A device that determines the cardiovascular risk score of users.

Abstract

Apparatus and methods are provided for determining a cardiovascular risk score from at least cardiovascular data. [Solution] The method includes providing a device adapted to measure a cardiovascular signal of a user, measuring the cardiovascular signal using the device during an observation period having a duration of at least 24 hours, each observation period having a duration of 24 hours having a plurality of observation periods, determining a cardiovascular value for each observation period, compiling the determined cardiovascular values ​​for corresponding measurement periods of each observation period into a set of cardiovascular parameters, creating a 24-hour circadian plot of the set of parameters against the corresponding measurement periods, determining a physiological parameter of the user using the circadian plot, and calculating a cardiovascular risk score for the user using the determined physiological parameter.

Description

[Technical Field]

[0001] The present invention relates to a device and method for determining a user's cardiovascular risk score. [Background technology]

[0002] Hypertension remains a major risk factor for death worldwide. Despite its prevalence, efforts to manage blood pressure (BP) have yet to be successful, and the difficulty lies in the tools still used to diagnose, measure, and treat hypertension—namely, the blood pressure monitor invented by Samuel Siegfried Carl von Bach in 1867. In recent years, there has been an explosion in devices that attempt to provide cuffless blood pressure estimates, overcoming many of the limitations of cuff-type blood pressure monitors. Unfortunately, due to the fundamental technological differences between conventional blood pressure cuffs and new cuffless devices, as well as reluctance to change well-established standards, skepticism and reluctance to adopt cuffless blood pressure monitors in clinical practice are understandable.

[0003] The scale of the hypertension problem is difficult to grasp. It is estimated that more than 1.3 billion people worldwide have hypertension. Nevertheless, current standards of thinking regarding the diagnosis, treatment, and monitoring of hypertension (HTN) result in low control rates. There are significant risks in continuing to maintain the status quo. Hypertension has been the leading cause of death and disease from cardiovascular disease for more than 40 years, is estimated to burden the US healthcare system with $131 billion annually, and is also the leading preventable risk factor for premature death globally. Changes that could improve the treatment of hypertension should be thoroughly considered, given the potential benefits.

[0004] Over the past 20 years, the medical field has undergone a dramatic digital shift, primarily driven by the proliferation of electronic health records (HERs), and accompanied by numerous digital health applications, enabling stronger, real-time, and actionable data and information exchange between patients and healthcare providers. In fact, 20 years ago (from the time of this application), seven years before the first iPhone® was released, the U.S. Institute of Medicine (IoM) authored a book highlighting the potential of computer-assisted (now called digital health or mobile health (mHealth)) devices to automate the transfer of clinical data to clinicians. These devices aim to improve clinical care and deepen the understanding of disease.

[0005] Twenty years later, technology is beginning to fulfill IoM's visionary insights and call to action. Cuffless blood pressure measurement technology promises improvements in the diagnosis, treatment, and monitoring of hypertension, with the potential to benefit millions of people with hypertension. While technological innovation has long focused on treating the disease after it has developed, technology now offers an opportunity to prevent uncontrolled hypertension and all its associated risks. The commercialization of cuffless blood pressure measurement devices will enable the resolution of many behavioral and practical challenges in the treatment of chronic diseases that have long been hidden. The treatment of hypertension has been limited by the limitations of blood pressure measurement in the doctor's office and at home using cuffs. Large-scale deployment will realize the advantages of cuffless blood pressure measurement devices, and ultimately, is expected to improve hypertension management worldwide.

[0006] In 1948, one of the most important studies on cardiovascular risk was initiated in Framingham, Massachusetts. There is no doubt about the groundbreaking nature of the insights gained from the subsequent 30 years of research. As part of the study protocol, blood pressure measurements in clinics were determined by auscultation of Korotkoff sounds, the only technique available at the time. Subjects sat with their backs leaning back, and only their left arm was measured. To this day, despite a significant shift from manual mercury sphygmomanometers to automated oscillometric devices, all major guidelines recommend that blood pressure measurements be performed according to the methods of the original Framingham study.

[0007] After decades of using cuff-type target values ​​in clinical trials and clinical guidelines, this blood pressure measurement method has become the accepted standard in the medical community for blood pressure estimation. Furthermore, this method supports the hypothesis that individuals have physiologically stable and predictable blood pressure. Expert consensus documents, such as the American Heart Association (AHA) guidelines, state that "traditional clinical measurements, when measured correctly, serve as surrogate markers of a patient's true blood pressure, are considered long-term averages, and are the most important factor in assessing the adverse effects of blood pressure."

[0008] However, as our understanding of hypertension progresses, it has become clear that blood pressure constantly changes and adapts 24 hours a day in response to lifestyle, daily activities, medication, physical or mental stressors, and changes in posture. Expert guidelines suggest that office measurements should be confirmed by measurements taken outside the office over the following weeks or months, meaning that the true nature of an individual's blood pressure pattern in their daily life cannot be fully estimated by relaxing for five minutes in a temperature-controlled office with both feet on the floor, in a quiet environment free from exercise, conversation, caffeine, and noise.

[0009] Portable automatic blood pressure monitors (ABPMs) show some of these fluctuations, but usually only over a 24-hour period. Recently, the development of cuffless continuous automatic blood pressure monitoring devices has made it possible for the first time to more accurately track an individual's blood pressure over time.

[0010] The importance of out-of-office blood pressure monitoring is recognized in all major hypertension guidelines that confirm in-office blood pressure measurements. ABPM (Ambient Blood Pressure Monitoring) has been considered the golden rule for out-of-office blood pressure monitoring. However, for various reasons, ABPM remains underutilized. While ABPM may be the currently recommended tool for out-of-office blood pressure monitoring, it is rarely used in the United States. Only about 0.1% of Medicare beneficiaries, where the prevalence of hypertension is estimated at 50%, use ABPM annually. In China, only 1.6% of primary care providers surveyed reported using ABPM to diagnose hypertension. A simpler, cheaper, and more widely available solution for blood pressure monitoring would greatly benefit both healthcare professionals and patients.

[0011] Home blood pressure monitoring (HBPM) is recommended in all major hypertension guidelines as an essential aid in the diagnosis, monitoring, and management of hypertension. However, in practice, it is difficult for patients to monitor their blood pressure at home and transmit meaningful data. In addition, patients need to be trained in measuring their blood pressure according to the same standard procedures as when measuring blood pressure in a clinic. Despite the easy availability of relatively inexpensive home blood pressure cuffs (home blood pressure monitors), the rate of active home blood pressure monitoring among actual patients is surprisingly low. Half of hypertensive patients report never measuring their blood pressure at home, 10% do so less than once a month, and only 24% report measuring their blood pressure at least once a week. Data shows that although expert panels and consensus guidelines routinely recommend HBPM, in reality, most hypertensive patients do not perform HBPM, and none perform it twice a day for at least seven days as recommended.

[0012] There are many possible explanations for the significant discrepancy between recommendations and actual clinical practice. A 2017 study investigated the barriers to primary care providers recommending HBPM to patients. More than two-thirds of respondents cited one or more of the following reasons as barriers to obtaining HBPM data: Patients who are unable to complete HBPM due to reasons such as low health literacy, time constraints, the invasiveness of the tests, the need for routine care, or the need to bring HBPM into the office. Inaccurate test results can result from patients failing to follow the HBPM protocol (rules for performing HBPM), such as using an inappropriate cuff size, poorly timing blood pressure measurements, failing to record measurements, or selectively choosing normal blood pressure values ​​("cherry-picking") to show to a doctor. Inaccurate results due to factors such as the patient's body type. The test results or cuff inflation may increase the patient's anxiety, and therefore may lack accuracy.

[0013] Blood pressure (BP) is known to change over time and follow a circadian rhythm. Typically, systolic and diastolic values ​​are higher during the day and lower at night and during sleep. This nighttime drop in blood pressure is commonly called "nocturnal dipping." However, the exact characteristics (profile) of this nocturnal drop vary greatly from person to person and can also change slowly over time. Several parameters of this characteristic, such as the amplitude of nocturnal dipping and the morning rise (also called "morning rise"), are clinically relevant because they correlate with various cardiovascular risk factors. [Prior art documents] [Patent Documents]

[0014] [Patent Document 1] European Patent Application Publication No. 3226758 [Overview of the project]

[0015] This disclosure relates to a method for determining a user's cardiovascular risk score, and the method comprises the following: To provide a device that measures at least the cardiovascular signals of a user. At least cardiovascular signals must be measured during an observation period with a duration of at least 24 hours. The observation period is further divided into at least one observation interval, each of which has a duration of 24 hours and comprises multiple measurement periods. Determine cardiovascular values ​​for each of the multiple measurement periods within at least one observation period. For each observation interval, collect the cardiovascular values ​​determined during a predetermined measurement period into a group of cardiovascular parameters. For each observation interval, plot (in a figure or graph) the cardiovascular parameter group against the corresponding measurement period with a 24-hour cycle. Multiple physiological parameters of the user are calculated from a 24-hour cycle plot. Calculating a risk score of a user's cardiovascular system from the determined (calculated plurality of) physiological parameters.

[0016] The present disclosure further relates to an apparatus for determining a risk score of a user's cardiovascular system.

[0017] The present disclosure further relates to a computer program comprising instructions that cause a computer to execute the steps of the above method when the program is executed by the computer.

[0018] Exemplary embodiments are disclosed in the description of this document and are illustrated by the following drawings.

Brief Description of the Drawings

[0019] [Figure 1] FIG. 1 ((a) to (d)) illustrates an apparatus and method for calculating a risk score of a user's cardiovascular system. [Figure 2] FIGS. 2 to 6 show examples of adaptation models that can be adopted, and the example of FIG. 2 is a trapezoidal model. [Figure 3] FIGS. 2 to 6 show examples of adaptation models that can be adopted, and the example of FIG. 3 is a rectangular model. [Figure 4] FIGS. 2 to 6 show examples of adaptation models that can be adopted, and the example of FIG. 4 is a Gaussian model. [Figure 5] FIGS. 2 to 6 show examples of adaptation models that can be adopted, and the example of FIG. 5 is a distorted Gaussian model. [Figure 6] FIGS. 2 to 6 show examples of adaptation models that can be adopted, and the example of FIG. 6 is a distorted flat Gaussian model. [Figure 7] FIG. 7 compares the average daytime blood pressure by the apparatus with the blood pressure measurement value by the home blood pressure observation (HBPM) measured on the same day when initializing the apparatus. [Figure 8]Figures 8(a) and (b) illustrate the low within-subject reproducibility of the portable blood pressure monitor (ABPM) trial. Because an individual's circadian blood pressure variability changes over time, the ABPM trial, by selecting an arbitrary measurement day, may generate a phenotype that misrepresents the patient's underlying blood pressure phenotype. [Figure 9] Figure 9 shows a comparison of estimated nocturnal blood pressure dipping (decrease) values ​​using ABPM and the device. [Figure 10a] Figure 10a shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10b] Figure 10b shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10c] Figure 10c shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10d] Figure 10d shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10e] Figure 10e shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10f] Figure 10f shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10g] Figure 10g shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10h] Figure 10h shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10i] Figure 10i shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 10j] Figure 10j shows a new generation of dynamic BP control metrics that can be generated from data captured by the device. [Figure 11] Figure 11 shows a device for determining a user's cardiovascular risk score according to one embodiment. [Figure 12] Figure 12 is a cross-sectional view of the apparatus according to one embodiment. [Modes for carrying out the invention]

[0020] This disclosure provides information about specific users based on their blood pressure measurement history over a certain period of time. We created a model of the circadian rhythm of blood pressure, From this model, we derive a set of relevant parameters that can be used to assess a user's cardiovascular risk score. Furthermore, this document describes methods and devices for communicating this cardiovascular risk score to users.

[0021] In particular (see Figures 1A to 1B), the user's cardiovascular signals are measured during the observation period Tm (Figure 1B) using a device 10 designed to measure such cardiovascular signals. The observation period Tm has a duration of at least 24 hours and is divided into several measurement periods (not shown). For each measurement period, the user's cardiovascular parameters are determined.

[0022] Each cardiovascular parameter determined for the corresponding measurement period is grouped into a cardiovascular parameter group.

[0023] From there, a chart or graph is created showing the circadian rhythm of cardiovascular parameters for the corresponding measurement period.

[0024] For specific users, systolic and diastolic blood pressure values ​​are aggregated over a continuous period (e.g., two weeks) using a 24-hour, 7-day system (24 hours a day, 7 days a week). For each measurement, the exact date is ignored; only the time information (hours and minutes) is stored. This process integrates all blood pressure measurements (systolic or diastolic, separately) for the study period into a single 24-hour period centered around midnight. Blood pressure measurements outside the defined range (median ± 2 × IQR (interquartile range)) for each hourly measurement period (e.g., from 7:00 AM to 7:59 AM) are discarded before further analysis (outlier exclusion).

[0025] For the remaining data, a constrained piecewise linear model is fitted by least squares optimization. This model is constrained such that the geometric features (profile) of the BP are constant during nighttime slopes and restricted to a different constant value during the day, allowing for two linear transitions (ramps) between these two states.

[0026] Once constructed, this model generates a set of six physiological parameters that uniquely describe the individual's BP profile (see Figure 1b). These parameters are: Diurnal BP value (Y0), Absolute amplitude (ampl) at night, Time of start of slope (X0), Duration of the lamp before nighttime (dl), Duration of the slope plateau (nl), Duration of lamps after nighttime (al) That is the case.

[0027] Physiological parameters may also be combined with additional clinically relevant parameters. These parameters may, for example (not exhaustively): Nighttime BP value, Relative nighttime fluctuation range, Duration of nighttime fluctuations, Time within the target range (TTR), or The steep incline of the morning (BP) That is the case.

[0028] The calculated physiological parameters can further determine the user's blood pressure phenotype, including classifications such as true normal blood pressure, white-coat hypertension, mask hypertension, persistent hypertension, hypotension, nocturnal depression, and nocturnal rise.

[0029] At least one of the physiological parameters, clinically relevant parameters, and blood pressure phenotype may be incorporated into the additional value for cardiovascular abnormality risk.

[0030] In the embodiments shown in Figures 1a to 1d, the method for calculating a user's cardiovascular risk score comprises the following steps. The steps include providing a device 10 that measures at least the cardiovascular signals of a user (Figure 1a), The step (Figure 1b) is to measure the signals of at least the cardiovascular system using the device 10 during an observation period Tm having a duration of at least 24 hours, The observation period Tm is divided into at least one observation interval Ts, where at least one observation interval Ts lasts for 24 (24) hours, and the step of measuring cardiovascular signals comprising multiple measurement periods is as follows: At least one observation section The steps include determining cardiovascular values ​​for each of the multiple measurement periods within Ts, Each of the above at least one observation section The steps include: collecting the cardiovascular values ​​determined for a predetermined measurement period of Ts into a cardiovascular parameter group; For each observation interval Ts, the step is to construct a 24-hour circadian plot (in graph form) of the parameter set for the corresponding measurement period, and The circadian plot is used to determine several physiological parameters of the user (Figure 1c), and The steps include: using the determined physiological parameters to calculate the user's cardiovascular risk score (Figure 1d); and It is equipped with.

[0031] The measurement period corresponds to the period during which the cardiovascular system signal is being measured by device 10.

[0032] In Figures 1a to 1d, Figure 1a shows the device 10, represented as a wrist cuffless blood pressure optical sensor. Figure 1b shows the cardiovascular values ​​(in this case, blood pressure values) calculated from the cardiovascular signals (in this case, PPG signals, not shown) provided to the device 10. Figure 1c shows the collection of cardiovascular values ​​for each observation interval Ts over the entire observation period Tm (see Figure 2), compiled into a set of cardiovascular parameters, along with the construction of a 24-hour circadian plot, the fitting of a model to the circadian plot, and the determination of multiple physiological parameters. From these multiple physiological parameters, the user's cardiovascular risk score is calculated (Figure 1d).

[0033] From one perspective, the observation period Tm can have a duration of 48 hours, 7 days, 1 month, or 1 year. Note that if the observation period Tm has a duration of 24 hours, its duration is equal to the duration of the observation interval Ts.

[0034] From another perspective, the measurement period can have a duration of at least 10 seconds or 30 seconds, i.e., 1 minute, 5 minutes, 1 hour, 2 hours, 4 hours, or 6 hours.

[0035] In the example in Figure 1b, the observation period Tm is longer than 24 hours. For example, the observation period Tm corresponds to two weeks (14 days). In this case, the observation period Tm comprises one or more observation intervals Ts (14 observation intervals lasting 24 hours for a two-week observation period Tm).

[0036] Each 24-hour observation period Ts comprises multiple measurement periods. For example, observation period Ts comprises 24 measurement periods, each lasting one hour. Therefore, multiple cardiovascular values ​​are calculated for each measurement period corresponding to a time in a day (24 cardiovascular values ​​are calculated for each time in a day).

[0037] Therefore, multiple cardiovascular values ​​are collected into cardiovascular parameter groups for each time period of the day corresponding to each measurement period (see Figure 1c).

[0038] From one perspective, cardiovascular signals can be measured separately on weekdays and non-weekdays.

[0039] Cardiovascular signals may include any signals that measure the degree of physical activity, such as those that measure the quantity and quality of sleep, values ​​derived from electrocardiogram signals, values ​​derived from photoplethysmography signals, values ​​derived from bioimpedance signals, values ​​derived from ultrasound signals, or values ​​derived from arterial pulsation signals such as pulse pressure.

[0040] Cardiovascular values ​​can be calculated from cardiovascular signals measured during the measurement period. Calculating cardiovascular values ​​may involve identifying multiple pulses of cardiovascular signals. Calculating cardiovascular values ​​may further involve determining at least one characteristic of the identified pulses and calculating the cardiovascular value based on that at least one characteristic. Calculating cardiovascular values ​​may further involve determining at least one feature of the identified pulses and calculating cardiovascular values ​​based on that at least one feature. For example, a method for calculating cardiovascular values ​​is presented in Patent Document 1. In other embodiments, cardiovascular values ​​may be calculated by applying a nonparametric algorithm or a machine learning algorithm without identifying multiple consecutive pulses of the cardiovascular signal.

[0041] The step of determining cardiovascular values ​​may include calculating cardiovascular values ​​from each cardiovascular signal. Cardiovascular values ​​may include any one of the following: systolic blood pressure, diastolic blood pressure, mean arterial pressure, heart rate, or blood glucose level.

[0042] Cardiovascular values ​​may further include any of the following: pulse pressure, central pulse wave velocity, peripheral pulse wave velocity, arteriosclerosis, aortic pulse wave time, augmentation factor, cardiac output, cardiac output parameters, pulse pressure parameters, systemic vascular resistance, venous pressure, systemic hemodynamic parameters, pulmonary hemodynamic parameters, cerebral hemodynamic parameters, heart rate, heart rate variability, heart rate interval, arrhythmia detection, ejection time, SpO2 (oxygen saturation), SppHb (total hemoglobin concentration), SpMet (methemoglobin concentration), SpCO (carboxyhemoglobin concentration), respiratory rate, tidal volume, and multiple general cardiovascular and health indicators.

[0043] The step of collecting cardiovascular values ​​may include grouping the cardiovascular values ​​determined by the device 10 during a predetermined measurement period into cardiovascular parameter groups (see Figures 1b and 1c).

[0044] The step of constructing a 24-hour circadian plot may involve plotting (in a chart) a set of cardiovascular parameters calculated for each measurement period as a function of time over 24 hours, for example, from midnight to the next midnight (see Figure 1c).

[0045] From one perspective, the cardiovascular parameter group can have durations of 10 seconds, 30 seconds, 1 minute, 5 minutes, 10 minutes, or 30 minutes.

[0046] The step of using a circadian plot may involve calculating several physiological parameters of the user from the circadian plot.

[0047] From one perspective, the step of using a circadian plot may involve calculating a representative cardiovascular value for each group of cardiovascular parameters, based on the cardiovascular values ​​collected for that group.

[0048] From another perspective, the step of using a circadian plot may include a step of calculating representative values ​​for the cardiovascular system, which may include classifying cardiovascular parameters as inliers or outliers and calculating representative values ​​for the cardiovascular system using only inlier cardiovascular parameters.

[0049] From one perspective, calculating representative values ​​for the cardiovascular system may involve classifying cardiovascular parameters as inliers or outliers. Cardiovascular parameters may be weighted according to the probability of being inliers or outliers.

[0050] From one perspective, the step of determining multiple physiological parameters of the user includes the step of fitting a model to a circadian plot, where the physiological parameters correspond to the model parameters.

[0051] The model may be one of a linear model, a non-linear model, a constrained model, and an unconstrained model that is fitted by least squares optimization.

[0052] In one aspect, the constrained model may have a time constraint regarding the duration of a physiological variable (e.g., the duration of the nighttime blood pressure dip plateau is not longer than 12 hours, or the duration of the slope before nighttime is not longer than the duration of the dip plateau), or may have an amplitude constraint in the physiological variable (e.g., the blood pressure value during the day is not higher than 200 mmHg, or the rapid increase in systolic blood pressure in the morning is not greater than 50 mmHg / hour).

[0053] As shown in FIG. 2, the physiological parameter may include any one of the blood pressure value during the day (Y0), the absolute dip amplitude (ampl) at night, the temporal element (X0) of the dip start, the duration (dl) of the ramp before nighttime, the duration (nl) of the dip plateau, or the duration (al) of the ramp after nighttime.

[0054] FIGS. 2 to 6 show examples of model fitting. That is, a trapezoidal model (FIG. 2) including six parameters Y o , X o , ampl, dl, nl, al, a rectangular model (FIG. 3) including four parameters Y o , X o , ampl, nl, a Gaussian model (FIG. 4) including four parameters Y o , X o , sd, ampl, a skewed Gaussian model (FIG. 5) including five parameters Y o , X o , sd o [[ID=4I]], sd1, ampl, a skewed flat Gaussian model (FIG. 6) including six parameters Y o , X o , sdo, sd1, ampl, nl.

[0055] Physiological parameters may include the difference between physiological parameters calculated on weekdays and weekends, or more generally, the difference between physiological parameters calculated on weekdays and non-weekdays.

[0056] Physiological parameters may further include any temporal dynamics of the physiological parameters. These temporal dynamics may include daily variations in the parameter, variability over a set number of days, trends over a set number of days, or the number of days the parameter is above or below a threshold.

[0057] From one perspective, the model also uses non-physiological parameters. Non-physiological parameters include the following: Geographic location information including the user's altitude, weather forecasts and observations, heatwaves, cold waves, travel information, pollution information, and public health information including the status of infectious disease outbreaks in the user's location. Timing, allergy information, sunlight patterns, social information, education level, family situation (marital status, number and ages of children), Financial information, political interests and views, degree of responsibility, type of contract, occupational circumstances including regularity of working hours, Information from the calendar (including business days / non-business days and holidays, workload, and work schedule), Dietary patterns, exercise and activity patterns, level of sedentary life, Leisure information including caffeine, alcohol, and drug consumption. Pharmaceuticals (not limited to antihypertensive drugs), the health status of the user and their close relatives, Type and size of residence, owned or rented, whether or not pets are kept, Questionnaires regarding social media use, religious practices, mood, and general state of mind. That is the case.

[0058] The method may further include combining at least two physiological parameters to obtain one or more related physiological parameters. This combination may include adding, multiplying, or dividing at least two physiological parameters. Such a combination may further include calculating a correlation coefficient or synchronization coefficient of two or more physiological parameters.

[0059] The relevant physiological parameters include any of the following: Daytime blood pressure values, nighttime blood pressure values, relative nocturnal blood pressure amplitude, duration of nocturnal blood pressure drop, time within target range (TTR), Blood pressure fluctuation patterns, the slope of the morning sharp rise, nighttime systolic blood pressure (SBP), nighttime diastolic blood pressure (DBP), nighttime heart rate (HR), SBP decreased, DBP decreased, HR decreased, morning sharp rise in SBP, morning sharp rise in DBP. Morning surge in HR, SBP decline time, DBP decline time, HR decline time, synchronization of SBP or DBP or HR, Response to the type of medication, adherence to blood pressure medication, indicators of patient engagement, or response to lifestyle modification instructions. That is the case.

[0060] From one perspective, relevant physiological parameters may include the user's blood pressure phenotype.

[0061] From one perspective, the relevant physiological parameters include any of the following: True normal blood pressure, white-coat hypertension, masked hypertension, persistent hypertension, hypotension, nocturnal hypotension, nocturnal hypertension, or A phenotype that predicts a response (such as renal denervation) to a specific drug or treatment.

[0062] The step of calculating the user's cardiovascular risk score using determined physiological parameters may comprise the calculation of the user's cardiovascular risk score from the determined physiological parameters.

[0063] From one perspective, calculating a user's cardiovascular risk score involves using user data.

[0064] User data includes any of the following: Age, weight, height, sex, ethnicity, lipid levels, diabetes status, smoking, CT calcium (Agaston score), Family history, genetic markers related to (disease risk), actigraphy information (information from an actigraph (a small, highly sensitive accelerometer and logger worn on a wristwatch)), exercise information, dietary information, Stress level, general feeling, hormone data, menstrual cycle information, Medication information, weekday or weekend information, seasonal information, sleep quality information, bedtime patterns, Any of the parameters used to calculate cardiovascular risk scores in clinical guidelines such as the American College of Cardiology (ACC / AHA) guidelines, the European Society of Cardiology (ESC) guidelines, or the Multiethnic Atherosclerosis Study (MESA) database, The user data may consist of any non-physiological parameters.

[0065] User data may be provided manually by users, or it may be automatically integrated from an external system.

[0066] The cardiovascular risk score may be any of the following: 10-year risk for cardiovascular disease, 10-year risk for heart disease, 10-year risk for stroke, or other clinically relevant cardiovascular risk scores.

[0067] From one perspective, cardiovascular parameters may include at least one of the following: blood pressure, heart rate, cardiac output, blood glucose level, physical activity level, or sleep volume and quality level.

[0068] Also disclosed here is a device for determining a user's cardiovascular risk score.

[0069] The device 10 (see Figure 11) includes a measurement module 20 configured to measure the user's cardiovascular signals during an observation period Tm lasting at least 24 hours, and the observation period Tm is divided into multiple 24-hour observation intervals Ts, each having multiple measurement periods.

[0070] The device 10 further includes a processing unit 30 which determines cardiovascular values ​​from measured cardiovascular signals for each measurement period. The device collects the determined cardiovascular values ​​for the corresponding measurement period of each observation interval Ts into a set of cardiovascular parameters, constructs a circadian plot by plotting the 24-hour parameter set against the corresponding measurement period, determines several physiological parameters of the user using the circadian plot, and calculates the user's cardiovascular risk score using the determined physiological parameters.

[0071] The device further includes an interface 40 for displaying and / or transmitting the calculated cardiovascular risk score. The interface 40 is operably connected to the processing unit 30.

[0072] The processing unit 30 can be configured to perform the steps of determining cardiovascular values, collecting cardiovascular values, creating a 24-hour circadian plot, calculating multiple physiological parameters of the user, and calculating the user's cardiovascular risk score.

[0073] Device 10 may be operationally connected to a wired or wireless communication line, the latter (wireless communication line) which may include WiFi®, Bluetooth®, or cellular support (mobile data communication). Device 10 may also be operationally connected to memory.

[0074] Interface 40 may include applications on a smartphone, tablet, computer, smartwatch, or any other portable device.

[0075] Interface 40 may generate external signals to the user to provide guidance on how to optimize the user's calculated anomaly risk, for example, by suggesting changes in lifestyle, medication, or treatment.

[0076] Interface 40 may be configured to allow manual or automatic input of non-physiological data or user data.

[0077] The interface can be placed near the user or at a distance from the user.

[0078] From one perspective, the measurement module 20 may be designed to automatically measure cardiovascular signals without interaction with the user.

[0079] The measurement module 20 is as follows: Galvanic skin response (GSR) sensor arrays, bioimpedance (BioZ) sensor arrays, electrocardiogram (ECG) sensors, radio frequency (RF) detection-based sensors, radar sensors, Mechanical sensors, pressure sensors, invasive sensors, intra-arterial sensors, minimally invasive sensors, Subcutaneous sensor, tonometer, strain sensor, volume pulse wave sensor, microphone, Ultrasonic sensors, capacitive sensors, electromagnetic sensors, Raman sensors, A sensor capable of measuring pulsation signals from the capillary bed of the skin or any other part of the arterial tree. It may be equipped with one of the following arterial pulsation sensors. Therefore, the cardiovascular signals measured by the device 10 via the measurement module 20 may correspond to the signals measured by any of the above-mentioned sensors.

[0080] The device 10 may include a wearable device. Possible configurations when the device 10 is a wearable device are shown in the cross-sectional view of Figure 12. The device 10 may include a wristband 15 equipped with a measuring module 20. The measuring module 20 may include at least one pulsation sensing unit 21. For example, the measuring module 20 may include four pulsation sensing units 21 arranged along the inside of the periphery of the wristband 15 so as to come into contact with the skin of the user's wrist when the device 10 is worn. The pulsation sensing units 21 may be arranged on the wristband 15 in other ways.

[0081] In one embodiment, the pulse sensing unit 21 may include a photoplethysmography (PPG) sensor array that measures one or both of arterial pulsation, arterial diameter, blood flow, and blood components. In this embodiment, the cardiovascular signal is a photoplethysmography (PPG) signal. In this embodiment, the pulse sensing unit 21 may be mounted on the wristband 15 such that the optical sensor array 21 straddles an artery such as the ulnar artery 111 (near the ulna 113) or the radial artery 112 (near the radius 114 or any arterial vascular bed 117 of the skin of the wrist), or in other ways.

[0082] In one embodiment, the device 10 further includes a trigger module 50 (see Figure 11) configured to start or stop the measurement period by the measurement module 20.

[0083] The trigger module 50 can control the measurement module 20 according to trigger parameters. The trigger parameters may be user-specific. Examples of trigger parameters include trigger signals such as motion signals representing the user's movements. Such motion signals can be measured, for example, using motion sensors 60 placed on the device 10. The motion sensors 60 may include an inertial measuring unit (IMU), an accelerometer, a gyroscope, a geomagnetometer, or a combination of these devices.

[0084] From one perspective, the cardiovascular parameter is the blood pressure (BP) value.

[0085] Blood pressure can be measured using either cuff-type BP measurement technology or an optical sensor, or both. Blood pressure is measured at the wrist.

[0086] Blood pressure values ​​include at least one of systolic BP, diastolic BP, or mean BP.

[0087] The device 10 disclosed herein enables the continuous delivery of blood pressure values, for example, throughout the day and night.

[0088] The device 10 disclosed herein corresponds to a cuffless blood pressure monitoring device. Cuffless blood pressure devices solve many practical and behavioral problems, overcome barriers to recommended daily blood pressure monitoring, and have the potential to acquire significantly more blood pressure data compared to conventional methods. The ability to continuously collect blood pressure measurements over weeks, months, and years—outside of home and work, during daily activities, and while sleeping—provides patients and healthcare providers with a more complete blood pressure assessment than intermittent verification under controlled postures and environments.

[0089] Traditionally, measuring blood pressure continuously outside the workplace, day or night, was limited because available observation techniques required inflating the cuff each time a measurement was taken. Devices that do not require cuff inflation can overcome most of the limitations inherent in conventional cuff-type blood pressure monitors.

[0090] This device 10 makes it possible to determine an individual's blood pressure non-invasively without causing arterial occlusion.

[0091] The device 10 can be placed on body parts such as the wrist, fingertips, chest, ears, forehead, or a combination thereof.

[0092] Device 10 provides an indirect blood pressure estimate that relies on the analysis of arterial pulses at one or more body positions, each having a sensor that does not apply pressure at that position. Rather than providing direct pressure measurements, Device 10 supplies a quantity calculated by a computer program from the analysis of pressure pulse waveforms, which are then associated with normal blood pressure values, following an initialization phase. From an optical sensor (which evaluates the pulsation of skin arterioles via reflection or transmission photoplethysmography sensors) Camera sensor (for evaluating skin fibrillation using video reflection photoplethysmography), Biopotential sensors (which evaluate different electromagnetic signatures of cardiac activity, or assess arterial pulsation from impedance volume pulse wave recording signals in different body parts), Radar sensors (which evaluate arterial pulsation in different body parts based on radar reflections), Multiple sensor technologies (sensor unit 21), including sound pressure sensors (which assess the pulsation of superficial arteries by sensing skin displacement), are currently used to capture the waveform of pressure pulses. Depending on the number of body parts on the patient's body from which pressure pulses can be captured, waveform analysis is performed based on either a pulse wave velocity algorithm (usually when at least two body parts are involved) or a pulse wave analysis algorithm (usually when only one body part is involved). Since pressure measurement is not involved in the evaluation of such pulse wave waveforms, most cuffless blood pressure monitors require an initialization procedure that provides information in "mmHg" units using an oscillometric device.

[0093] A major advantage of this device 10 is its potential to provide a greater number of blood pressure data points. Its ability to continuously collect blood pressure values ​​over several days to several years—at home, outside the workplace, during daily activities, at night, and while sleeping—allows patients and healthcare providers to obtain a far more representative blood pressure assessment than occasional cuff estimates. A single point in time, measured in a clinic or using a home blood pressure monitor, represents only a small part of the entire dynamic blood pressure dataset. Without this data (the entire dynamic dataset), doctors and patients essentially know nothing about the true nature of blood pressure.

[0094] To illustrate the limitations mentioned above, actual systolic blood pressure (SBP) data from male subjects were recorded using device 10 over a two-month period. This data shows significant discrepancies between measurements taken in a simulated examination room, occasional home blood pressure monitoring, and blood pressure monitoring using a portable automatic blood pressure monitor. Estimates made in the doctor's office suggest significantly higher absolute systolic blood pressure values ​​than average and do not capture blood pressure data over time. This is most clearly and commonly seen in white-coat hypertension and masked hypertension (up to 40% of subjects). While home blood pressure measurements, when taken regularly, may correlate with the overall average, they cannot capture daily or circadian rhythms. Finally, portable automatic blood pressure monitors only provide information for a limited period of 24 or 48 hours. All three conventional methods of blood pressure measurement capture only a small fraction of dynamic blood pressure, as is very clearly demonstrated by device 10.

[0095] Compared to other existing technologies, device 10 can provide significantly more blood pressure measurements, better illustrate blood pressure variability, and measure nocturnal blood pressure, all of which have clinical significance. However, clinicians may wonder how the mean daytime blood pressure provided by device 10 compares to measurements taken by home blood pressure monitoring (HBPM) on the same day. To answer this question, we analyzed anonymized data from 2,928 users of device 10 offline (Figures 7a and 7b). The analysis compared daytime blood pressure data (8:00 AM to 8:00 PM) measured with device 10 on the day of the initialization procedure with data measured using upper arm cuff blood pressure measurement (as home blood pressure monitoring, HBPM) during the same initialization procedure. The analysis was repeated for both systolic and diastolic blood pressure. In all cases, the difference between the measurements from the two methods was statistically significant, as shown by paired t-tests (p<0.001 in both cases). However, the difference (systolic blood pressure 2.25 mmHg, diastolic blood pressure 0.44 mmHg) was below the resolution and error range of the automated blood pressure monitoring device.

[0096] Figures 7a and 7b compare the mean daytime systolic and diastolic blood pressure values ​​measured by device 10 with HBPM measurements taken during device initialization on the same day. Figure 7a shows the systolic and diastolic blood pressure values ​​for 2928 users of device 10, each represented by a single data point. The X-axis represents a single measurement taken with HBPM during device initialization, and the Y-axis compares this to the mean daytime blood pressure (8 AM to 8 PM) measured by device 10 on the same day. The left side shows the systolic blood pressure value (SBP), and the right side shows the diastolic blood pressure value (DBP). The dotted line is calculated using Huber linear regression. The numerical plots in Figure 7b show the distribution of the difference between the values ​​measured by device 10 and the corresponding HBPM values ​​for systolic and diastolic blood pressure. The mean and standard deviation of each distribution are shown at the bottom of the figure. An asterisk indicates a statistically significant difference between HBPM and the value measured by device 10 (both p<0.001).

[0097] Using real-world data from over 2,000 patients using device 10, the device's significantly rich blood pressure dataset, its ability to longitudinally measure nocturnal blood pressure, and its automated, passive nature offer clear advantages compared to conventional observation methods (Table 1). Furthermore, the systematic difference between the daytime mean blood pressure measured by device 10 and the daytime measurements by HBPM in this population is small and within the acceptable margin of error. In summary, device 10 has great potential to significantly improve the ability to monitor blood pressure in outpatients. [Table 1] Table 1: Guidelines for interpreting the daily average values ​​obtained from device 10 compared with the daily average values ​​obtained from HBPM.

[0098] Although its use in clinical practice is limited (see previous section), observation with a portable automatic blood pressure monitor (ABPM) remains the recommended method when a more complete analysis of a patient's blood pressure pattern is needed in the diagnosis / monitoring of hypertension. ABPM is currently the only recommended method and allows for circadian blood pressure measurement, but its limited use has raised concerns about its reproducibility. Circadian blood pressure fluctuations are known to be dynamic, and if the observation period (24 or 48 hours) is arbitrarily shortened, clinicians may obtain data that represents only a small portion of the overall blood pressure.

[0099] To further illustrate the issues with the reproducibility of ABPM, Figures 8a and 8b show two examples of repeated ABPM records from an ongoing clinical trial (bottom panel). A meta-analysis of 35 observational studies has shown that for one-third of patients, the classification as dipper or non-dipper cannot be reproduced using ABPM measurements over two consecutive nights (for example, a patient classified as a dipper on the first night but classified as a non-dipper on the following night), and that the difference between mean systolic and diastolic blood pressure over two consecutive nights can vary within the ranges of -19.6 to 21.3 mmHg and -11.3 to 12.3 mmHg, respectively. These data support the limited reproducibility of ABPM in assessing intra-individual dipping status and daytime and nighttime blood pressure values. Figures 8a and 8b show ABPM records from selected patients in ABPM studies. Figure 8a shows very poor reproducibility within the subjects, while Figure 8b shows better reproducibility in measuring nocturnal diastolic blood pressure and daytime and nocturnal mean blood pressure.

[0100] Given the documented low reproducibility of ABPM, device 10 may be able to overcome the arbitrariness of cuff-type ABPM by leveraging its ability to generate large amounts of data over long periods. However, two factors must be considered when comparing data from the two methods. First, device 10 measures blood pressure in a fundamentally different way than conventional oscillometric ABPM. Second, the frequency and cycle of ABPM measurements (e.g., every 20 minutes) differ from those of device 10 (e.g., if the user is stationary for a sufficiently long time). This difference lies in capturing blood pressure during different daily activities; ABPM captures more measurements during periods of physical activity than device 10. Therefore, these two differences (technical and activation timing) produce average blood pressure values ​​for daytime and nighttime, and these values ​​may differ depending on the measurement method.

[0101] Figures 9a to 9d provide a first overview of the systematic differences observed between ABPM and device 10 when estimating the patient's nocturnal blood pressure dipping state.

[0102] Figures 9a and 9b show the differences in estimated SBP reduction among patients in the NCT04548986 trial. Figure 9c shows the phylogenetic factor 3.4 across the entire initial patient cohort of the same trial. Figure 9d shows the phylogenetic factor 3.1 across the entire N=4644 users of device 10, when matched to the phenotypic distribution of a large independent trial (N=6359). As expected, different observational modes tend to provide similar phenotypic information, but the application of technology-dependent conversion factors is necessary.

[0103] Figures 9a and 9b show an example of simultaneous BP data obtained from one patient enrolled in the NCT04548986 trial using an ABPM monitor (Diasys 3 Plus, Novacor, France, Figure 9a) and device 10 (Aktiia BP monitor, Aktiia, Switzerland, Figure 9b). Note that ABPM data was recorded over a 24-hour period, while data from device 10 was recorded during ABPM recording and for one week after ABPM recording. The observation period using device 10 was extended to one week to account for the daily variability of the circadian pattern and to increase the number of data points registered during the day and night due to the low sampling frequency of device 10. Two estimates of blood pressure decrease were extracted on the same plot. The difference between daytime and nighttime blood pressure, a common approach, was used to calculate the decrease on the ABPM record. The daytime and nighttime sub-periods were defined based on fixed clock time intervals. Daytime was from 9:00 AM to 9:00 PM, and nighttime was from 12:00 AM to 6:00 AM. Data recorded during transition periods were excluded to avoid excessive variance between individual users. Data points located higher than the interquartile range from the median of each sub-period were considered outliers, and finally, dips were calculated as the difference between the medians of the sub-periods. A statistical approach was employed to calculate the dips recorded by device 10. Taking advantage of the fact that the cuffless circadian plot has a high density of data points, a parametric model was used to fit the circadian rhythms of SBP and DBP (see the "fitted" line of the continuum in the plot). Then, the nocturnal dip was extracted from one of the model's parameters. The estimated nocturnal dips in this patient already differ in two ways. The ABPM dip (A) appears to be larger than the cuffless dip (B).

[0104] Figure 9c shows a statistical analysis of the A>B phenomenon in the preliminary patient population of the NCT04548986 trial. Data from the initial 20 enrolled patients were processed to estimate the systematic gain difference comparing dipping (blood pressure decrease phenomenon) measured with ABPM and dipping measured with device 10. In this cohort, after bootstrapping the available sample, ABPM dipping was found to be characteristically 3.4 times greater than cuffless dipping, with a 95% confidence interval ranging from 2.3 to 4.4. It is important to note that NCT04548986 is incomplete, and a comprehensive analysis of the collected data will be published in a separate publication.

[0105] Figure 9d shows a further statistical investigation of the same A>B phenomenon, integrating data from an independent ABPM study involving N=6359 patients with real-world data from N=4644 users of device 10. According to Kario et al.'s study, the phenotype of nocturnal hypotension measured by ABPM (a phenotype is defined as an observable characteristic of the circadian rhythm of blood pressure) is expected to have the following distribution in the general population. 16% of individuals experienced an extreme dipper (drop of more than 20 mmHg), 40% of individuals experienced a normal dipper (a descent of 10 to 20 mmHg), 32% of individuals were non-dippers (drop of 0 to 10 mmHg), 12% of individuals are risers (positive dips). However, observation of the dip patterns recorded by device 10 revealed that the same distribution was not satisfied, and the dip distribution was clearly compressed. In this analysis, we estimated the optimal factors necessary to extend the dip distribution of device 10 to a representation equivalent to that of ABPM. In this cohort of N=4644 users, after applying the bootstrap method, it was found that the dip measured by ABPM was 3.1 larger than the dip measured by cuffless continuous measurement. The 95% confidence interval ranged from 2.8 to 3.4.

[0106] By integrating real-world data from over 4,000 users of device 10 with clinical data from controlled clinical trials, it has been demonstrated that device 10 can estimate a patient's blood pressure phenotype. However, due to technical differences between device 10's cuffless technology and oscillometric ABPM monitors, a technology-dependent conversion factor may be necessary to compare estimates from both methods. Table 2 provides guidance on how blood pressure phenotype data from device 10 should be interpreted in comparison to blood pressure characteristics obtained from ABPM.

[0107] Thus, the apparatus of the present invention may further include the step of converting calculated cardiovascular values, calculated cardiovascular parameters, calculated circadian plots, or calculated physiological parameters into ABPM equivalent values ​​and plots according to the mapping function. The mapping function may be a pre-calculated affine function as described above (a dip similar to ABPM can be calculated as 3.1 times the dip calculated by the apparatus 10), another type of mapping function pre-calculated from data recorded from a large population, or calculated from any of the user data described in claim 15, or a combination of both. The same mapping approach may be applied to convert the calculated cardiovascular values, calculated cardiovascular parameters, calculated circadian plots, or calculated physiological parameters according to the mapping function into HBPM equivalent values ​​and plots. Then, the ABPM equivalent or HBPM equivalent may be used in place of, or in addition to, the relevant physiological parameters in calculating the user's cardiovascular risk score. [Table 2] Table 2. Guidelines for interpreting BP phenotype data obtained from device 10 when compared with BP characteristics obtained from ABPM.

[0108] To demonstrate the potential performance of device 10, Figures 10a to 10J show a new set of dynamic BP control indices estimated for a male patient (51 years old) during a 5-month period of continuous observation with device 10.

[0109] In particular, Figures 10a to 10J show a new generation of dynamic BP control metrics that can be generated from data captured by device 10. The presented time series were captured from subjects over a period of five months. In addition to standard BP indicators such as 24-hour, daytime, and nighttime average values ​​of SBP, DBP, and HR (Figures 10a, 10c, 10e, 10f, and 10g), we present new dynamic indicators such as therapeutic time (TTR, Figure 10b), SBP variability (Figure 10d), dynamic circadian model (Figure 10h), dynamic nocturnal SBP decline (Figure 10i), and the duration of dynamic nocturnal decline and the acceleration of the morning sharp rise (Figure 10j).

[0110] Figure 10a reports all 4729 SBP measurements taken during the observation period, as well as the trend of the 24-hour average SBP. SBP measurements within the target range (less than 120 mmHg) are shown as green dots, and those outside the target range (greater than 120 mmHg) are shown as red dots. Based on this data, Figure 10b shows a new metric for SBP in BP management. This new metric represents SBP time within the target range (Y-axis quartiles are 75% to 100% time = green, 50% to 75% time = yellow, 25% to 50% time = orange, 0% to 25% time = red), with the total percentage of time spent in each quartile on the right side (in the figure). Figure 10c further displays the average nighttime SBP, with SBP > 120 mmHg colored red and SBP < 120 mmHg colored green. Figure 10d shows the medium-term blood pressure fluctuations (SD in mmHg units) during the day (orange), night (black), and mean (green). Figures 10e and 10f show the 24-hour average diastolic blood pressure (green line) and heart rate (green dots), as well as all individual data points. Figure 10g shows SBP during the day (orange), at night (black), and as a 24-hour average (green). The shaded time zones a, b, and c in the same figure correspond to Figure 10h and are also highlighted in panels I and J. Therefore, Figure 10h shows the circadian pattern of SBP in time zones a, b, and c, illustrating different patterns of nocturnal decline, duration of nocturnal decline, and morning surge in this patient. Regarding sleep-related blood pressure variables, Figure 10i shows the changes in mmHg (dark green) and % (light green) for nocturnal SBP decrease. Figure 10j shows the trend of the duration of the nighttime decline in SBP (red) and quantifies the sharp increase in the morning (orange).

[0111] The emergence of 24-hour ABPM demonstrated that blood pressure phenotypes are more complex than a simple binary variable (hypertension or not), allowing for the demonstration of diurnal blood pressure variability, including daytime and nighttime blood pressure components and nocturnal physiological blood pressure dips. Predictions of these different components were compared, and each component showed predictive values ​​regardless of absolute blood pressure. Mean nighttime blood pressure (systolic or diastolic) was a stronger predictor of cardiovascular events. Predictive values ​​of mean nighttime blood pressure (ABPM) are superior to those of mean daytime blood pressure (HBPM) in both hypertensive patient populations and the general population. This higher predictive value is noteworthy because small and recent large studies have shown low reproducibility of dipping patterns. In fact, only a small fraction of hypertensive patients maintain their initial dipping phenotype for more than four years. In addition, nocturnal ABPM is less tolerable than daytime ABPM, and may therefore affect sleep quality. Sleep disturbances caused by cuff inflation have also been shown to affect the association between nocturnal ABPM and outcomes.

[0112] Device 10 has the potential to overcome these undesirable characteristics of nocturnal ABPM. First, nocturnal blood pressure can be repeatedly measured over several days to several months, allowing for the derivation of a more consistent nocturnal phenotype. Second, by not inflating the cuff, the impact on sleep quality is expected to be negligible. Nevertheless, these unique characteristics may affect the normal range of nocturnal cuffless blood pressure and may need to be redefined.

[0113] To encourage the exploration of new phenotype-driven assessments of cardiovascular abnormality risk, Table 3 lists existing promising blood pressure phenotypes that can be promoted by the large-scale deployment of device 10. [Table 3] Table 3 lists recommendations for BP phenotypes that can already be identified by HBPM / ABPM screening, and lists enhancements to BP phenotypes that will be improved by the deployment of device 10.

[0114] Before we can be confident that they can be used in routine clinical practice, the usefulness and predictive value of classical and novel phenotypes must be demonstrated in longitudinal epidemiological studies. For evidence-based individualized treatment to become a reality, independent predictors of 24-hour mean or nocturnal blood pressure are needed. While research into these phenotypes will take time, it will certainly provide physicians with a new panel of physiological or induced blood pressure responses, which could potentially help in the individualization of antihypertensive treatment in the future.

[0115] The disclosure further relates to a non-temporary computer-readable storage medium comprising a computer program product that includes instructions for causing at least one processing unit to execute a method for determining a user's cardiovascular risk score. This application offers, for example, the following perspectives. [Perspective 1] To provide a device (10) that measures at least the cardiovascular signals of a user, Measuring at least the cardiovascular signals during an observation period (Tm) having a duration of at least 24 hours, The aforementioned observation period (Tm) is divided into at least one observation interval (Ts), The measurement of at least one observation interval (Ts) having a duration of 24 hours and comprising multiple measurement periods, and the measurement of signals from at least the cardiovascular system, For each of the multiple measurement periods within the at least one observation period (Ts), the cardiovascular values ​​are determined, For each observation interval of the aforementioned at least one observation interval (Ts), the cardiovascular values ​​determined during a predetermined measurement period are collected into a group of cardiovascular parameters. For each of the at least one observation period (Ts) mentioned above, a 24-hour circadian plot of the cardiovascular parameter group is created for the corresponding measurement period. The calculation of multiple physiological parameters of the user from the aforementioned circadian plot, Calculate the user's cardiovascular risk score from the determined physiological parameters. A method for providing this. [Perspective 2] The observation period (Tm) is the method described in Viewpoint 1, having a duration of 48 hours, 7 days, 1 month, or 1 year. [Perspective 3] The method according to viewpoint 1 or 2, wherein the measurement period has a duration of at least 10 seconds or 30 seconds, i.e., 10 seconds, 30 seconds, 1 minute, 5 minutes, 1 hour, 2 hours, 4 hours, or 6 hours. [Perspective 4] The method described above is The method according to any one of viewpoints 1 to 3, further comprising calculating a representative value of the cardiovascular system from the cardiovascular values ​​collected in each group of cardiovascular parameters. [Perspective 5] Calculating the aforementioned representative values ​​for the cardiovascular system is The aforementioned cardiovascular parameters are classified as inliers or outliers, The representative values ​​of the cardiovascular system are calculated using only the cardiovascular parameters of the inlya. The method described in viewpoint 4, comprising the features of the method described in viewpoint 4. [Perspective 6] Calculating the aforementioned representative values ​​for the cardiovascular system is The system includes classifying the cardiovascular parameters as inliers or outliers, The method according to viewpoint 5, wherein the cardiovascular parameters are weighted according to the probability of in-lie or out-lie. [perspective 7] Determining multiple physiological parameters of the aforementioned user is The method according to any one of views 1 to 6, comprising the step of fitting the model to the circadian plot, wherein the multiple physiological parameters correspond to multiple parameters of the model. [Perspective 8] The method described in perspective 7, wherein the model is one of a linear model, a nonlinear model, a constrained model, or an unconstrained model fitted by least-squares optimization. [Perspective 9] The aforementioned physiological parameters are the daytime blood pressure value (Y 0 ), absolute dip amplitude (ampl) at night, time element (X) of dip onset 0 The method according to any one of viewpoints 1 to 8, comprising one of the following: the duration of the pre-nighttime ramp (dl), the duration of the dip plateau (nl), or the duration of the post-nighttime ramp (al). [Perspective 10] The method described above, which further uses several non-physiological parameters, as described in any one of views 7 to 9. [Perspective 11] The method according to any one of views 1 to 10, further comprising combining at least two of the physiological parameters to obtain one or more related physiological parameters. [Perspective 12] The aforementioned one or more related physiological parameters include: Daytime blood pressure, nighttime blood pressure, relative nighttime dip amplitude, duration of nighttime dip, time within target range (TTR), Blood pressure fluctuation patterns, the slope of the morning sharp rise, nighttime systolic blood pressure (SBP), nighttime diastolic blood pressure (DBP), nighttime heart rate (HR), SBP decreased, DBP decreased, HR decreased, morning sharp rise in SBP, morning sharp rise in DBP. Morning surge in HR, SBP decline time, DBP decline time, HR decline time, synchronization of SBP or DBP or HR, Response to the type of medication, adherence to blood pressure medication, indicators of patient engagement, or response to lifestyle modification instructions. The method described in perspective 11, which includes any of the following. [Perspective 13] The method according to viewpoint 11 or 12, wherein the relevant physiological parameters include the user's blood pressure phenotype. [Perspective 14] The aforementioned related physiological parameters include: Normal blood pressure, white-coat hypertension, masked hypertension, persistent hypertension, hypotension, nocturnal dip, nocturnal hypertension, or phenotypes that predict a response to a specific drug or therapy. The method described in perspective 13, which includes any of the following. [Perspective 15] The method of any one of perspectives 1 to 14, comprising using user data, to calculate the user's cardiovascular risk score. [Perspective 16] The aforementioned user data includes: Age, weight, height, sex, ethnicity, lipid levels, diabetes status, smoking, CT calcium (Agaston score), Family history, genetic markers related to disease risk, actigraphy information, exercise information, dietary information, Stress level, general feeling, hormone data, menstrual cycle information, Medication information, weekday or weekend information, seasonal information, sleep quality information, bedtime patterns, Any of the multiple parameters used to calculate cardiovascular risk scores in clinical guidelines such as the American College of Cardiology (ACC / AHA) guidelines, the European Society of Cardiology (ESC) guidelines, or the Multiethnic Atherosclerosis Study (MESA) database, The method described in Perspective 15, which includes any of the following. [Perspective 17] The method according to any one of perspectives 1 to 16, wherein the cardiovascular risk score is one of the following: 10-year risk for cardiovascular disease, 10-year risk for heart disease, 10-year risk for stroke, or other clinically relevant cardiovascular risk score. [Perspective 18] The method according to any one of viewpoints 1 to 17, wherein the cardiovascular parameters are at least one of the following: blood pressure, heart rate, cardiac output, blood glucose level, physical activity scale, sleep volume and sleep quality scale, electrocardiogram signal, photoplethysmography signal, bioimpedance signal, and ultrasound signal. [Perspective 19] A measurement module (20) is configured to measure the cardiovascular signals of a user during an observation period (Tm) lasting at least 24 hours, wherein the observation period (Tm) is divided into multiple observation intervals (Ts), and each observation interval (Ts) consists of multiple measurement periods (Tm) and lasts for 24 hours, and the measurement module (20) A processing device (30) that determines cardiovascular values ​​for each measurement period (Tm), The processing device collects multiple cardiovascular values ​​determined for the corresponding measurement period (Tm) of each observation interval (Ts) and assembles them into a group of cardiovascular parameters. A 24-hour circadian plot of the cardiovascular parameter group against the corresponding measurement period (Tm) is created. The circadian plot is used to determine multiple physiological parameters of the user. The processing device (30) uses the physiological parameters determined above to calculate the user's cardiovascular risk score, An interface (40) for displaying and / or transmitting the calculated cardiovascular risk score, and A device that determines the user's cardiovascular risk score. [perspective 20] The device according to viewpoint 19, wherein the interface (40) comprises a smartphone, tablet, computer, smartwatch, or portable device. [Perspective 21] The device according to viewpoint 19 or 20, wherein the device is connectable to a wired communication line or a wireless communication line including WiFi®, Bluetooth®, or cellular support. [Perspective 22] A non-temporary computer-readable storage medium comprising a computer program product that includes instructions to cause at least one processing unit to perform a method described in any one of viewpoints 1 to 18.

Claims

1. A non-temporary computer-readable storage medium comprising a computer program product including instructions for causing at least one processing unit to execute a method, wherein the method is To provide a device that measures at least the cardiovascular signals of the user, Measuring the signals of at least the cardiovascular system during an observation period having a duration of at least 48 hours, The aforementioned observation period is divided into more than one observation interval, Each of the more than one observation intervals has a duration of 24 hours and comprises multiple measurement periods, wherein the signals of at least the cardiovascular system are measured. The cardiovascular values ​​are determined for each of the multiple measurement periods within each of the more than one observation intervals. For each observation interval of the aforementioned observation interval (more than one), the cardiovascular values ​​determined during a predetermined measurement period are collected into a cardiovascular parameter group. By plotting the relationship between time period and cardiovascular parameter groups for each measurement period, a single 24-hour circadian plot is constructed from multiple cardiovascular parameter groups collected over multiple 24-hour observation intervals. The calculation of multiple physiological parameters of the user from the aforementioned single circadian plot, To calculate the user's cardiovascular risk score from the calculated physiological parameters. Equipped with, The cardiovascular values ​​are at least one of the following: systolic blood pressure, diastolic blood pressure, and mean blood pressure. Calculating multiple physiological parameters of the user is The process includes the step of fitting the model to the single circadian plot, wherein the multiple physiological parameters correspond to multiple parameters of the model. The aforementioned model is one of the following: a linear model, a nonlinear model, a constrained model, or an unconstrained model, which is fitted by least-squares optimization. A non-temporary computer-readable storage medium in which the physiological parameters include one of the following: daytime blood pressure value, nocturnal absolute dip amplitude, time element of dip onset, duration of pre-nocturnal ramp, duration of dip plateau, or duration of post-nocturnal ramp.

2. The non-temporary computer-readable storage medium according to claim 1, wherein the observation period has a duration of 48 hours, 7 days, 1 month, or 1 year.

3. The non-temporary computer-readable storage medium according to claim 1, wherein the measurement period has a duration of at least 10 seconds or 30 seconds, i.e., 10 seconds, 30 seconds, 1 minute, 5 minutes, 1 hour, 2 hours, 4 hours, or 6 hours.

4. The method described above is A non-temporary computer-readable storage medium according to claim 1, comprising calculating a representative value of the cardiovascular system from the cardiovascular values ​​collected in each group of cardiovascular parameters.

5. Calculating the aforementioned representative values ​​for the cardiovascular system is The aforementioned cardiovascular parameters are classified as inliers or outliers, The representative values ​​of the cardiovascular system are calculated using only the cardiovascular parameters of the inlya. A non-temporary computer-readable storage medium according to claim 4, comprising the above.

6. Calculating the aforementioned representative values ​​for the cardiovascular system is The non-temporary computer-readable storage medium according to claim 4, comprising classifying the cardiovascular parameters as inliers or outliers.

7. The non-temporary computer-readable storage medium according to claim 1, further comprising the method of combining at least two of the physiological parameters to obtain one or more related physiological parameters.

8. The aforementioned one or more related physiological parameters include: Daytime blood pressure values, nighttime blood pressure values, relative nighttime dip amplitude, duration of nighttime dip, time within target range (TTR), Blood pressure fluctuation patterns, the slope of the morning sharp rise, nighttime systolic blood pressure (SBP), nighttime diastolic blood pressure (DBP), nighttime heart rate (HR), SBP decrease, DBP decrease, HR decrease, sharp rise in SBP in the morning, sharp rise in DBP in the morning, Morning surge in HR, SBP decline time, DBP decline time, HR decline time, synchronization of SBP or DBP or HR A non-temporary computer-readable storage medium according to claim 7, comprising any of the following:

9. The non-temporary computer-readable storage medium according to claim 7, wherein the relevant physiological parameters include the user's blood pressure phenotype.

10. The aforementioned related physiological parameters include: True normal blood pressure, white-coat hypertension, masked hypertension, persistent hypertension, hypotension, nocturnal dip, nocturnal hypertension A non-temporary computer-readable storage medium according to claim 9, comprising any of the following:

11. The non-temporary computer-readable storage medium according to claim 1, wherein the cardiovascular risk score is any of the following: a 10-year risk of cardiovascular disease, a 10-year risk of heart disease, a 10-year risk of stroke, or any other clinically relevant cardiovascular risk score.

12. The non-temporary computer-readable storage medium according to claim 1, wherein the cardiovascular parameters of the cardiovascular parameter group are at least one of the following: blood pressure value, heart rate value, cardiac output value, blood glucose level, measure of physical activity, or measure of sleep volume and sleep quality, electrocardiogram signal, photoplethysmography signal, bioimpedance signal, and ultrasound signal.

13. A measurement module that measures the cardiovascular signals of a user during an observation period lasting at least 48 hours, wherein each observation period is divided into one or more observation intervals, and each of the more than one observation intervals consists of multiple measurement periods and lasts for 24 hours; A processing device that determines cardiovascular values ​​for each measurement period within multiple measurement periods within one or more observation intervals, The processing device further collects multiple cardiovascular values ​​determined for the corresponding measurement period for each of the more than one observation intervals to form a set of cardiovascular parameters. The processing device further plots the relationship between the time period and the cardiovascular parameter group for each measurement period, thereby constructing a single 24-hour circadian plot from multiple cardiovascular parameter groups collected over multiple 24-hour observation intervals. The single circadian plot is used to calculate multiple physiological parameters of the user from the single circadian plot. The physiological parameters calculated above are used to calculate the user's cardiovascular risk score. The processing apparatus and A device for determining a user's cardiovascular risk score, wherein the device for determining the user's cardiovascular risk score is A device for determining a user's cardiovascular risk score, further comprising an interface for displaying and / or transmitting the calculated cardiovascular risk score, The cardiovascular values ​​are at least one of the following: systolic blood pressure, diastolic blood pressure, and mean blood pressure. Calculating multiple physiological parameters of the user is The process includes the step of fitting the model to the single circadian plot, wherein the multiple physiological parameters correspond to multiple parameters of the model. The aforementioned model is one of the following: a linear model, a nonlinear model, a constrained model, or an unconstrained model, which is fitted by least-squares optimization. The aforementioned physiological parameters include one of the following: daytime blood pressure, nocturnal absolute dip amplitude, dip onset time, duration of pre-nocturnal ramp, duration of dip plateau, or duration of post-nocturnal ramp. A device for determining the cardiovascular risk score of the aforementioned user.

14. The apparatus according to claim 13, wherein the interface comprises an application located on a smartphone, tablet, computer, smartwatch, or portable device.

15. The apparatus according to claim 13, wherein the apparatus is connectable to a wired communication line or a wireless communication line including Wi-Fi®, Bluetooth®, or cellular support.

Images