhythm by ECG, partial pressure of oxygen, peripheral blood oxygen (SpO2). , jugular venous pressure, presence of peripheral edema or orthopnea, presence of right ventricular heave, self-assessed health status (scale), self-assessed symptoms of cardiovascular disease progression (including pain, shortness of breath, burning sensation, cramping, discomfort, bloating, tingling, indigestion, lightheadedness, nausea, numbness, tingling, pain, pressure, shortness of breath, sweating, dizziness, pressure, tension, vomiting, irregular heartbeat, palpitations, chest pounding, fatigue, weakness, presence of peripheral edema), a parameter indicative of renal function, preferably creatinine clearance rate, glomerular filtration rate (GFR), or the level of at least one of the waste products creatinine and urea, or a parameter indicative of liver function.

[0329] In a preferred embodiment, the at least one clinical parameter is preferably age, weight, body mass index, sex, ethnic background, blood creatinine, left ventricular ejection fraction (LVEF), NYHA classification MAGGIC heart failure risk score, status of medical treatment, blood pressure (systolic / diastolic), heart rate, heart rhythm by ECG, SpO2, self-assessed health status (scale), self-assessed symptoms of cardiovascular disease progression or glomerular filtration rate (GFR).

[0330] As used herein, "age" refers to the length of time that an individual has been alive in years.

[0331] As used herein, "Body Mass Index (BMI)" is a value derived from a subject's mass (weight) and height. BMI is defined as the subject's weight, i.e., body weight divided by the square of the subject's height, and is calculated from weight in kilograms and height in meters, expressed as kg / m 2 BMI is universally expressed in units of 18.5. BMI may be determined using a table or chart (reference value) that displays BMI as a function of mass and height using contour lines or colors for the different BMI categories, or may use two different units of measurement. BMI is an attempt to quantify an individual's tissue mass (muscle, fat, and bone) and classify that person as underweight, normal weight, overweight, or obese based on that value. The commonly accepted ranges for BMI are underweight: under 18.5, normal weight: 18.5-25, overweight: 25-30, and obese: over 30.

[0332] As used herein, "body weight" refers to the mass of a subject in kg. In the context of the present invention, normal body weight can be theoretically calculated according to the Devin formula or the Hamwi method. According to the Hamwi method, the ideal body weight for men is 48kg+2.7kg for every 2.54cm above 1.5m. For women, it is 45kg+2.3kg for every 2.54cm above 1.5m. Values ​​below or above these normal values ​​indicate increased risk of important subjects and / or progression of disease.

[0333] As used herein, the term "glomerular filtration rate (GFR)" refers to the rate at which the kidneys filter blood and remove excess waste and fluid, and provides a calculation to measure remaining renal function. Typically, GFR is not measured directly in humans, but is assessed from clearance measurements or serum levels of filtration markers, primarily exogenous or endogenous solutes that are removed by glomerular filtration. GFR can be calculated using formulas well known in the art, for example, by comparing a person's size, age, sex, and / or race with serum creatinine levels.

[0334] In some embodiments, GFR is calculated using the formula for estimated creatinine clearance rate (eCCr) published in Cockroft et al. 1976.

number

[0335] where wt refers to weight in kilograms (kg) and Scr refers to serum creatinine level in mg / 100 ml. If the patient is female, the resulting value is preferably multiplied by a constant of 0.85.

[0336] When serum creatinine is measured in μmol / L, the formula is preferably adapted as follows:

number

[0337] where wt refers to weight in kilograms (kg) and Scr refers to serum creatinine level in μmol / L. The resulting value is preferably multiplied by a constant of 1.23 if the patient is male and 1.04 if the patient is female. This embodiment is preferably referred to as GFR based on Cockroft-Gault.

[0338] In some embodiments, GFR is calculated using an equation called the Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation, published in Levey, AS et al. (2009) (see Table 2). [Table 1]

[0339] This embodiment is preferably referred to as GFR based on CKD-EPI.

[0340] It will be understood that as used herein, the term glomerular filtration rate (GFR) is not limited to the above estimations, but can equally use any estimation or measurement technique, e.g., as described in Levey et al. 2017 and the references cited therein.

[0341] In certain embodiments, renal function is monitored using a small blood sample. As used herein, the term "renal function" is used to describe the health of a patient's kidneys, including excretory function, as determined by the tests or assays described herein or known in the art. Renal function can be monitored in some embodiments by determining glomerular filtration rate, by determining creatinine clearance rate, or by determining the level of at least one of creatinine and urea waste products.

[0342] In some embodiments, renal function is monitored by determining the levels of creatinine, blood urea nitrogen (BUN), or both in a sample using standard methods (see, e.g., Alfawassermann ACE Clinical Chemistry System Operator's Manual, August 2005 revision). When renal function is normal, blood creatinine levels are within the range of about 0.6 to about 1.2 mg / dL or about 53 to about 106 μmol / L in men, about 0.5 to about 1.1 mg / dL or about 44 to about 97 μmol / L in women, about 0.5 to about 1.0 mg / dL in teenagers, about 0.3 to about 0.7 mg / dL in children, and about 0.3 to about 1.2 mg / dL in newborns. When renal function is normal, the ratio of BUN to creatinine is about 10:1 to about 20:1 in patients aged 12 months or older, and up to about 30:1 in patients under 12 months of age.

[0343] In some embodiments, a GFR below 60, preferably below 50, more preferably below 30, is indicative of impaired renal function, ie, the kidneys are not functioning properly.

[0344] In some embodiments, in addition to determining the level of one or more biomarkers described herein, the MAGGIC heart failure risk score is used to determine whether to prescribe remote patient management.

[0345] The MAGGIC heart failure risk score was introduced by Pocock et al. 2013 and takes into account 13 independent clinical parameters (age, sex, diabetes, COPD comorbidities, diagnosis of heart failure within the past 18 months, smoking status, NYHA classification, beta-blockers, ACEi / ARB medication, BMI, systolic blood pressure, creatinine and ejection fraction). The Meta-Analysis Global Group in Chronic (MAGGIC) heart failure risk score has been described to aid in the prognosis of adverse events such as mortality in patients affected by heart failure. However, the MAGGIC heart failure, when combined with the biomarker measurements described herein, may further help to increase the reliability of therapy decisions regarding prescription of remote patient management. In general, a low score on the MAGGIC heart failure risk score may magnify the low benefit level of biomarkers at which patients may be safely excluded from remote patient management.

[0346] As used herein, terms such as "marker", "surrogate", "prognostic marker", "factor", "biomarker" or "biological marker" are used interchangeably and serve as indicators for health-related and physiologically related assessments such as risk of disease / disorder / clinical condition. It refers to measurable and quantifiable biological markers (e.g., concentration of a particular enzyme or its fragment, concentration of a particular hormone or its fragment, distribution of a particular genetic phenotype in a population, presence of a biological substance or its fragment). Furthermore, a biomarker is defined as a characteristic that is objectively measured and evaluated as an indicator of normal biological processes, pathogenic processes, or pharmacological responses to therapeutic interventions. Biomarkers may be measured in biological samples (blood, urine, tissue tests, etc.), may be recordings taken from a person (blood pressure, ECG, Holter), or may be imaging tests (echocardiogram, CT scan) (Vasan et al. 2006, Circulation 113:2335-2362). Biomarkers can indicate various health or disease characteristics, including the level or type of exposure to an environmental factor, genetic susceptibility, genetic response to exposure, biomarkers of asymptomatic or clinical disease, or indicators of response to therapy. Thus, a simple way to think of a biomarker is as an indicator of disease characteristics (risk factor or risk biomarker), disease state (preclinical or clinical), or disease rate (progression). Thus, biomarkers can be used as precursor biomarkers (identifying risk of developing disease), screening biomarkers (screening for asymptomatic disease), diagnostic biomarkers (recognizing overt disease), staging biomarkers (classifying disease severity), or prognostic biomarkers (predicting future disease course, including future recurrence and response to therapy, and monitoring efficacy of therapy). Biomarkers can also function as surrogate endpoints. Surrogate endpoints are endpoints that can be used as outcomes of clinical trials to evaluate the safety and efficacy of a therapy instead of measuring the true outcome of interest. The underlying principle is that changes in the surrogate endpoint are closely related to changes in the outcome of interest.Surrogate endpoints have the advantage that they can be collected in a shorter time frame and at less cost than endpoints such as morbidity and mortality, which require large clinical trials for evaluation. Additional value of surrogate endpoints includes the fact that they are closer to the exposure / intervention of interest and may be easier to attribute causality to than more distant clinical events. An important drawback of surrogate endpoints is that residual confounding may reduce the validity of the surrogate endpoint if the clinical outcome of interest is influenced by many factors (in addition to the surrogate endpoint). It has been suggested that the validity of a surrogate endpoint is higher if it can explain at least 50% of the effect of the exposure or intervention on the outcome of interest. Quantification of biomarkers can be performed by measuring the respective ribonucleic acid, deoxyribonucleic acid, protein, peptide or fragments thereof.

[0347] The at least one additional biomarker and / or parameter of the subject is selected from the group consisting of lactate level, creatine level, hemoglobin level, hematocrit level, white blood cell level, platelet level, sodium level, potassium level, soluble fms-like tyrosine kinase-1 (sFlt-1), histone H2A, histone H2B, histone H3, histone H4, arginine vasopressin (AVP), atrial natriuretic peptide (ANP), myoglobin, neutrophil gelatinase-binding lipocalin (NGAL), troponin, myocardial topoisomerase (MT), myocardial troponin ... troponin T (cnTNT), C-reactive protein (CRP), pancreatic stone protein (PSP), triggering receptor expressed on myeloid cells 1 (TREM1), interleukin-6 (IL-6), interleukin-1, interleukin-24 (IL-24), interleukin-22 (IL-22), interleukin (IL-20), other ILs, presepsin (sCD14-ST), lipopolysaccharide-binding protein (LBP), alpha-1-antitrypsin, matrix metalloproteinase 2 (MMP2), matrix metalloproteinase 2 (MMP8), Metalloproteinase 9 (MMP9), matrix metalloproteinase 7 (MMP7, placental growth factor (PlGF), chromogranin A, S100A protein, S100B protein and tumor necrosis factor alpha (TNFα), neopterin, alpha-1-antitrypsin, proarginine vasopressin (AVP, proAVP, or copeptin), endothelin-1, procalcitonin (PCT), CCL1 / TCA3, CCL11, CCL12 / MCP-5, CCL13 / MCP-4, CCL14, CCL15, CCL16, CCL17 / TARC, CCL18, CCL19, CCL20, CCL21, CCL22, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL29, CCL30, CCL31, CCL32, CCL33, CCL34, CCL35, CCL36, CCL37, CCL38, CCL39, CCL40, CCL41, CCL42, CCL43, CCL44, CCL45, CCL46, CCL47, CCL48, CCL49, CCL50, CCL51, CCL52, CCL53, CCL54, CCL55, CCL56, CCL57, CCL58, CCL59, CCL60, CCL61, CCL62, CCL63, CCL64, CCL65, CCL66, CCL67, CCL68, CCL69, CCL69, CCL70, CCL71, CCL72, CCL73, CCL74, CCL75, CCL76, CCL77, CCL78, CCL79, CCL79, CCL70, CCL71, CCL72, CL18, CCL19, CCL2 / MCP-1, CCL20, CCL21, CCL22 / MDC, CCL23, CCL24, CCL25, CCL26, CCL27, CCL28, CCL3, CCL3L3, CCL4, CCL4L1 / LAG-1, CCL5, CCL6, CCL7, CCL8, CCL9, CX3CL1, CXCL1, CXCL10, CXCL11, CXCL12, CXCL13, CXCL14, CXCL15, CXCL16, CXCL17, CXCL2 / MIP-2, CXCL3, CXCL4, CXCL5, CXCL6, CXCL7 / Ppbp,CXCL9, IL8 / CXCL8, XCL1, XCL2, FAM19A1, FAM19A2, FAM19A3, FAM19A4, FAM19A5, CLCF1, CNTF, IL11, IL31, IL6, leptin, LIF, OSM, IFN A1, IFNA10, IFNA13, IFNA14, IFNA2, IFNA4, IFNA7, IFNB1, IFNE, IFNG, IFNZ, IFNA8, IFNA5 / IFNaG, IFNω / IFNW1, BAFF, 4-1BBL, TNFS F8, CD40LG, CD70, CD95L / CD178, EDA-A1, TNFSF14, LTA / TNFB, LTB, TNFa, TNFSF10, TNFSF11, TNFSF12, TNFSF13, TNFSF15, TNFSF4, IL18, IL18BP, IL1A, IL1B, IL1F10, IL1F3 / IL1RA, IL1F5, IL1F6, IL1F7, IL1F8, IL1RL2, IL1F9, IL33, or fragments thereof.

[0348] In a preferred embodiment, the further biomarker is a "cardiovascular marker" relevant for the diagnosis and / or prognosis of cardiovascular disease, such as myoglobin, troponin T (cTnT) and I (cTnI), creatine kinase MB (CK-MB), FABP, GDF-15, ST-2, procalcitonin (PCT), C-reactive protein (CRP) including proendothelin-1 and fragments thereof including C-terminal proendothelin-1 (CTproET-1), big endothelin-1, endothelin-1, NT-proendothelin-1, proANP and mid-region atrial natriuretic peptide (MRproANP), N-terminal proANP (NTproANP), ANP, provasopressin and C-terminal pro-arginine vasopressin peptide (CTproAVP), vasopressin, neurophysin II, proBNP, and fragments thereof including BNP and N-terminal proBNP (NTproBNP).

[0349] In a preferred embodiment, the additional biomarker is a "renal function marker" that is related to renal function or the diagnosis and / or prognosis of renal disease, including acute kidney injury (AKI) or chronic kidney disease (CKD), such as creatinine, serum creatinine, urea, uric acid, cystatin C, beta-trace protein (BTB), inulin, iohexol, radioactive markers, proteinuria, urinary albumin, kidney injury marker-1 (KIM-1), neutrophil gelatinase-associated lipocalin (NGAL). , interleukin-18, liver-type fatty acid binding protein (L-FABP), asymmetric dimethylarginine (ADMA), lactate dehydrogenase (LDH), glutathione-S-transferase (GST), N-acetylglucosaminidase (NAG), alanine aminopeptidase (AAP), gamma-glutamyltransferase (GGT), tissue inhibitor of metalloproteinase-2 (TIMP-2) and / or insulin-like growth factor binding protein 7 (IGFBPT).

[0350] For further reviews regarding preferred biomarkers related to renal function, the skilled artisan can also refer to published reviews such as Krstic et al. 2016 or Gowda et al. 2010.

[0351] As used herein, the term "sample" refers to a biological sample obtained or isolated from a patient or subject. As used herein, "sample" may refer to a sample of bodily fluid or tissue obtained for the purpose of diagnosis, prognosis, therapy guidance, stratification, monitoring, or control or evaluation of a subject of interest, such as a patient. Preferably, the sample herein is a sample of bodily fluid, such as blood, serum, plasma, cerebrospinal fluid, urine, saliva, sputum, pleural effusion, cells, cell extracts, tissue samples, tissue biopsies, stool samples, etc. In particular, the sample is blood, plasma, serum, or urine.

[0352] The embodiment of the present invention refers to the isolation of a first sample and a second sample, optionally a "third sample", a "fourth sample", etc. In the context of the method of the present invention, the terms "first" and "second sample", as well as a possible "third sample" or "fourth sample", etc. relate to the relative determination of the chronological order of the isolation of the samples used in the method of the present invention. When the terms first and second samples are used in identifying the method, these samples are not to be considered as an absolute determination of the number of samples taken. Thus, additional samples may be isolated from the patient before, during or after the isolation of the first and / or second sample, or between the first or second samples, and these additional samples may or may not be used in the method of the present invention. Thus, the first sample may be considered as any sample obtained previously. The second sample may be considered as any further or subsequent sample.

[0353] "Plasma" in the context of the present invention is the substantially cell-free supernatant of blood containing anticoagulants obtained after centrifugation. Examples of anticoagulants include calcium ion-binding compounds such as EDTA or citrate, and thrombin inhibitors such as heparate or hirudin. Cell-free plasma can be obtained by centrifuging anticoagulated blood (e.g., citrated, EDTA or heparinized blood) at, for example, 2000-3000 g for at least 15 minutes.

[0354] "Serum" in the context of the present invention is the liquid fraction of whole blood that is collected after the blood is allowed to clot. When the clotted blood (clot) is centrifuged, serum can be obtained as the supernatant.

[0355] As used herein, "urine" is the liquid product of the body secreted by the kidneys and excreted through the urethra through a process called urination (or micturition).

[0356] According to the present invention, proADM, proBNP and / or proANP and / or optionally other markers or clinical scores are used as markers for therapy guidance, therapy stratification and / or therapy control of patients diagnosed with cardiovascular disease and optionally for the prognosis, diagnosis, risk assessment and risk stratification of adverse events in the health of patients diagnosed with cardiovascular disease, preferably heart failure.

[0357] A person skilled in the art can obtain or develop means for identifying, measuring, determining and / or quantifying any one of the above-mentioned proADM, proBNP and / or proANP molecules, or fragments or variants thereof, as well as other markers of the present invention according to standard molecular biological practices.

[0358] The level of proADM or a fragment thereof, as well as the level of other markers of the present invention, can be determined by any assay that reliably determines the concentration of the marker. In particular, mass spectrometry (MS) and / or immunoassays can be used as illustrated in the accompanying examples. As used herein, an immunoassay is a biochemical test that measures the presence or concentration of a macromolecule / polypeptide in a solution through the use of an antibody or an antibody binding fragment or an immunoglobulin.

[0359] The method of determining proADM, proBNP and / or proANP or other markers used in the context of the present invention is contemplated in the present invention.As an example, a method selected from the group consisting of mass spectrometry (MS), luminescence immunoassay (LIA), radioimmunoassay (RIA), chemiluminescence and fluorescence immunoassay, enzyme immunoassay (EIA), enzyme-linked immunoassay (ELISA), luminescence-based bead array, magnetic bead-based array, protein microarray assay, rapid test formats such as real-time immunochromatography strip test, rare earth cryptate assay, and automated system / analyzer can be used.

[0360] The determination of proADM, proBNP and / or proANP and any other markers based on antibody recognition is a preferred embodiment of the present invention. As used herein, the term "antibody" refers to immunoglobulin molecules and immunologically active parts of immunoglobulin (Ig) molecules, i.e. molecules that contain an antigen-binding site that specifically binds (immunoreacts with) an antigen. According to the present invention, antibodies may be monoclonal as well as polyclonal antibodies. In particular, antibodies that specifically bind at least proADM, proBNP and / or proANP or fragments thereof are used.

[0361] An antibody is considered specific if its affinity for a molecule of interest, such as proADM, proBNP and / or proANP, or a fragment thereof, is at least 50 times higher, preferably 100 times higher, most preferably at least 1000 times higher, than for other molecules contained in the sample containing the molecule of interest. How to develop and select an antibody with a given specificity is well known in the art. In the context of the present invention, monoclonal antibodies are preferred. The antibody or antibody binding fragment specifically binds to a marker or a fragment thereof as defined herein. In particular, the antibody or antibody binding fragment binds to a peptide of proADM, proBNP and / or proANP as defined herein. Thus, the peptide as defined herein may also be the epitope to which the antibody specifically binds. Furthermore, antibodies or antibody binding fragments are used that specifically bind to ADM or proADM, in particular to MRproADM, to proBNP, in particular to NTproBNP, and / or to proANP, in particular to MRproANP.

[0362] Furthermore, the methods and kits of the present invention use antibodies or antibody-binding fragments that specifically bind to proADM, proBNP, and / or proANP, or fragments thereof, and optionally other markers of the present invention.Exemplary immunoassays may be luminescence immunoassays (LIA), radioimmunoassays (RIA), chemiluminescence and fluorescence immunoassays, enzyme immunoassays (EIA), enzyme-linked immunoassays (ELISA), luminescence-based bead arrays, magnetic bead-based arrays, protein microarray assays, rapid test formats, rare earth cryptate assays.Furthermore, assays suitable for point-of-care testing and rapid test formats, such as immunochromatographic strip tests, may be used.Automated immunoassays, such as KRYPTOR assays, are also contemplated.

[0363] Alternatively, instead of antibodies, other capture molecules or molecular scaffolds that specifically and / or selectively recognize proADM, proBNP, and / or proANP may be included within the scope of the present invention. As used herein, the term "capture molecule" or "molecular scaffold" includes molecules that can be used to bind target molecules or molecules of interest, i.e. analytes (e.g., proADM, proADM, MRproADM, proBNP, NTproBNP, proANP, and / or MRproANP) from a sample. Thus, the capture molecule must be appropriately shaped both spatially and with respect to surface features such as surface charge, hydrophobicity, hydrophilicity, presence or absence of Lewis donors and / or acceptors, to specifically bind to the target molecule or molecule of interest. Thus, the binding may be mediated, for example, by ionic, van der Waals forces, pi-pi, sigma-pi, hydrophobic or hydrogen bond interactions, or a combination of two or more of the aforementioned interactions or covalent interactions between the capture molecule or molecular scaffold and the target molecule or molecule of interest. In the context of the present invention, the capture molecule or molecular scaffold may be selected from the group consisting of, for example, nucleic acid molecules, carbohydrate molecules, PNA molecules, proteins, peptides, and glycoproteins. The capture molecule or molecular scaffold may include, for example, aptamers, DAR pins (Designed Ankyrin Repeat Proteins), affimers, and the like.

[0364] In certain aspects of the invention, the method comprises the steps of: a) The sample i. a first antibody or an antigen-binding fragment or derivative thereof specific for a first epitope of proADM, proBNP and / or proANP; ii. contacting with a second antibody, or an antigen-binding fragment or derivative thereof, specific for a second epitope of proADM, proBNP and / or proANP; b) detecting binding of the two antibodies or antigen-binding fragments or derivatives thereof to proADM, proBNP and / or proANP.

[0365] Preferably, one antibody is labeled and the other antibody is bound to a solid phase or can be selectively bound to a solid phase. In a particularly preferred embodiment of the assay, one of the antibodies is labeled while the other antibody is either bound to a solid phase or can be selectively bound to a solid phase. The first and second antibodies can be present dispersed in a liquid reaction mixture, a first label component that is part of a labeling system based on fluorescence or chemiluminescence quenching or amplification binds to the first antibody and a second label component of the labeling system binds to the second antibody, thereby generating a measurable signal that allows the detection of the resulting sandwich complex in the measurement solution after binding of both antibodies to be detected to proADM, proBNP and / or proANP or fragments thereof. The labeling system can comprise rare earth cryptates or chelates in combination with fluorescent or chemiluminescent dyes, especially of the cyanine type.

[0366] In a preferred embodiment, the method is carried out as a heterogeneous sandwich immunoassay, in which one of the antibodies is immobilized on any selected solid phase, for example on the wall of a coated test tube (for example a polystyrol test tube; coated tube; CT), or on a microtiter plate, for example made of polystyrol, or on particles, for example magnetic particles, whereby the other antibody bears a group that resembles a detectable label or allows selective binding to a label and serves for the detection of the sandwich structure formed. A temporary delay or subsequent immobilization with a suitable solid phase is also possible.

[0367] The method according to the invention can further be embodied as a homogeneous method, in which the sandwich complex formed by the antibody / antibodies to be detected and the marker, proADM, proBNP and / or proANP or fragments thereof, is detected while remaining suspended in the liquid phase. In this case, when two antibodies are used, it is preferred that both antibodies are labeled as part of the detection system, which results in the generation of a signal or the induction of a signal when both antibodies are integrated into a single sandwich. Such a technique should be embodied in particular as a fluorescence enhancement or fluorescence quenching detection method. Particularly preferred embodiments relate to the use of detection reagents used in pairs, such as, for example, those described in US4882733, EP0180492 or EP0539477 and the prior art cited therein. In this way, measurements are possible in which only reaction products containing both labeled components directly in a single immune complex in the reaction mixture are detected. For example, such techniques are provided under the trade names TRACE® (Time Resolved Amplified Cryptate Emission), or KRYPTOR®, which implement the teachings of the above-cited applications. Therefore, in a particularly preferred embodiment, a diagnostic device is used to carry out the method provided herein.For example, the level of proADM, proBNP and / or proANP or its fragments, and / or the level of any additional marker of the method provided herein is determined.In a particularly preferred embodiment, the diagnostic device is KRYPTOR® or related automated system.

[0368] The level of the markers of the present invention, such as proADM, proBNP and / or proANP or fragments thereof, or other markers, can also be determined by methods based on mass spectrometry (MS). Such methods may include detecting the presence, amount or concentration of one or more modified or unmodified fragment peptides, such as proADM, proBNP and / or proANP, in the biological sample or, for example, in a protein digest (e.g., a tryptic digest) from the sample, optionally separating the sample using a chromatographic method, and subjecting the prepared and optionally separated sample to MS analysis. For example, selected reaction monitoring (SRM), multiple reaction monitoring (MRM) or parallel reaction monitoring (PRM) mass spectrometry can be used for MS analysis, particularly to determine the amount of proADM, proBNP and / or proANP or fragments thereof.

[0369] As used herein, the term "mass spectrometry" or "MS" refers to analytical techniques for identifying compounds by their mass. To improve the mass resolution and mass determination capabilities of mass spectrometry, samples may be treated prior to MS analysis. Thus, the present invention relates to MS detection methods, methods relating to sample preparation and / or chromatographic methods, preferably liquid chromatography (LC), more preferably high performance liquid chromatography (HPLC) or ultra-high performance liquid chromatography (UHPLC), that can be combined with immunoenrichment techniques. Sample preparation methods include techniques for dissolution, fractionation, digestion of the sample into peptides, depletion, concentration, dialysis, desalting, alkylation, and / or peptide reduction, although these steps are optional. Selective detection of analyte ions can be performed using tandem mass spectrometry (MS / MS). Tandem mass spectrometry is characterized by a mass selection step (as used herein, the term "mass selection" refers to the isolation of ions with a specific m / z or a narrow range of m / z), followed by fragmentation of the selected ions and mass analysis of the resulting product (fragment) ions.

[0370] A person skilled in the art knows how to quantify the levels of a marker in a sample by mass spectrometry. For example, relative quantification "rSRM" or absolute quantification can be used as described above.

[0371] Furthermore, levels (including reference levels) can be determined by mass spectrometry-based methods, such as methods that determine relative quantification or methods that determine absolute quantification of the protein of interest or a fragment thereof.

[0372] Relative quantification "rSRM" can be achieved by: 1. Determine the increased or decreased presence of a target protein by comparing the SRM (selected reaction monitoring) signature peak area from a given target fragment peptide detected in a sample with the same SRM signature peak area of ​​the target fragment peptide in at least a second, third, fourth, or more biological samples.

[0373] 2. Determine the increased or decreased presence of a target protein by comparing the SRM signature peak area from a given target peptide detected in a sample with the SRM signature peak area resulting from fragment peptides from other proteins in other samples derived from different separate biological sources, and the SRM signature peak area comparison between the two samples for peptide fragments is normalized, for example, to the amount of protein analyzed in each sample.

[0374] 3. To normalize changes in the levels of histone proteins to the levels of other proteins that do not change their expression levels under various cellular conditions, determine the increased or decreased presence of the target protein by comparing the SRM signature peak area for a given target peptide with the SRM signature peak areas from other fragment peptides derived from different proteins within the same biological sample.

[0375] 4. These assays can be applied to both unmodified fragment peptides and modified fragment peptides of target proteins, where the modifications include phosphorylation and / or glycosylation, acetylation, methylation (mono-, di-, tri-), citrullination, ubiquitination. The relative levels of modified peptides are determined in the same way as the relative amounts of unmodified peptides are determined.

[0376] Absolute quantification of a given peptide can be achieved by: 1. Compare the SRM / MRM signature peak area for a given fragment peptide from a target protein in an individual biological sample with the SRM / MRM signature peak area of ​​an internal fragment peptide standard spiked into a protein lysate from the biological sample. The internal standard can be a labeled synthetic version of the fragment peptide from the target protein being investigated or a labeled recombinant protein. This standard can be spiked into the sample in a known amount before (required for recombinant proteins) or after digestion, and the SRM / MRM signature peak area can be determined separately for both the internal fragment peptide standard and the native fragment peptide in the biological sample, followed by a comparison of both peak areas. This can be applied to unmodified and modified fragment peptides, where the modification is phosphorylation and / or glycosylation, acetylation, methylation (e.g. mono-, di-, or trimethylation), citrullination, ubiquitination, where the absolute level of the modified peptide can be determined in the same way as the absolute level of the unmodified peptide is determined.

[0377] 2. Peptides can also be quantified using external calibration curves. The normal curve approach uses a fixed amount of a heavy peptide as an internal standard and various amounts of a light synthetic peptide spiked into the sample. A representative matrix similar to that of the test sample needs to be used to construct a standard curve to account for matrix effects. Besides, the inverse curve method avoids the problem of endogenous analytes in the matrix, where a fixed amount of a light peptide is spiked onto the endogenous analyte to create an internal standard, and various amounts of a heavy peptide are spiked to create a set of concentration standards. The test samples to be compared with either the normal curve or the inverse curve are spiked with the same amount of standard peptide as the internal standard spiked into the matrix used to create the calibration curve.

[0378] The present invention further relates to kits, the use of the kits and methods in which such kits are used. The present invention relates to kits for carrying out the methods provided herein above and below. The definitions provided herein, such as those provided for the methods, also apply to the kits of the present invention. In particular, the present invention relates to kits for therapy guidance, stratification and / or monitoring of remote patient management for patients diagnosed with cardiovascular disease, the kit comprising: - a detection reagent for determining at least one biomarker or fragment(s) thereof selected from the group consisting of proADM, proBNP and proANP in a sample from a patient; reference data, such as reference values ​​for determining whether the level of at least one biomarker selected from the group consisting of proADM, proBNP and proANP indicates the prescription or non-prescription of remote patient management, in particular for a low benefit level of the at least one biomarker and for a high benefit level of the at least one biomarker, said reference data being preferably stored on a computer readable medium and / or used in the form of a computer executable code configured to compare the determined at least one biomarker with the reference value; Optionally, a detection reagent for determining the level of at least one additional biomarker or a fragment thereof in a sample from a patient and / or at least one clinical parameter, preferably age, weight, body mass index, sex, ethnic background, blood creatinine, left ventricular ejection fraction (LVEF), right ventricular ejection fraction (LVEF), NYHA classification, MAGGIC heart failure risk score, status of medical treatment, blood pressure (systolic / diastolic), heart rate, cardiac rhythm according to electrocardiogram (ECG), peripheral blood oxygen content (SpO2), self-assessed health status (scale), or renal function, preferably creatinine clearance rate and / or glomerular filtration rate (GF R), and reference data, such as reference values ​​for deciding whether the level of the at least one additional biomarker or fragment thereof and / or the at least one clinical parameter is indicative for prescribing or not prescribing remote patient management, said reference data being preferably stored on a computer readable medium and / or used in the form of a computer executable code configured for comparing the determined level of said at least one biomarker or fragment(s) thereof and / or said at least one clinical parameter with the reference value.

[0379] As used herein, "reference data" includes reference level(s) of proADM, proBNP and / or proANP, and optionally further markers as described herein. The levels of proADM, proBNP and / or proANP in a subject's sample can be compared with the reference levels contained in the reference data of the kit. The reference levels are described herein above and are also illustrated in the accompanying examples. The reference data can also include a reference sample to which the levels of proADM, proBNP and / or proANP, and optionally further markers, are compared. The reference data can also include instructions on how to use the kit of the present invention.

[0380] In addition, the kit may include items useful for obtaining a sample, such as a blood sample, for example, the kit may include a container, the container including a device for attaching the container to a cannula or syringe, e.g., a syringe suitable for blood isolation, that exhibits an internal pressure below atmospheric pressure suitable for drawing a predetermined amount of sample into the container, and / or additionally includes a filtration system containing detergents, chaotropic salts, ribonuclease inhibitors, chelating agents such as guanidinium isothiocyanate, guanidinium hydrochloride, sodium dodecyl sulfate, polyoxyethylene sorbitan monolaurate, RNAse inhibitor proteins, and mixtures thereof, and / or nitrocellulose, silica matrix, ferromagnetic spheres, a cup retrieve spill over, trehalose, fructose, lactose, mannose, poly-ethylene-glycol, glycerol, EDTA, TRIS, limonene, xylene, benzoyl, phenol, mineral oil, aniline, pyrrole, citrate, and mixtures thereof.

[0381] As used herein, a "detection reagent" or the like is a reagent suitable for determining the marker(s) described herein, e.g., proADM, proBNP and / or proANP. Such an exemplary detection reagent is, for example, a ligand, e.g., an antibody or a fragment thereof, that specifically binds to a peptide or epitope of a marker(s) described herein. Such a ligand can be used in an immunoassay as described above. Additional reagents used in an immunoassay to determine the level of a marker(s) can also be included in the kit and are considered as detection reagents herein. A detection reagent can also relate to a reagent used to detect a marker or a fragment thereof by a MS-based method. Thus, such a detection reagent can also be a reagent used to prepare a sample for MS analysis, e.g., an enzyme, a chemical, a buffer, etc. A mass spectrometer can also be considered as a detection reagent. A detection reagent according to the present invention can also be, for example, a calibration solution(s) that can be used to determine and compare the level of a marker(s).

[0382] The terms "cut-off value", "reference value" or "threshold value" can be used interchangeably, and the specific values ​​described herein may be different for other assays if they are "calibrated" differently from the assay system used in the present invention. The cut-off value or reference value shall therefore be applied to such differently calibrated assays accordingly, taking into account the difference in calibration. One possibility to quantify the difference in calibration is a method comparison analysis (correlation) using the method of the assay in question (e.g. biomarker x assay) with the respective biomarker assay used in the present invention (e.g. assay name x) by measuring the respective biomarker (e.g. biomarker x) in a sample. Another possibility is to determine with the assay in question, and compare the results with the median biomarker levels described in the literature (e.g. cited normal populations), assuming that this test has sufficient analytical sensitivity, the median biomarker levels of a representative normal population, and recalculate the calibration based on the difference obtained by this comparison.

[0383] The sensitivity and specificity of diagnostic and / or prognostic tests do not only depend on the analytical "quality" of the test, but they also depend on the definition of what constitutes an abnormal result. In practice, receiver operating characteristic curves (ROC curves) are typically calculated by plotting the value of a variable against its relative frequency in a "normal" population (i.e., apparently healthy individuals without infection) and a "disease" population, e.g., subjects with an infection. For any particular marker (such as proADM, proBNP, and / or proANP), the distributions of marker levels for subjects with and without disease / condition will likely overlap. Under such conditions, the test will not perfectly distinguish normal from disease with 100% accuracy, and the area of ​​overlap may indicate where the test cannot distinguish normal from disease. A threshold is selected below which the test is considered abnormal and above which the test is considered normal, or below or above which the test indicates a particular condition, e.g., an infection or a cardiovascular or cerebrovascular event. The area under the ROC curve is a measure of the probability that the perceived measurement will allow correct identification of a condition. Even when the test results do not necessarily give an exact number, ROC curves can be used. As long as the results can be ranked, ROC curves can be created. For example, the results of a test on a "disease" sample can be ranked according to degree (e.g., 1=low, 2=normal, and 3=high). This ranking can be correlated with the results of a "normal" population, and a ROC curve can be created. These methods are well known in the art, see, for example, Hanley et al. 1982.Radiology 143:29-36. Preferably, the threshold value is selected to provide a ROC curve area greater than about 0.5, more preferably greater than about 0.7, even more preferably greater than about 0.8, even more preferably greater than about 0.85, and most preferably greater than about 0.9. The term "about" in this context refers to + / - 5% of a given measurement.

[0384] The horizontal axis of the ROC curve represents (1-specificity), which increases with the rate of false positives. The vertical axis of the curve represents sensitivity, which increases with the true positive rate. Thus, for a particular cutoff selected, the value of (1-specificity) can be determined, and the corresponding sensitivity can be obtained. The area under the ROC curve is a measure of the probability that the measured marker level will allow for correct identification of a disease or condition. Thus, the area under the ROC curve can be used to determine the validity of a test.

[0385] Ruling-in identifies a minimum proportion of subjects who actually develop a disease, disorder, or adverse event, and ensures that this true positive group has a sufficiently large proportion of subjects who test positive. Such a test should achieve maximum specificity and maximum positive predictive value (PPV). In this specification, ruling-in preferably refers to showing that remote patient management would be beneficial and should be prescribed.

[0386] Ruling-out identifies a minimum proportion of subjects who are sure to not develop a disease, disorder, or adverse event, and ensures that a sufficiently small proportion of subjects who test negative develop the disease (false negatives). Thus, such a test must reach maximum sensitivity and maximum negative predictive value (NPV). In this specification, ruling-out preferably refers to indicating that remote patient management is not beneficial and should not be prescribed.

[0387] The method of the present invention may be partially computer-implemented. For example, the step of comparing the level of the detected marker, such as proADM, proBNP and / or proANP or fragments thereof, with the reference level may be implemented in a computer system. In the computer system, the determined level of the marker(s) may be combined with other marker levels and / or parameters of the subject to calculate a score indicated for diagnosis, prognosis, risk assessment and / or risk stratification. For example, the determined values ​​may be input into the computer system (either manually by a medical professional or automatically from the device(s) where the respective marker level(s) was determined). The computer system may be directly at the point-of-care (e.g., primary care, ICU, or ED) or may be at a remote location connected via a computer network (e.g., via the Internet or a specialized medical cloud system, optionally in combination with other IT systems or platforms such as a hospital information system (HIS)). Typically, the computer system will store values ​​(e.g., marker levels or parameters such as age, blood pressure, weight, sex, or clinical scoring systems such as SOFA, qSOFA, BMI, etc.) in a computer readable medium and calculate a score based on predefined and / or prestored reference levels or values. The resulting score will be displayed and / or printed for a user (typically a medical professional such as a physician). Alternatively or additionally, an associated prognosis, diagnosis, evaluation, therapy guidance, patient management guidance or stratification will be displayed and / or printed for a user (typically a medical professional such as a physician).

[0388] In one embodiment of the present invention, a software system can be used in which a machine learning algorithm is evident to identify hospitalized patients at risk for sepsis, severe sepsis and septic shock, preferably using data from an electronic health record (EHR). The machine learning approach can be trained with a random forest classifier using EHR data from the patient (labs, biomarker expression, vitals, demographics, etc.). Machine learning is a type of artificial intelligence that gives computers the ability to learn complex patterns of data without being explicitly programmed, unlike simple rule-based systems. Previous studies have used electronic health record data to trigger alerts to detect general clinical deterioration. In one embodiment of the present invention, the processing of proADM, proBNP and / or proANP levels can be incorporated into appropriate software for comparison with existing data sets, for example, proADM, proBNP and / or proANP levels can also be processed with machine learning software to aid in the diagnosis or prognosis of the occurrence of an adverse event or to aid in the decision of whether to prescribe remote patient management.

[0389] As used herein, the terms "comprising" and "including" or grammatical variations thereof should be interpreted as specifying stated features, integers, steps, or components, but not excluding the addition of one or more additional features, integers, steps, components, or groups thereof. This term encompasses the terms "consisting of" and "consisting essentially of."

[0390] Thus, the terms "comprise" / "include" / "have" mean that any additional components (or similar features, integers, steps, etc.) may be present. The term "consisting of" means that no additional components (or similar features, integers, steps, etc.) are present.

[0391] The term "consisting essentially of" or grammatical variations thereof, as used herein, should be construed to identify the stated features, integers, steps, or components, but not to preclude the addition of one or more additional features, integers, steps, components, or groups thereof, but only if those additional features, integers, steps, components, or groups thereof do not materially alter the basic and novel characteristics of the composition, device, or method being claimed.

[0392] Thus, the term "consisting essentially of" means that certain additional components (or similar features, integers, steps, etc.) may be present, i.e., that do not substantially affect the essential characteristics of the composition, device, or method. In other words, the term "consisting essentially of" (which may be used interchangeably herein with the term "substantially comprising") allows for the presence of other components in the composition, device, or method in addition to the essential components (or similar features), provided that the essential characteristics of the device or method are not substantially affected by the presence of the other components.

[0393] The term "method" refers to methods, means, techniques, and procedures for accomplishing a given task, and includes, but is not limited to, those methods, means, techniques, and procedures known to those skilled in the chemical, biological, and biophysical arts or readily developed from known methods, means, techniques, and procedures by those skilled in the chemical, biological, and biophysical arts.

[0394] The present invention will now be further described by reference to the following non-limiting examples, which illustrate non-limiting working embodiments that are presented to further illustrate the invention. EXAMPLES

[0395] Example of the method: Study design and subjects: This study is part of the Telemedicine Interventional Management in Heart Failure II (TIM-HF2), a randomized controlled trial investigating the impact of telemedicine on unplanned cardiovascular hospitalizations and mortality in heart failure. Details of the study's methods and some of its results are published in Koehler et al. 2018a and Koehler et al. 2018b, which are incorporated herein by reference.

[0396] The TIM-HF2 study was a prospective, randomized, controlled, parallel-group, unmasked (with randomization concealment), multicenter study with pragmatic elements implemented for data collection (ClinicalTrials.gov Identifier: NCT01878630). The study took place in Germany, and patients were recruited from 200 university, regional, and local hospitals, cardiologists' and general practitioner (GP) practices. In total, 113 study sites located in 14 metropolitan areas and / or medical universities with more than 200,000 inhabitants (i.e., Berlin, Dresden, Hamburg, Stuttgart, Frankfurt am Main, Leipzig, Hanover) and 11 rural regions of Germany (i.e., Brandenburg, Bavaria, Thuringia, Saxony, Saxony-Anhalt, Hesse, Baden-Württemberg, Lower Saxony, Mecklenburg-West Pomerania, North Rhine-Westphalia, Saarland) were included. Forty-three study sites were hospitals, 10 were university hospitals, and 60 were local cardiologist practices. In addition, 87 general practitioners (GPs) contributed to the study by screening and following up patients.

[0397] Patients were hospitalized for worsening heart failure within 12 months prior to randomization, belonged to New York Heart Association functional class II or III, and had a left ventricular ejection fraction ≤45% (or were being treated with oral diuretics if >45%). Patients were excluded if they had major depression (i.e., PHQ-9 score >9), were on hemodialysis, or had been hospitalized for any reason within 7 days prior to randomization. In addition, patients with left ventricular assist devices or who had undergone coronary revascularization or cardiac resynchronization therapy implantation within 28 days prior to randomization were excluded, as were patients planning coronary revascularization, transcatheter aortic valve implantation, mitral clip implantation, or cardiac resynchronization therapy implantation 3 months after randomization. Inclusion and exclusion criteria are summarized in Table 1.

[0398] The TIM-HF2 trial was designed, conducted, and overseen by an independent steering committee. The report was prepared and submitted for publication by the steering committee. An independent data safety monitoring committee reviewed safety data on an ongoing basis. A clinical endpoint committee, masked to study arm allocation, adjudicated all deaths and hospitalizations using criteria prospectively defined in the clinical endpoint committee charter. Adjudicated data were used for outcomes regarding hospitalization and death. 19 The trial conformed to good clinical practice in accordance with the Declaration of Helsinki and applicable legislation in Germany. Written approval from the appropriate ethical committee was obtained.

[0399] Patients provided written informed consent and gave permission for the telemedicine center to contact their health insurance companies to cross-reference hospitalizations reported by the investigators with those recorded in their health insurance records. This process was approved by the German Federal Social Insurance Agency and was performed for patients in both study groups.

[0400] Randomization and Masking Potentially eligible patients were screened, and those who agreed to participate and provided written informed consent were screened and baseline measurements and assessments were performed. Eligible and willing patients were randomly assigned (1:1) to either remote patient management plus usual care (remote patient management group) or usual care only (usual care group) using a secure web-based system. To ensure balance of important clinical covariates between the two study arms, Pocock's minimization algorithm with 10% residual randomness was used. Randomization was concealed, but in this open trial neither participants nor investigators were masked to group allocation.

[0401] Procedures and Remote Patient Management The remote patient management intervention consisted of: daily transmission of weight, systolic and diastolic blood pressure, heart rate, cardiac rhythm analysis, peripheral capillary oxygen saturation (SpO2), and self-assessed health status (scale range 1–5) to the telehealth centre, definition of patient risk category using baseline and follow-up visit biomarker data in combination with the daily transmitted data, patient education, and collaboration between the telehealth centre and the patient's GP and cardiologist.

[0402] Home remote monitoring system The telemonitoring system, installed in patients' homes within 7 days after randomization, was a multicomponent system.

[0403] The system used is based on a Bluetooth® system that uses a digital tablet (Physio-Gate® PG1000, GETEMED Medizin- und Informationstechnik AG) as the central structural element, transmitting vital measurements from the patient's home to the TMC at the Charité - Universitatsmedizin Berlin. Four measurement devices are part of the system: a three-channel ECG device (PhysioMem® PM1000 GETEMED Medizin- und Informationstechnik AG) for collecting 2-minute, or streaming ECG measurements, a device for collecting peripheral capillary oxygen saturation (SpO2; Masimo Signal Extraction Technology (SET®)), a system for collecting blood pressure (UA767PBT, A&D Ltd.) and a weight scale (Seca 861, seca GmbH&Co KG). Each device is equipped with a Bluetooth® chip and is connected to a digital tablet. The TMC software used is "Fontane" (eHealth Connect 2.0, T-Systems International GmbH), developed specifically for use in the TIM-HF2 trial. Fontane's main innovation is its new self-adaptive TMC middleware, which consists of three main components: Algorithms to identify critical or missing values ​​in the transmitted patient data, thus allowing immediate identification of patients who require immediate (medical) attention; Telecommunications software for direct communication between TMC staff, patients, GPs and local cardiologists, as well as All relevant medical information (e.g. medication regimens, Electronic health records (reports of previous hospitalizations, laboratory data).

[0404] The patients were also provided with a mobile phone to be used to contact the Telemedicine Centre directly in case of an emergency. The mobile phone (DORO Easy 510 / Doro HandlePlus 334gsm, Doro AB) allowed a direct call to the TMC in case of an emergency. In such a situation it is also possible to start a live ECG stream using the ECG device. Using the mobile network, the tablet automatically transmits the patient data in an encrypted manner (GSM encryption via a VPN tunnel) to a central server at the TMC in Berlin, provided by project partner Deutsche Telekom AG. The combination of the measurements and the personal data using an individual information code is performed exclusively on a server at Charité - Universitatsmedizin Berlin. To ensure patient safety, the average transmission time to get the data to the TMC must be <90 seconds. The availability of the mobile network connection is provided by the provider Deutsche Telekom AG. The complete data collection process, transmission and processing are carried out in strict compliance with state-of-the-art confidentiality and technical standards agreed and certified by the relevant Data Protection Officer. A unique device identifier is embedded in every data transmission to authenticate each individual measurement. Service level agreements with technical providers are concluded for first and second level support and corresponding service and escalation concepts.

[0405] Remote Patient Management During the installation process of the remote monitoring system, a certified nurse provided patient training on the system and initiated a heart failure patient education program, the latter continued monthly by structured telephone interviews with patients. The monthly telephone interviews were an integral part of the remote patient management intervention. In combination with daily data transmission to the telemedicine center, the patient's clinical and symptomatic status and concomitant medications were evaluated, in addition to compliance with the remote patient management intervention and other social and technical issues discussed between the patient and the telemedicine center nurse. Patient data were transmitted from home to the center using a wireless system with a digital tablet. This was done using a mobile phone network (secured via a virtual private network tunnel), and transmission of patient data was set at a fixed time each day.

[0406] The telemedicine centers provided physician-led medical support and patient management 24 hours a day, Monday through Sunday, throughout the study period, using the CE-marked telemedicine analysis software, the Fontane system (T-Systems International GmbH, Frankfurt, Germany). Algorithms were programmed and implemented in the system to guide patient management and enable telemedicine center physicians to take rapid action (e.g., change concomitant medications, initiate outpatient evaluation with a home physician, or admit the patient) and prioritize high-risk patients.

[0407] Patients were classified as low or high risk using a combination of mid-region pro-adrenomedullin (MRproADM) values ​​and patient-submitted data.

[0408] At baseline and each follow-up visit, biomarkers were obtained and analyzed by an independent laboratory. Results were sent to the CTC and TMC. According to defined cut-off values ​​of mid-region pro-adrenomedullin (MRproADM), patients were risk-classified as follows: low-risk patients (MRproADM ≤ 1.2 nmol / L) and high-risk patients (MRproADM > 1.2 nmol / L). High-risk patients were followed primarily by TMC physicians ("doctor care"), whereas low-risk patients were followed by registered TMC nurses ("nurse care"). Risk classification was re-evaluated every 3 months using MRproADM results obtained at each follow-up visit.

[0409] Prioritization of transmitted data was managed according to the criteria presented below, and doctors and nurses prioritized their workload and work streams so that patients presenting with any of the data cutoff limits were managed with priority. Bradycardia, heart rate <50 bpm Tachycardia, heart rate >100 bpm Ventricular tachycardia New-onset atrial fibrillation oPQ interval > 200 ms oQRS duration ≥ 120 ms QTc interval >460 ms SpO2 < 94% Weight (weight gain >1 kg in 1 day, >2 kg in 3 days) , >2.5 kg in 8 days) o Systolic blood pressure: <90 or >140mmHg, diastolic: <40 or >90mmHg o Self-assessed health status (on a scale from 1-very good to 5-very bad): 1 being about 2 grades lower, or 4 or 5 grades worse

[0410] The Fontane system also enabled direct communication between telemedicine centre staff and the patient, and between the patient's GP and local cardiologist, all of whom were involved in the patient's management. Through the Fontane system, the telemedicine centre created a study-specific electronic patient file that was accessible to both telemedicine centre staff and the patient's care providers.

[0411] Patients in both study arms were followed up for at least 365 and up to 393 days after randomization. All patients were seen by their treating cardiologist at screening and baseline visits, and at the final study visit, the latter of which took place on day 365 after randomization (a 28-day time frame). In between, patient consultations were scheduled at 3, 6, and 9 months and were performed by the patient's GP or local cardiologist. At all visits, data including vital signs and weight were collected on a case report form, and patients were asked about the occurrence of hospitalization since their last study contact. The study flow is shown in Figure 1. The assessments performed at each visit are shown in Table 2.

[0412] To avoid bias in contact information and data collection, given the daily contact with patients in the remote patient management group, a quality control system was implemented to ensure accurate and complete reporting of hospitalizations in both the remote patient management + usual care and usual care groups. This process required the cooperation of patients, investigators, and the patients' respective health insurance companies. Accuracy of data on hospitalizations was confirmed using data from the health insurance companies and cross-checked with hospitalizations reported by the investigators.

[0413] Thus, the RPM intervention consisted, inter alia, of the following components: Daily transmission of weight, blood pressure (systolic / diastolic), heart rate, cardiac rhythm analysis from a 2-minute 3-channel electrocardiogram (ECG), peripheral capillary oxygen saturation (SpO2), and self-assessed health status (scale range 1-5) -Identifying patient risk stratification using biomarker values ​​at baseline and follow-up visits Patient education and Telemedicine Center (TMC) and patient collaboration between GPs and cardiologists (the "doctor-to-doctor telemedicine scenario");

[0414] Patients randomized to the UC group were followed according to current standards (i.e., ESC guidelines for HF management) at the discretion of the treating physician (Ponikowski et al. 2016 ).

[0415] In addition, patients assigned to the RPM arm will receive a structured daily review of their concomitant medications based on the data submitted. With the consent of the study site physician, TMC physicians will optimize the concomitant treatment as needed to achieve the following goals: · Heart rate <75 b.pm in patients with sinus rhythm. · Blood pressure control: systolic <140mmHg and diastolic <90mmHg. · Patients with new-onset atrial fibrillation: Use of anticoagulant therapy as long-term treatment and antiarrhythmic therapy. NYHA class II-IV patients: Encourage the use of mineralocorticoid receptor antagonists when possible. The aim is to prescribe the patient the maximum tolerated dose to achieve these goals and further adapt the diuretic dose in case of weight gain and worsening symptoms.

[0416] The telemedicine team will inform the patient's GP or providing physician by phone, fax or email of any new events or important clinical findings from monthly telephone contacts, contacts with the emergency physician, or interventions made in the patient's treatment as a result of the measured telemedicine key parameters. The TMC should only advise the patient's primary physician, as this has the overall responsibility for driving the patient's medical management.

[0417] Study outcomes The primary outcome was the proportion of days lost due to unplanned cardiovascular hospitalization or death from any cause in patients receiving telemanagement plus usual care during individual patient follow-up compared with patients receiving usual care only.Key secondary outcomes were all-cause mortality and cardiovascular mortality during individual patient follow-up plus 28 days and up to 393 days after the last study visit, the proportion of days lost due to unplanned cardiovascular hospitalization, and the proportion of days lost due to unplanned heart failure hospitalization, change in Minnesota Living with Heart Failure Questionnaire (MLHFQ) global score, and change in N-terminal prohormone brain natriuretic peptide (NTproBNP) and MRproADM between randomization and the last study visit.

[0418] Key secondary outcomes comparing RPM with usual care were: a) All-cause mortality during individual patient follow-up (+28 days up to 393 days from last visit) b) Mortality from any cause during the follow-up period for individual patients (+28 days from last visit up to a maximum of 393 days) c) The proportion of days lost due to unplanned cardiovascular hospitalization during an individual patient's follow-up period. d) Proportion of days lost due to unplanned HF hospitalization during individual patient follow-up e) Change in MLHFQ-Questionnaire global score from baseline to 365 days f) Change in NTproBNP and MRproADM levels between baseline and 365 days

[0419] The following recurrent event analysis was performed. a) Unplanned cardiovascular hospitalizations and cardiovascular mortality. b) Unplanned cardiovascular hospitalizations and all-cause mortality. c) Unplanned HF hospitalization and cardiovascular disease d) Unplanned HF hospitalization and all-cause mortality

[0420] Subgroup analyses will be performed for the primary outcomes to assess the consistency of intervention effects across the following subgroups: · Metropolitan versus rural health care. · Male vs. Female. -Age above / below median. · LVEF ≤ 45% vs LVEF > 45%. NYHA function class I / II vs. III / IV. Did you receive cardiac resynchronization therapy (CRT) at baseline? Yes / No. Did you have an implantable cardioverter defibrillator (ICD) at baseline? Yes / No. · MRproADM ≤ 1.2nmol / L vs. 1.2nmol / L at baseline.

[0421] statistical analysis Data from specific subgroups of the TIM-HF trial were used for sample size calculations. In the patient subgroup reflecting the population intended to be included in the TIM-HF2 trial, 19 days were lost to death from any cause or unplanned cardiovascular hospitalization over 12 months in the usual care group and 12 days were lost in the remote management group, corresponding to a reduction of 38%. With an estimated pooled SD of 48, it was calculated that 750 patients in each group would be needed to detect this difference with 80% power and a two-sided alpha of 5%.

[0422] Analyses were performed using R (version 3.4.4) and Stata (version 14.2). Primary and secondary efficacy analyses were performed on the full analysis population according to the intention-to-treat principle. The full analysis population consisted of all randomized patients who gave consent and started their assigned care.

[0423] Baseline characteristics were summarized as number of patients (%) for categorical variables and mean (SD) for continuous variables; for all baseline incident laboratory tests, median and IQR were used.

[0424] For the primary analysis of the proportion of days lost due to death from any cause or unplanned cardiovascular hospitalization, the proportion of follow-up time lost due to death or unplanned cardiovascular hospitalization was defined as the number of days lost divided by the intended follow-up. For patients who died, we counted the number of days lost between the date of death and the number of days of intended follow-up plus the number of days hospitalized for cardiovascular reasons. For patients who completed the study as planned or who were withdrawn early from follow-up, the proportion of follow-up time was defined as the number of days lost (due to cardiovascular hospitalization) divided by the realized follow-up time (i.e., the date and time of study censoring). As the primary outcome, we used a permutation test to compare the weighted mean of the proportion of days lost between the two groups. The p-value of the two-sided permutation test was calculated as the proportion of permutations with an absolute value of the test statistic at least as large as the observed test statistic when applying the intermediate p correction in the case of equality. In this analysis, 2000 randomly drawn permutations were used.

[0425] Confidence intervals (CIs) were calculated using the method described by Garthwaite (Garthwaite PH. Confidence intervals from randomization tests. Biometrics 1996;52:1387-93), which is based on the Robbins-Monro method. Briefly, this method searches for each endpoint of the CI separately by sequentially updating the estimate, whose step size is governed by the distance between the original test statistic and the test statistic of the permuted data, and the number of steps. Follow-up times are weighted using weighted arithmetic means, and annual means are displayed.

[0426] In brief, the method searches for each endpoint of CI separately by sequentially updating estimates where the magnitude of the steps is governed by the distance between the original test statistic and that of the permuted data, and the number of steps. Follow-up times are weighted using a weighted arithmetic mean, and annual means are displayed.

[0427] All survival analyses were based on time to first event. Cumulative incidence curves for all-cause mortality were constructed according to the Kaplan-Meier method, and differences between curves were examined by the log-rank statistic. For cardiovascular mortality, competing risk analysis was used, taking into account that the event of interest did not occur because another fatal event had occurred previously. Cox proportional hazards regression models were used to estimate (cause-specific) hazard ratios (HRs). Event rates are expressed as the number of events per 100 patients at 1 year of follow-up, taking into account the censoring of follow-up data.

[0428] Sensitivity analyses for the mortality outcome examined the robustness of the results using the largest analysis population of all patients censored at 393 days after randomization, as defined in the statistical analysis plan. Data for the number of hospitalization events were analyzed by negative binomial models. For continuous variables, such as the MLHFQ global score, group mean changes in both study arms at 12 months were compared by ANCOVA models adjusting for baseline values. Biomarker test results were analyzed using logarithmic scales and ANCOVA models.

[0429] Adherence to daily data transmission to the telemedicine center was defined as the number of days from the date of the first data transmission to the telemedicine center to the end of the patient's individual follow-up minus the number of days the patient was hospitalized for any reason. Statistical tests of interaction were performed to evaluate whether the effect of remote patient management on the primary outcome was consistent across prespecified subgroups. Tests of interaction for subgroup analyses were performed by adding the interaction term to the corresponding model.

[0430] Example 1: Benefits of remote patient management for patients with HF Between August 13, 2013 and May 12, 2017, 1571 patients were randomly assigned (796 to usual care in addition to telepatient management and 775 to usual care only, of which 765 were in the telepatient management group and 773 in the usual care group, and were included in the largest analysis population; Figure 2 ). Baseline clinical and laboratory characteristics and cardiovascular medication use were similar between the two groups (see Table 3 ).

[0431] The mean age of all patients was 70 years (SD 10), and 70% were male.

[0432] For patients randomly assigned to receive remote patient management, 743 (97%) were at least 70% compliant with daily transfer of data to the telemedicine center. Furthermore, all patients were contacted within 24 hours of missing data submission. Vital status was known for all patients until each patient's maximum follow-up (i.e., 393 days after randomization).

[0433] Of 765 patients in the telepatient management group, 265 (35%) and of 773 (38%) in the usual care group were hospitalized for unplanned cardiovascular reasons or died. The proportion of days lost due to unplanned cardiovascular hospitalization or death from any cause was statistically reduced in patients assigned to telepatient management (4.88%, 95% CI 4.55-5.23) compared with usual care (6.64%, 95% CI 6.19-7.13; ratio 0.80, 95% CI 0.65-1.00; p=0.0460; Table 2). Patients assigned to telepatient management lost a weighted mean of 17.8 days per year for this outcome compared with 24.2 days per year for patients assigned to usual care.

[0434] All-cause mortality was 7.9 per 100 patients at 1 year follow-up in the telemanagement group and 11.3 per 100 patients at 1 year follow-up in the usual care group (HR 0.70, 95% CI 0.50-0.96, p=0.0280, Table 4, Figure 3). With regard to death from cardiovascular causes, the difference between the telemanagement and usual care groups was not statistically significant (HR 0.67, 95% CI 0.45-1.01, p=0.0560).

[0435] Patients assigned to telemanagement lost fewer days to unplanned hospitalization for worsening heart failure than did patients assigned to usual care (mean 3.8 days per year [95% CI 3.5-4.1] vs. 5.6 days per year [5.2-6.0], respectively). The proportion of days lost to this outcome in the telemanagement and usual care groups was 1.04% (95% CI 0.96-1.11) and 1.53% (1.43-1.64), respectively (proportion 0.80, 95% CI 0.67-0.95; p=0.0070). Sensitivity analyses for all-cause mortality yielded similar results when comparing telemanagement with usual care (proportion 0.74, 95% CI 0.54-1.02; p=0.0633).

[0436] The proportion of days lost due to unplanned cardiovascular hospitalization was 1.71% (95% CI 1.59-1.83) in the remote patient management group and 2.29% (2.13-2.45) in the usual care group (ratio 0.89, 95% CI 0.74-1.07; p=0.208).

[0437] The change from baseline in the Minnesota Heart Failure Questionnaire (MLHFQ) global score at 12 months was not statistically different between the telepatient management and usual care groups (Table 5).

[0438] The results of the subgroup analysis for the primary outcome are shown in Figure 3. There was no effect of prespecified subgroups on the difference between treatment groups in the primary outcome.

[0439] 2251 unplanned hospitalizations were reported and classified by the Clinical Endpoint Committee (Appendix p4). Of these hospitalizations, 262 (14 in the telepatient management group and 248 in the usual care group) were identified during the cross-check validation procedure with health insurance records. 1026078 vital parameters were transmitted to the telemedicine center (median 1421 per patient [range 6-3962]) and Table 6 shows a summary of the data transmitted and the actions taken.

[0440] Example 2: Prognostic power of biomarker decisions on whether or not to prescribe remote patient management For the study population described above and in Koehler et al. 2018a and Koehler et al. 2018b and for the data collected from the TIM-HF II study, further analyses were performed to evaluate the prognostic ability of the biomarkers proADM, proBNP, and / or proANP to predict the benefit of prescribing or not prescribing remote patient management.

[0441] Methods of biomarker analysis As mentioned above, a total of 1538 patients (median age: 73, IQR: 64-78; 70% male) were randomly assigned to either RPM (N=765) or SOC (N=773) in the TIM-HF II study. The terms SOC (standard of care) and usual care group (UC) are used interchangeably herein. Patients attended study visits every 90 days over a 1-year follow-up period. Blood was drawn at baseline and at all visits for biomarker analysis.

[0442] NTproBNP, MR-ANP, and MRproADM were evaluated for (i) their association with the primary endpoint of % of days lost in the original study due to unplanned cardiovascular hospitalization or death from any cause, (ii) their association with the specific endpoints of HF, mortality from any cause or readmission, considered crucial for the selection of RPM patients, and (iii) their predictive performance when used in combination to identify patients who can be safely removed from RPM in the next 90 days (without mortality from any cause or readmission due to HF).

[0443] For the latter, statistical power was obtained by pooling all repeated observations of biomarkers and subsequent 90-day follow-up of all patients under SOC (as expected, no trends beyond repeated measurements were observed). After considering the association between renal failure and biomarker levels by stratification by eGFR (CKD-EPI equation) or GFR based on Cockroft-Gault, we calculated biomarker cut-offs for safe exclusion (100%, 99%, 95% sensitivity) and their hypothetical performance in terms of saved RPM effort (proportion of patients excluded from RPM) as well as in terms of RPM efficacy for included patients (reduction in risk of events, number of NNT needed to treat). The unique dataset available for RPM patients allows further evaluation of the exclusion algorithm using detailed data on emergencies, phone calls, and medications.

[0444] Results of biomarker analysis of MRproADM, proBNP or proANP within 90 days from baseline For the endpoint of "death from any cause or unplanned CV hospitalization due to acute decompensation within 90 days from baseline," ROC analysis and analysis of performance biomarker cutoffs in UC slowing were performed as benchmarks.

[0445] Figures 5-7 show the ROC curves for death or acute decompensation within 90 days of first measurement (for patients with usual care). The area under the curve (AUC) for MRproADM is 0.755, for NTproBNP is 0.736, and for MRproANP is 0.69. Thus, all biomarkers show high prognostic potential, which may be utilized for therapy guidance, monitoring, or stratification of remote patient management, as described herein.

[0446] At the baseline visit in TIM-HF2, patients with higher levels of NTproBNP or MRproADM had a higher rate of lost days and were more likely to experience adverse events during the study year, indicating a potential benefit from RPM allocation.

[0447] Tables 7-9 summarize the cut-off analysis for the biomarkers proADM, proBNP, and proANP. In particular, preferred cut-offs for establishing low and high benefit levels for prescribing or not prescribing are those related to cut-offs that reach 100% or 95% sensitivity.

[0448] As shown in Table 7, for MRproADM, a cutoff value of 0.75 nmol / L provides a sensitivity of 100% and 0.86 nmol / L provides a sensitivity of 95%.

[0449] As shown in Table 8, for NTproBNP, a cutoff value of 237.6 pg / mL results in 100% sensitivity, and 609.4 pg / mL results in 95% sensitivity.

[0450] As shown in Table 9, for MRproANP, a cutoff value of 106.9 pmol / L provides a sensitivity of 100% and 158.5 pmol / L provides a sensitivity of 95%.

[0451] Results of biomarker analysis when combined with GFR determination within 90 days from baseline In particular, good results were obtained when NTproBNP and MRproADM were used in combination with determining parameters indicative of renal function, such as GFR.

[0452] NTproBNP and MRproADM based algorithms stratified by eGFR<60 and eGFR>=60 are able to identify a notable portion of patients who remained event-free for 90 days already under SOC, while identifying important SOC patients with events with perfect or near perfect sensitivity. At a sensitivity of 100% (99%, 95%), 9% (15%, 25%) of SOC patients could be virtually excluded from RPM. For comparison, at a sensitivity of 100%, using NTproBNP alone or in combination with eGFR would safely exclude only 0.8% or 2.7% of patients, respectively.

[0453] For an exclusion algorithm with a sensitivity of 100%, the risk of suffering an event within 90 days in SOC patients was 11% for patients hypothetically assigned to RPM and 0% for those maintained in SOC. By comparing these subpopulations between the study arms (again, pooling all biomarker observations and follow-up observations during the following 90 days, no trends in biomarkers over repeated measurements were observed), it was shown that RPM could significantly reduce the risk of suffering an event among hypothetically enrolled patients from 11% to 8%. Furthermore, the NNT dropped from 42 (all patients) to 34 (hypothetically enrolled patients). Finally, true RPM patients who would have been excluded from RPM by the presented algorithm were significantly less likely to suffer emergency events, had fewer medication changes, and less communication with RPM physicians than enrolled patients. This further suggested that identified low-risk patients, based on biomarker characterization, do not require RPM more than identified high-risk patients.

[0454] Table 10 summarizes the results and beneficial impact of stratifying biomarker cutoffs by GFR based on CKD-EPI.

[0455] Tables 11 and 13 summarize the results and beneficial impact of stratifying biomarker cutoffs by Cockroft-Gault-based GFR.

[0456] Particularly good results are obtained when proADM, proBNP and GFR are used in combination: in such cases, as can be seen in columns 6 and 9 of both tables, two or three times more patients can be safely excluded from remote management compared to using only proBNP and GFR without proADM to prescribe or not prescribe remote patient management.

[0457] Results of biomarker analysis from baseline to 1 year Furthermore, the predictive power of biomarkers was evaluated for adverse events within 1 year after randomization and sample disaggregation. NTproBNP and MRproADM were used at baseline. The primary endpoint was death from any cause or loss to CV hospitalization, and the secondary endpoint was death from any cause within 1 year after randomization.

[0458] Primary analysis of quintiles of the two biomarkers for prediction of the primary endpoint shows that the incidence in the SOC group increased from 1.4% days lost (MRproADM ≤ 0.75 nmol / L, lowest quintile) to 17.6% in the pore quintile. In the RPM group, values ​​were similar in the lowest quintile (1.4%) and 12.1% in the highest quintile (p = 0.21 vs. SOC). The treatment effect (ratio of days lost in RPM vs. SOC) increased from 0.98 to 0.69 (p for interaction is 0.29). NTproBNP had a similar prognostic ability, with treatment effects ranging from 0.87 in the lowest quintile to 0.63 in the highest quintile (p for interaction is 0.33). Findings for the secondary endpoint (death from any cause) were similar. Thus, there was a trend towards a lower proportion of days lost and risk of death, with a smaller benefit of RPM in patients with lower biomarker levels. Based on this observation, a biomarker-based RPM patient selection algorithm was explored, aiming to reduce the proportion of patients recommended for RPM.

[0459] Combining biomarkers to identify patients who were event-free (death from any cause) and therefore could not benefit from RPM allowed 13.7% of patients with MRproADM<0.69 nmol / L or NTproBNP<125.1 ng L to be excluded from the SOC group. If a miss-rate of <2.5% or <5% was accepted, the relative share of excluded patients rose to 16.9% or 25.3%, respectively. In all three scenarios, the hazard ratio for the beneficial treatment effect of RPM remained significant at 0.71 (p<0.05) and was similar to the primary study and reciprocally (HR=0.70).

[0460] Thus, by using biomarker-based selection of patients, the number needed to treat to prevent one death can be reduced from 28 to 26 (sensitivity 100%, no missed events) and as low as 23 (sensitivity 95.5%, MRproADM<0.75nmol / L, NTproBNP<383.3ng / L).

[0461] Table 12 summarizes the results and Figure 8 shows the Kaplan-Meier curves for the biomarker-based modified patient cohort (event-free patients excluded based on the biomarker cutoffs above with 100% sensitivity).

[0462] In conclusion, the use of MRproADM and NTproBNP alone or in combination allows a safer, more accurate, more effective and therefore more cost-saving allocation of HF patients to remote patient management. The number of people needed to be treated with RPM to save one life can be reduced from 28 in the original study to 23 using a biomarker approach.

[0463] Example 3: Prognostic potential of the combination of proBNP and ADM for prescribing or not prescribing remote patient management Further statistical analyses on the study population described above and in Koehler et al. 2018a and Koehler et al. 2018b and on the data collected from the TIM-HF II study were performed to determine particularly informative scenarios for predicting the benefit of prescribing or not prescribing remote patient management using proADM and / or proBNP.

[0464] Outcome and biomarker analysis Briefly, as outcomes, the primary study end point was the "proportion of days lost due to unplanned cardiovascular (CV) hospitalization or death from any cause during individual follow-up periods." Regular individual follow-up periods covered 365 days after randomization for all patients and various times until the final study visit, which should have taken place within 4 weeks after the 365-day period. Secondary end points included all-cause mortality (a), CV mortality (b), % days lost due to unplanned CV hospitalization (c), and days lost due to hospitalization with worsening heart failure (d).

[0465] For the purposes of biomarker analysis, baseline levels of NTproBNP and MRproADM were used. Whole blood was collected by venipuncture during the study visit. NTproBNP was measured using the chemiluminescent immunoassay Roche NTproBNP (Roche Diagnostics GmbH, Mannheim, Germany), which has a measurement range of 5–35.000 pg / ml and a functional assay sensitivity of 50 pg / ml (manufacturer information in the package insert). MRproADM was measured using the immunofluorescence assay B·R·A·H·M·S MRproADM KRYPTOR (B·R·A·H·M·S GmbH, Hennigsdorf, Germany). The MRproADM assay has a measurement range of 0.05–100 nmol / L with a functional assay sensitivity of 0.25 nmol / L.

[0466] statistics Association of NTproBNP and MRproADM with endpoints Linear hazards regression and Cox proportional hazards regression were used to test the association of both biomarkers with % of days lost due to unplanned CV hospitalization and time to death from any cause, respectively. For modeling, biomarker levels were log-transformed. Also, % of days lost was log-transformed after substituting a value of 0.1% for patients with 0.0% of days lost, which is consistent with previous analyses. Models including both biomarkers were compared with models including only NTproBNP as a predictor to assess the significance of adding MRproADM (by F-test and likelihood ratio test, respectively).

[0467] As a supplement, we calculated the mean % of days lost due to unplanned CV hospitalization (using the average weighted by individual follow-up time in line with previous analyses of the study) as well as the proportion of death from any cause in both SOC and RPM groups for all quintiles of both biomarkers. For each quintile, p-values ​​for the effect of RPM vs. SOC on these endpoints were calculated using permutation tests (% of days lost due to unplanned CV hospitalization) and Cox proportional hazards regression (death from any cause). We also calculated p-values ​​for the interaction between quintile and RPM to explore how strong the evidence was that the effect of RPM differed between biomarker quintiles.

[0468] Recommended patient selection scenarios for RPM Only patients assigned to the SOC study arm were used to identify criteria for a subgroup of the original population for which RPM could be recommended based on additional biomarker evaluation. These therefore served as the benchmark population for the derivation and evaluation of biomarker guidance, since only in this group could it be assumed that no clinical endpoints were hindered by RPM.

[0469] For the primary endpoint, an event was defined as having at least 30 days lost out of 365 days of follow-up, i.e., having a proportion of lost days of at least 8.2%. In the following description of this example, this scenario is referred to as "≧30 days lost / year". This approach includes not only the majority of patients who died (82 out of 89 in the SOC group), but also all patients who spent more than one month in the hospital within one year due to unplanned CV hospitalization. In this binary measure, only deaths relatively distant in time from the baseline biomarker measurement (at least 11 months for patients followed for one year) were classified as no event. Figure 12 further illustrates this event definition. The secondary endpoint was prespecified as death from any cause.

[0470] Biomarker-based selection scenarios of patients recommended for RPM were further optimized for high safety. Different scenarios were considered for both endpoints with desired sensitivities of 100%, 98% and 95%, meaning that the defined biomarker cut-offs should not miss more than 0%, 2% or 5% of patients who developed an event during the follow-up period. To take advantage of the information carried by both biomarkers, patients were recommended for RPM if they had at least a certain level of NTproBNP and, at the same time, at least a certain level of MRproADM.

[0471] It should be noted that, theoretically, in all scenarios with a sensitivity of <100%, there are multiple possible combinations of biomarker cutoffs that achieve the desired sensitivity. The analysis of the current example remained limited to the case where both biomarkers achieved the same sensitivity by themselves. This is equivalent to assuming that both biomarkers should be equally weighted when used jointly for patient selection (instead of assuming that higher levels of NTproBNP are better tolerated than those of MRproADM, or vice versa). Figure 9 shows how biomarker guidance was evaluated for the combination of NTproBNP and MRproADM. For comparison, we also calculated the proportion of patients who could be excluded from the population for which RPM was recommended when NTproBNP was used alone.

[0472] Evaluating scenarios for the effectiveness of RPM The fundamental objective of biomarker guidance for RPM is to assign patients to the intervention that will benefit most from RPM and exclude those who will not. As a result, for all six scenarios (two endpoints (% of days lost due to unplanned CV hospitalization, death from any cause) and three desired sensitivities) it was important to show how the endpoints of TIM-HF2, and therefore the efficacy of RPM in heart failure, are affected by retrospectively reducing the original population to a subpopulation recommended for RPM via biomarkers. Effect estimates and p-values ​​were calculated for the subpopulations that met the recommended criteria (i.e., NTproBNP and MRproADM exceeded their respective thresholds) following all statistical procedures for the original study population in our previous publication (Koehler et al. 2018a).

[0473] Briefly, for the ratio of % of days lost between treatments (primary and secondary endpoints c and d), the geometric mean of % of days lost in the RPM group was divided by the geometric mean of % of days lost in the SOC group. Prior to this, patients with 0.0% of days lost were assigned a value of 0.1% of days lost. The corresponding p-values ​​were calculated by permutation testing with 2000 permutations and the difference in the means as the test statistic. For time-to-event analyses of death from any cause (secondary endpoint a) and CV death (secondary endpoint b), Cox proportional hazards regression was used to estimate hazard ratios, confidence intervals, and p-values. Regarding the interpretation of the results, it should be noted that the upward shift in p-values ​​is at least somewhat mechanical, because in the reduced subpopulations, statistical tests are less capable of detecting effects at the same significance level. For the latter endpoint, non-CV deaths were treated as normal censored events (end of follow-up). In addition, Kaplan-Meier curves and log-rank tests were provided for one endpoint, death from any cause, in the patient selection scenario.

[0474] Further analysis of the most efficient scenario: Patient demographics and RPM interventions To be the most efficient of the evaluated scenarios of dual biomarker guidance (which most reduces the population recommended for RPM), with a sensitivity of 95% for ≥30 days lost / year, baseline demographics were compared between the groups in which RPM was recommended and those in which RPM was not recommended. Baseline demographics were also compared between the SOC and RPM groups for patients in which RPM was recommended (i.e., the groups on which the endpoint calculations described in the previous subsection were based).

[0475] The availability of emergency data, cross-validated with the unique electronic health record data of patients who belonged to the original RPM group, allowed further comparisons. TMC adopted various interventions in the course of telemedicine and registered the occurrence of emergency situations in RPM patients. These RPM data therefore allow us to compare (i) the incidence of emergency situations, (ii) the average medical effort expended by TMC physicians between patients who were recommended RPM in the retrospective scenario and those who were not encouraged RPM in this scenario, in line with their original random group allocation in the study. For the calculation of efforts in time, all interventions by TMC recorded in the electronic patient record were categorized into medical and non-medical related actions. The average duration of each action was calculated via experience values ​​and values ​​from the electronic patient record. Emergency situations and medical efforts were visualized according to Allen et al. (2019).

[0476] Furthermore, the investigated biomarker guidance can be roughly estimated by summing up the time of medical and non-medical efforts of all patients who were not recommended to RPM in the retrospective scenario but were assigned to RPM in the original study.

[0477] All statistical analyses were performed and documented by script using R version 3.5.1, a language and environment for data processing, statistical computing, and graphics (R. Core Team 2018). Cox proportional hazards models were calculated with package survival 2.42.3 (Terry et al. 2000).

[0478] Results regarding the use of proBNP and ADM in combination for remote patient management prescriptions Association of NTproBNP and MRproADM with endpoints Linear and Cox proportional hazards regression models showed that both NTproBNP and MRproADM were significantly associated with % of days lost due to unplanned CV hospitalization and time to death from any cause (all p<0.001). In model comparisons, linear and Cox proportional hazards models including both biomarkers performed significantly better than models including only NTproBNP as a predictor (all p<0.001). Raw data for the association of biomarkers with the primary endpoint are shown in Figure 12.

[0479] Consistent with this, the primary analysis of the quintiles of the two biomarkers and their association with the primary endpoint event rate showed that for MRproADM, the % of days lost in the SOC group increased across quintiles, from 1.4% (MRproADM ≦ 0.75 nmol / L, lowest quintile, Table 14A) to 17.6% (MRproADM up to 7.8 nmol / L, highest quintile). In the RPM group, the trend was similar (1.4% to 12.1%). The same trend of a higher risk of suffering an event with higher biomarker levels is seen for NTproBNP (Table 14A), as well as for the endpoint all-cause mortality (Table 14B).

[0480] Moreover, the impact of RPM on these two endpoints tended to increase across quintiles. For example, NTproBNP had prognostic value for the treatment effect of RPM on % of days lost, as patients in the lowest quintile (≤487.9 pg / ml) had a slightly higher % of days lost in RPM than in SOC, whereas those in the highest quintile (with 3701.2-35000 pg / ml) had a significantly decreased % of days lost in the SOC group. The same trend was observed for MRproADM and the endpoint death from any cause.

[0481] Recommended patient selection scenarios for RPM The cutoffs for selection at which patients should not be recommended for RPM ranged from 125.1 to 413.7 pg / ml for NTproBNP and 0.63 to 0.75 nmol / L for MRproADM, depending on the desired safety (sensitivity 100%, 98%, 95%) and patient inclusion criteria (unplanned CV hospitalization or death from any cause; at least 30 days / year lost due to death from any cause), as shown in Table 15.

[0482] The lower the desired sensitivity, the higher the cutoff for significant biomarkers and the higher the proportion of patients not recommended for RPM. Also, the lower the desired sensitivity, the higher the positive predictive value (PPV) of the biomarker guidance. For example, if we allowed 5% of patients suffering from a loss of 30 or more days out of 365 days not to receive RPM (95% sensitivity), 21.5% of patients recommended for RPM by the biomarker combination would experience an event. For comparison, the rate of this event in the total SOC group in the original study population was 16.4%. This increase in PPV was associated with a 27.0% decrease in the population recommended for RPM.

[0483] This scenario of biomarker guidance is shown in panel B of Figure 9. Thus, biomarkers allowed to define the critical population more precisely and efficiently. In particular, the proportion of patients recommended RPM could be reduced by about a factor of four in both scenarios with a sensitivity of 95%. Even with a sensitivity of 100% using NTproBNP and MRproADM together, 10.8% and 13.9% could be excluded from RPM (Table 15).

[0484] The reduction in the population recommended for RPM with the combination of NTproBNP and MRproADM allowed us to exclude event-free patients with fairly high NTproBNP levels but fairly low MRproADM levels (lower right quadrant in Figure 9B ), who were included in the RPM based on the sole use of NTproBNP for risk stratification ( Figure 9 , panel A).

[0485] Comparing the guidance of single and dual biomarkers investigated, the superiority of using two biomarkers is especially evident in terms of safety: at a sensitivity of 100% in terms of ≥30 days lost out of 365 days (respectively death from any cause), with NTproBNP alone the population for which RPM is recommended would reduce by 3.4% (3.4%) of patients, reaching a specificity of 4.0% (3.9%). This is more than three times higher when using the combination of NTproBNP and MRproADM, the population for which RPM is recommended would reduce by 10.8% (13.9%) of patients, reaching a specificity of 12.9% (15.7%).

[0486] For a 95% sensitivity for ≥30 lost days out of 365, NTproBNP alone reduced the RPM-recommended population by 23.4% of patients (Figure 9, Panel A) and reached a specificity of 27.1%. This also increased with the additional use of MRproADM, allowing the RPM-recommended population to reduce by 27.0% of patients and reach a specificity of 31.4% (Figure 9, Panel B).

[0487] It should be noted that for all sensitivities <100%, these figures depend on the exact combination algorithm used to integrate the information of both biomarkers. For example, following the approach considered in the example with equal weight of both biomarkers (see Methods), in a scenario with a sensitivity of 95% for all-cause mortality, NTproBNP alone reduced the population recommended for RPM by 32.3% (cutoff ≥ 383.3 pg / ml), while dual biomarker guidance in this scenario only reduced by 25.6% (Table 15). By additionally using MRproADM (single marker sensitivity 100%) in the selection procedure, the exclusion rate can be further increased to 36.0%.

[0488] Evaluating scenarios for the effectiveness of RPM Table 15 shows the effect of biomarker-based attenuation of the randomized group with respect to the primary endpoint and all secondary endpoints. The impact on the endpoints remained mostly significant, especially for the most efficient scenario and the endpoint all-cause mortality. More notably, as the p-values ​​are affected by the biomarker-based attenuation of the sample, the effect estimates remained very similar to the results of the original study, indicating the effectiveness of RPM for most endpoints. This was also highlighted by the Kaplan-Meier curves for the latter scenario and the endpoint all-cause mortality (see Figure 11). The number needed to treat (NNT) for all-cause mortality could be reduced by excluding patients. In the 95% sensitivity scenario for ≥30 days lost / year (all-cause mortality), the NNT reduced from 28 in the original population to 23 (21) in the attenuated subpopulation selected via biomarkers.

[0489] Further analysis of the 95% scenario: Patient demographics and RPM interventions The biomarker guidance scenario with 95% sensitivity for the endpoint of ≥30 days lost / year (i.e., ≥8.2% days lost) described the patient demographics of the identified subgroups. The biomarker cutoffs for this scenario were as follows: only patients with both NTproBNP levels ≥413.7 pg / ml and MRproADM levels ≥0.75 nmol / L were recommended for RPM (see Table 16).

[0490] This left n=1098 / 1538 (71.4%) patients in the biomarker-selected dataset with 95% sensitivity in relation to the primary endpoint. Table 16a shows the background of these patients by randomized treatment group, and Table 16b shows the background of selected and unselected patients. The data in Table 16a confirm that despite the biomarker-based selection, both randomized treatment groups remain comparable without significant differences. Table 16b shows that the biomarkers of selected patients indicate a higher risk with lower LVEF and higher NYHA classification.

[0491] Further results were obtained based on the unique electronic health record data available for patients who were in the RPM group during the study. In the scenarios considered in the current example, the incidence of emergency events in patients excluded from RPM was significantly lower than in patients recommended for RPM (median 0, IQR 0–1 vs. median 1, IQR 0–2; Wilcoxon rank sum test, p<0.001, Figure 10A). This result is therefore consistent with the aim of identifying patients who are more in need of RPM because they are at risk of experiencing adverse events. Moreover, as expected, the medical efforts expended by TMC staff in patients who were not recommended for RPM given the investigated biomarker guidance scenarios were significantly lower than those expended in patients recommended for RPM (mean 305 min, SD 88 min vs. mean 355 min, SD 141 min; t-test, p<0.001, Figure 10B).

[0492] Based on effort data that could be estimated from the electronic health records of the individual patients assigned to RPM in the first study, this biomarker guidance scenario would have saved substantial staff time. The total estimated medical effort hours spent by the TMC for patients in the RPM arm of the study was 4332 hours (approximately 5.7 hours per patient). The total non-medical effort hours spent by the TMC was 356 hours (approximately 0.5 hours per patient). Of these, 1170 hours of medical effort and 99 hours of non-medical effort would have been saved if RPM had not been recommended for patients below the biomarker cutoffs examined here (see Table 16a, b for a detailed description of the individual subpopulations of patients). Note that these results are qualitatively similar to other biomarker guidance scenarios based on other desired sensitivities or other endpoints (death from any cause).

[0493] Further results relate to patients selected with a sensitivity of 95% for the endpoint all-cause mortality. Figure 11 shows the Kaplan-Meier curves for the two treatment arms in the biomarker-based personalized cohort.

[0494] In summary, the current working example of the biomarker substudy of TIM-HF2 allows the selection of patients originally randomized with the same benefit as the original cohort in 72.5% of cases with the combination of NTproBNP (cut-off <383.3 pg / ml) and MRproADM (cut-off <0.75 nmol / L). Thus, the RPM index can be individualized to a higher-risk cohort by simply measuring these two biomarkers at baseline. Of note, the biomarker test can be easily taken by any caregiver and does not require other preanalytical steps. Blood samples can be sent by standard postal service to a central laboratory, which is very cost-effective, as evidenced in the main study (Koehler et al., 2018a).

[0495] Example 3 mainly analyzes the prognostic effect of NTproBNP and MRproADM, both of which are significantly associated with outcome. When comparing the two randomized groups, the effect of treatment is not significantly predicted by both markers. Therefore, the approach using these markers was mainly adopted to identify patients who do not benefit from RPM. This was done by analyzing the biomarkers in the SOC group without events. The cutoffs identified by this approach were applied to the randomized cohort to compare the selected populations for efficacy.

[0496] Example 3 shows that the effect of RPM remains largely the same. This approach allows the recommendation of RPM to be individualized, in this example reducing the eligible cohort to 71.4% of the total population, thus effectively avoiding unnecessary and costly treatment.

[0497] [Table 2]

[0498] [Table 3]

[0499] [Table 4]

[0500] [Table 5]

[0501] [Table 6]

[0502] [Table 7]

[0503]

Table 8

[0504]

Table 9

[0505]

Table 10

[0506]

Table 11

[0507]

Table 12

[0508]

Table 13

[0509]

Table 14

[0510]

Table 15

[0511]

Table 16

[0512]

Table 17

[0513] [Table 18]

[0514] [Table 19]

[0515] [Table 20]

[0516] [Table 21]

[0517] [Table 22]

[0518] [Table 23]

[0519] [Table 24]

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Claims

1. 1. A method for managing remote patient care for a patient diagnosed with cardiovascular disease and currently undergoing remote patient management (RPM), the method comprising: - providing at least one sample from said patient; - determining the level of at least one biomarker or one or more fragments thereof selected from the group consisting of pro-adrenomedullin (proADM), pro-brain natriuretic peptide (proBNP) and / or pro-atrial natriuretic peptide (proANP) in said at least one sample; - comparing said level of said at least one biomarker or one or more fragments thereof with a reference value; - assessing the benefit level of the RPM based on said comparison; - the method, wherein said benefit level of said at least one biomarker or one or more fragments thereof indicates the continuation, adjustment or discontinuation of RPM.

2. 2. The method of claim 1, wherein the low benefit level of proADM or one or more fragments thereof is less than or equal to ±20% of the reference value and / or the high benefit level of proADM or one or more fragments thereof is more than or equal to ±20% of the reference value, said reference value being selected from the range of 0.75 nmol / L to 1.07 nmol / L.

3. 2. The method according to claim 1, wherein the low benefit level of proBNP or one or more fragments thereof is less than or equal to ±20% of the reference value and / or the high benefit level of proBNP or one or more fragments thereof is more than or equal to ±20% of the reference value, said reference value being selected from the range of 237.6 pg / ml to 1595.8 pg / ml.

4. 2. The method according to claim 1, wherein the low benefit level of proANP or one or more fragments thereof is less than or equal to ±20% of the reference value and / or the high benefit level of proANP or one or more fragments thereof is more than or equal to ±20% of the reference value, said reference value being selected from the range of 106.9 pmol / L to 248.3 pmol / L.

5. 10. The method of claim 1, wherein the cardiovascular disease is heart failure.

6. 6. The method of claim 5, wherein the patient has been hospitalized within the past 12 months as a result of heart failure.

7. 10. The method of claim 1, wherein the cardiovascular disease is heart failure with a high risk of adverse outcomes.

8. The method of claim 1, wherein determining the level of proADM or one or more fragments thereof in the sample comprises determining the level of MRproADM, determining the level of proBNP or one or more fragments thereof in the sample comprises determining the level of NTproBNP, and / or determining the level of proANP or one or more fragments thereof in the sample comprises determining the level of MRproANP.

9. 10. The method of claim 1, further comprising determining at least one additional biomarker and / or clinical parameter.

10. The method of claim 1 , wherein the sample is selected from the group consisting of a blood sample, a saliva sample and / or a urine sample.

11. The method of claim 1 , wherein the remote patient management comprises remote monitoring of the patient's health status with respect to the status or progression of the cardiovascular disease.

12. The remote health monitoring includes repeatedly collecting data regarding the patient's health at the patient's location and remotely transmitting it to a monitoring system or device for review by a medical professional or automated medical system, the health data including blood pressure, electrocardiogram (ECG), peripheral blood oxygen saturation (SpO 2 12. The method of claim 11, wherein the measurement includes measuring the blood pressure, blood pressure, or body weight.

13. 10. The method of claim 1, wherein a first sample is isolated from the patient at a first time point and a second sample is isolated from the patient at a second time point.

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