Inhaler system

Abstract

To provide a system for calculating a probability (or likelihood) of a respiratory disease exacerbation in a subject.SOLUTION: The system comprises an inhaler device for administering a medicament to a subject. The drug is, for example, an emergency drug or a maintenance drug. The inhaler device has a usage detection system configured to measure inhalation performed by the subject using the inhaler device. A sensor system is configured to measure a parameter related to airflow during inhalation. The user interface enables user input of an indication representative of a respiratory disease condition experienced by the subject. A processor is configured to calculate a probability of respiratory disease exacerbation based on the recorded inhalations from the usage detection system, the parameter values received from the sensor system and the indication received from the user interface. Further provided is a method for calculating the probability of respiratory disease exacerbation in a subject.SELECTED DRAWING: Figure 2

Description

[Technical field]

[0001] The present invention relates to inhaler systems, and more particularly to systems and methods for calculating the probability of a respiratory exacerbation. [Background technology]

[0002] Many respiratory diseases, such as asthma or chronic obstructive pulmonary disease (COPD), are lifelong illnesses where treatment involves managing the patient's symptoms and lifelong administration of medications to reduce the risk of irreversible disease. Currently, there is no cure for diseases such as asthma and COPD. Treatment takes two forms. First, the maintenance treatment aspect is intended to reduce inflammation in the airways and therefore control symptoms in the future. Maintenance treatment is typically achieved by prescribing inhaled corticosteroids alone or in combination with long-acting bronchodilators and / or muscarinic antagonists. Second, treatment also has an emergency (or rescue) treatment aspect, where the patient is given fast-acting bronchodilators to relieve severe attacks of asthma, coughing, chest tightness and difficulty in breathing. Patients with respiratory diseases such as asthma or COPD may also experience temporary flare-ups or exacerbations that make the symptoms of their respiratory disease worse. In the worst cases, the exacerbations can be life-threatening to the patient.

[0003] The ability to identify impending respiratory exacerbations may allow for improved planning of action and provide the opportunity for preemptive treatment, such as unscheduled visits to or from a physician, hospitalization, and systemic steroids, before the patient's symptoms require it.

[0004] Therefore, there is a need in the art for improved methods for identifying the risk of an impending respiratory exacerbation. Summary of the Invention

[0005] Accordingly, the present invention provides a system for calculating a probability of a respiratory exacerbation in a subject, comprising: an inhaler device for administering a medicament to a subject, the inhaler device having a usage detection system configured to measure inhalations performed by the subject using the inhaler device; a sensor system configured to measure a parameter related to airflow during inhalation; a user interface configured to receive an input of an indicator representative of a respiratory disease symptom experienced by the subject; Record each inhalation, receiving a parameter measured during inhalation; Receive the indicators, and a processor configured to calculate a probability of a respiratory exacerbation based on the recorded inhalation and received parameter measurements and the received indicators.

[0006] By using measurements of parameters relating to the number of inhalations and airflow during inhalation, together with an index representative of the respiratory disease symptoms experienced by the subject, it is possible to accurately predict respiratory disease exacerbations. [Brief description of the drawings]

[0007] [Figure 1] FIG. 1 is a block diagram of a system according to one embodiment of the present invention. [Diagram 2] FIG. 2 illustrates a system according to another embodiment of the present invention. [Figure 3A] FIG. 2 is a flow diagram of a method according to one embodiment of the present invention. [Figure 3B] 4 is a flow diagram and timeline of a method according to another embodiment of the present invention. [Figure 4] FIG. 1 illustrates a timeline for inhalation of rescue medication. [Diagram 5] Graph of mean number of rescue inhalers versus days since asthma exacerbation. [Figure 6]1 is another graph of the mean number of rescue inhalers versus days since asthma exacerbation. [Figure 7] 1 shows four graphs of percent change from baseline in various rescue inhalation numbers and airflow parameters versus days since asthma exacerbation. [Figure 8] FIG. 1 shows a receiver operating characteristic (ROC) curve analysis of a model for calculating the probability of an asthma exacerbation. [Figure 9] Graph of mean number of rescue inhalations versus days since COPD exacerbation. [Figure 10] Another graph shows the mean number of rescue inhalations versus days since a COPD exacerbation. [Figure 11] Graph of mean peak inhalation flow (L / min) versus days since COPD exacerbation. [Figure 12] FIG. 1 is another graph of mean peak inhalation flow (L / min) versus days since COPD exacerbation. [Figure 13] Graph of mean inhaled dose (L) versus days since COPD exacerbation. [Figure 14] FIG. 1 is another graph of mean inhaled dose (L) versus days since COPD exacerbation. [Figure 15] Graph of mean inhalation duration (s) versus days since COPD exacerbation. [Figure 16] FIG. 1 is another graph of mean inhalation duration (s) versus days since COPD exacerbation. [Figure 17] FIG. 1 shows a Receiver Operating Characteristic (ROC) curve analysis of a model for calculating the probability of impending COPD exacerbation. [Figure 18] FIG. 1 is a perspective view of an inhaler. [Figure 19] FIG. 19 is a longitudinal sectional view of the inhaler shown in FIG. [Figure 20] FIG. 19 is an exploded perspective view of the inhaler shown in FIG. 18. [Figure 21] FIG. 19 is an exploded perspective view of the top cap and electronics module of the inhaler shown in FIG. 18. [Figure 22] 19 is a graph of airflow versus pressure for the inhaler shown in FIG. 18. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0008] It should be understood that the detailed description and specific examples given while illustrating the device, system and method are merely for the purpose of illustrating the present invention and are not intended to limit the scope of the present invention. These and other features, aspects and advantages of the device, system and method of the present invention will be better understood from the following description of the specification, the appended claims and the drawings. It should be understood that the drawings are only schematic and are not drawn to scale. It should also be understood that the same or similar components are numbered the same throughout the drawings.

[0009] Asthma and COPD are chronic inflammatory diseases of the airways. Both are characterized by variable and relapsing episodes of airway obstruction and bronchial paralysis. Symptoms include attacks of wheezing, coughing, chest tightness and dyspnea.

[0010] Symptoms are controlled by avoiding triggers and using medications, especially inhaled medications, which include inhaled corticosteroids and inhaled bronchodilators.

[0011] Inhaled corticosteroids (ICS) are steroid hormones used for long-term control of respiratory diseases. They work by reducing airway inflammation. Inhaled corticosteroids include, for example, budesonide, beclomethasone (beclomethasone dipropionate), fluticasone (fluticasone propionate), mometasone (mometasone furoate), ciclesonide, and dexamethasone (dexamethasone sodium). Parentheses represent the preferred salt or ester forms.

[0012] Different classes of bronchodilators target different receptors in the airways. Two commonly used classes are β2-agonists and anticholinergics.

[0013] Beta 2 adrenergic agonists (beta 2 agonists) act on adrenergic receptors inducing smooth muscle relaxation and dilating the airways. Long-acting beta 2 agonists (LABAs) include, for example, formoterol (formoterol fumarate), salmeterol (salmeterol xinafoate), indacaterol (indacaterol maleate), bambuterol (bambuterol hydrochloride), clenbuterol (clenbuterol hydrochloride), olodaterol (olodaterol hydrochloride), carmoterol (carmoterol hydrochloride), tulobuterol (tulobuterol hydrochloride) and vilanterol (vilanterol triphenylacetate). Short-acting beta 2 agonists (SABAs) include, for example, albuterol (albuterol sulfate).

[0014] Typically, short-acting bronchodilators provide quick relief from acute bronchoconstriction (often called "rescue" or "reliever" drugs), while long-acting bronchodilators help control and prevent symptoms over a longer period of time. However, some of the fast-onset, long-acting bronchodilators, such as formoterol fumarate, can be used as rescue medications. That is, rescue medications provide relief from acute bronchoconstriction. Rescue medications are taken as needed. Rescue medications can also take the form of combination products, such as ICS-formoterol fumarate, typically budesonide-formoterol fumarate hydrate inhalants. Thus, rescue medications preferably consist of SABAs or fast-onset LABAs, more preferably albuterol sulfate or formoterol fumarate, even more preferably albuterol sulfate.

[0015] Albuterol (also known as salbutamol), typically administered as the sulfate salt, is the preferred rescue medication in the present invention.

[0016] Anticholinergics (antimuscarinics) block the neurotransmitter acetylcholine by selectively blocking its receptors in nerve cells. Upon topical application, anticholinergics act primarily on the M3 muscarinic receptors located in the airways, causing smooth muscle relaxation and bronchodilation. Long-acting muscarinic antagonists (LAMAs) include, for example, tiotropium (tiotropium bromide), oxitropium (oxitropium bromide), aclidinium (aclidinium bromide), ipratopium (ipratopium bromide), glycopyrronium (glycopyrronium bromide), oxybutynin (oxybutynin hydrochloride or oxybutynin hydrobromide), tolterodine (tolterodine tartrate), trospium (trospium chloride), solifenacin (solifenacin succinate), fesoterodine (fesoterodine fumarate), and darifenacin (darifenacin hydrobromide).

[0017] To date, there have been a number of approaches to preparing and formulating these medications for administration by inhalation, such as with dry powder inhalers (DPIs), pressurized metered dose inhalers (pMDIs) and nebulizers.

[0018] According to the Global Initiative for Asthma (GINA) guidelines, a stepwise approach is taken for the treatment of asthma. In the first stage, representing a mild form of asthma, the patient is given an on-demand SABA, such as albuterol sulfate. The patient may also be given low-dose ICS-formoterol or low-dose ICS on-demand whenever a SABA is prescribed. In the second stage, a regular low-dose ICS is given together with the SABA or low-dose ICS-formoterol on-demand. In the third stage, a LABA is added. In the fourth stage, the dose of each drug is increased, and in the fifth stage, additional treatments such as anticholinergics or low-dose oral corticosteroids are included. Thus, each stage can be considered a treatment plan, with each treatment being structured according to the degree of acute severity of the respiratory disease.

[0019] COPD is a leading cause of death worldwide. It is a heterogeneous, long-term disease that includes chronic bronchitis and emphysema as well as narrowed airways. In patients with COPD, lesions are primarily confined to the airways, lung parenchyma, and pulmonary vasculature. These lesions reduce the lungs' healthy ability to inhale and expel gas.

[0020] Bronchitis is characterized by long-term inflammation of the airways. Common symptoms include wheezing, dyspnea, coughing, and expectoration of sputum, all of which are bothersome and detrimental to the patient's quality of life. Emphysema also involves long-term bronchial inflammation, where the response to inflammation results in destruction of lung tissue and progressive narrowing of the airways. Over time, the lung tissue loses its natural elasticity and expands. Gas exchange is then compromised, and breathed gases are often trapped in the lungs. This causes localized hypoxia, reducing the amount of oxygen delivered to the patient's bloodstream per inspiration. The patient then experiences difficulty breathing.

[0021] Patients living with COPD experience a variety of symptoms, some of which may be present on a daily basis. The severity of the disease is determined by various factors, but most commonly correlates with the progression of the disease. The symptoms, regardless of the severity of the disease, represent stable COPD, which is maintained and controlled through the administration of various medications. Treatments may vary, but often include inhaled bronchodilators, anticholinergics, long-acting and short-acting beta-2 agonists, and inhaled corticosteroids. Medications are often administered as monotherapy or in combination therapy.

[0022] Patients are classified according to the severity of their COPD using categories defined in the Global Initiative for Chronic Obstructive Lung Disease, Inc. (GOLD) guidelines. The categories consist of A-D, and the recommended first choice of treatment varies from category to category. Patient group A is prescribed a short-acting muscarinic antagonist (SAMA) or a short-acting beta2 agonist (SABA) as needed. Patient group B is prescribed a long-acting muscarinic antagonist (LAMA) or a long-acting beta2 agonist (LABA). Patient group C is prescribed an inhaled corticosteroid (ICS) + LABA, or a LAMA. Patient group D is prescribed an ICS + LABA, and / or a LAMA.

[0023] Patients with respiratory diseases such as asthma or COPD suffer from periodic exacerbations that go beyond the basic day-to-day changes in their condition. Exacerbations are acute worsening of the condition that require additional treatment, that is, treatment that goes beyond the scope of maintenance treatment.

[0024] For asthma, additional treatment for moderate exacerbations consists of repeated doses of SABAs and oral corticosteroids and / or controlled oxygen inhalation (the latter requiring hospitalization). For severe exacerbations, anticholinergics (typically ipratopium bromide), nebulized SABAs or magnesium sulfate are added.

[0025] For COPD, additional treatment for moderate exacerbations consists of repeated doses of SABAs, oral corticosteroids and / or antibiotics. For severe exacerbations, controlled oxygen inhalation and / or respiratory support (both of which require hospitalization) are added.

[0026] As used herein, the term "exacerbation" includes both moderate and severe exacerbations.

[0027] The present invention relates to a therapeutic approach that predicts respiratory disease exacerbations, thereby allowing for earlier intervention in patient treatment and thereby improving patient outcomes.

[0028] According to the present invention, a system for calculating the probability (or likelihood) of a respiratory disease exacerbation in a subject is provided. The system comprises an inhaler device for administering a medication to the subject. The medication is, for example, a rescue or maintenance medication. The rescue medication is suitable for treating a respiratory disease exacerbation, for example by rapidly dilating the bronchi and bronchioles upon inhalation of the medication. The inhaler device has a usage detection system configured to measure an inhalation made by the subject with the inhaler device. The sensor system is configured to measure a parameter related to the airflow during inhalation. The user interface allows a user input of an indicator representative of a respiratory disease condition experienced by the subject. The processor is configured to calculate the probability of a respiratory disease exacerbation based on the recorded inhalations from the usage detection system, and the parameter values ​​received from the sensor system, and the indicators received from the user interface. The preferred embodiments described with respect to this system may be applied to the method according to the present invention, or vice versa.

[0029] The inhaler device has a first inhaler for administering a rescue medication to the subject, and correspondingly, the usage detection system is configured to meter inhalation of the rescue medication.

[0030] Alternatively or additionally, the inhaler device includes a second inhaler for administering a maintenance medication to the subject, and correspondingly, the usage detection system is configured to meter inhalation of the maintenance medication.

[0031] The sensor system is configured to measure a parameter during inhalation of the rescue medication and / or the maintenance medication.

[0032] Rescue medications are as described above and typically consist of a SABA or a fast-onset LABA such as formoterol fumarate. Rescue medications also come in the form of combination products, such as ICS-formoterol fumarate, typically budesonide-formoterol phthalate. Such an approach is called "MART" (maintenance and rescue therapy). However, the presence of a rescue medication represents the first inhaler herein, since its presence is crucial in the weighted model. It therefore covers both rescue medication and a combination of rescue and maintenance medications. In contrast, the second inhaler, if used, is only used in the maintenance aspect of treatment and not for rescue purposes. The key difference is that the first inhaler is used as needed, whereas the second inhaler is used on a regular, predetermined basis.

[0033] The system further comprises a processor configured to, for example, measure or record the number of inhalations within a first period of time. Thus, the number of rescue inhalations and / or the withdrawal of regular inhalations are measured. The number of rescue inhalations is a factor in predicting an exacerbation, because the closer to the occurrence of an exacerbation, the more the subject will use the first inhaler.

[0034] Alternatively or additionally, the frequency of regular inhalations provides useful information for predicting exacerbations, since a decrease in the frequency of maintenance inhalations (indicating poor adherence to the maintenance treatment regimen) increases the risk of exacerbations.

[0035] In a relatively simple example, an increase in the number of rescue inhalations using a first inhaler (relative to a baseline period for the subject in question) and / or a decrease in the number of inhalations using a second inhaler (representing reduced compliance with the treatment regimen), together with inhalation parameters indicative of reduced lung function, would result in a higher probability of a respiratory disease exacerbation.

[0036] A parameter relating to airflow during inhalation is indicative of an impending exacerbation because the parameter serves as a proxy for the subject's lung function and / or lung health.

[0037] The respiratory disease symptoms as experienced by the subject provide useful diagnostic information.For example, the respiratory disease symptoms as experienced contemporaneously by the subject or in other ways verify that the risk of exacerbation represented by other factors, such as the number of inhalations and / or inhalation parameters, is properly calculated.In this way, the indicator of respiratory disease symptoms improves the accuracy of exacerbation prediction relative to predictions based on the number of inhalations and inhalation parameters but ignoring the respiratory disease symptoms experienced by the subject.

[0038] Attempts have been made to assess the risk of impending respiratory disease exacerbation, such as asthma exacerbation or COPD exacerbation, by monitoring various subject-related and environmental factors. Challenges have been made as to which factors should be taken into account and which should be ignored. Ignoring factors that have a small or negligible effect on the risk assessment allows for a more efficient risk calculation, for example using less processing resources, battery power, memory capacity, and other computational resources. More importantly, the accuracy of the calculation of the probability of impending respiratory disease exacerbation is improved. More accurate risk assessment can lead to a more effective alarm system, which can result in more appropriate medical intervention for the subject. That is, a more accurate assessment of exacerbation risk has the potential to guide intervention in the subject at the time of urgent risk.

[0039] If the probability of exacerbation is high, a gradual change in the treatment plan may be justified, for example, to the therapy that was configured for the subject at the time of acute risk. Alternatively, if the probability of exacerbation is low over the long term, a more accurate probability calculation can be used as a guide to justify a downgrade or even withdrawal of the current therapy. This means, for example, that the subject is no longer required to take high doses of medication that are out of proportion to the condition of his or her respiratory disease.

[0040] The inventors have conducted extensive clinical research and have found that the accuracy of calculating the probability of a respiratory exacerbation can be improved by using a model that bases the calculation of the probability of an exacerbation on both the number of inhalations of medication performed by a subject and (measurements of) parameters related to airflow during inhalation of the medication, as described in detail below.

[0041] The number of inhalations is recorded, for example, over a first period of time. The first period corresponds to a sampling period during which the number of inhalations is measured. The first period is, for example, 1-15 days. The sampling period is selected such that it is long enough to allow for the collection of data on the number of inhalations, for example rescue inhalations. A sampling period that is too short may not allow the collection of the amount of data required for a reliable prediction of an exacerbation, whereas a sampling period that is too long may result in averaging effects that make it difficult to distinguish short-term trends that are meaningful for diagnosis or prognosis.

[0042] By using both the number of inhalations and the parameters (values), a model can be formed that allows for more accurate predictions than a model that ignores, for example, either one of these factors. As will be explained in more detail below, depending on the type of respiratory disease, e.g., asthma or COPD, the number of inhalations may be more significant or less significant than the inhalation parameters in calculating the probability of an exacerbation.

[0043] From clinical studies, it was found that the number of rescue inhalations and the tendency for rescue inhaler use were more significant in calculating the probability of an asthma exacerbation than parameters related to airflow during inhalation. Although the parameters are still significant factors in calculating the probability of an asthma exacerbation, they have a smaller effect on the probability compared to the number of rescue inhalations. Therefore, by weighting the predictive model such that the number of rescue inhalations is more significant than the parameters in the probability calculation, the accuracy of the probability calculation is improved.

[0044] A model for asthma, for example, may have a first weighting factor related to the number of rescue inhalations and a second weighting factor related to the parameter, and when standardized to account for the different units used to quantify the number of rescue inhalations (or the tendency for rescue medication use) and the parameter, the first weighting factor will be greater than the second weighting factor, thereby ensuring that the number of rescue inhalations is more significant than the parameter in the probability calculation.

[0045] The probability calculation is based in part on the number of rescue inhalations. Basing the probability calculation on the number of rescue inhalations means that the model uses the absolute number of rescue inhalations in the first time period and / or one or more trends based on the number of rescue inhalations. Such trends consist of fluctuations in the number of rescue inhalations, rather than the number of rescue inhalations per se.

[0046] Trends based on the number of rescue inhalations may include, for example, the number of inhalations taken during a particular time of day. Thus, for example, the number of inhalations taken during the night may be included as a factor in the number of inhalations. The processor may, for example, include a suitable clock function to record the use of rescue medication during such times of day.

[0047] The first weighting factor weights one or more trends based on the absolute number of rescue inhalations and / or the frequency of rescue inhalations.

[0048] More generally, for asthma exacerbation prediction models, the number of rescue inhalations (e.g. including any associated trends) has a significance / importance (e.g. weight) in the model (relative to other factors) of 40% to 95%, preferably 55% to 95%, more preferably 60% to 85%, most preferably 60% to 80%, for example about 60% or about 80%.

[0049] The calculation of the probability of asthma exacerbation is also based on a parameter related to airflow during rescue inhalation and / or regular inhalation with the second inhaler, if the second inhaler is provided.The parameter corresponds to a single factor related to airflow during inhalation, or includes a plurality of such factors.For example, the parameter is at least one of the following: maximum inhalation flow rate, inhalation volume, inhalation duration, and inhalation rate.The maximum inhalation flow rate versus time, for example, provides a measure of inhalation rate.

[0050] Basing the calculation of the probability of an asthma exacerbation on parameters means that the model uses one or more factors related to airflow during inhalation and / or one or more trends related to each factor, such trends corresponding to the variability of each factor.

[0051] The second weighting factor weights one or more factors related to airflow during inhalation and / or one or more trends related to each factor.

[0052] More typically, the inhalation parameters (e.g. including any relevant trends) have a significance / importance (e.g. weight) in the model of between 2% and 49% or between 2% and 30%, preferably between 2% and 45%, more preferably between 5% and 40%, most preferably between 10% and 35%, for example about 10% or about 35%.

[0053] The probability of an asthma exacerbation is the probability of an impending asthma exacerbation occurring within an exacerbation period following a first period. Thus, the model allows the calculation of the probability of an asthma exacerbation occurring within a predetermined period, called the "exacerbation period", following a first period during which inhalation data, i.e. the number of rescue inhalations and parameter data, are collected. The exacerbation period may be, for example, 1-10 days, e.g., 5 days. The exacerbation period is selected based on the ability of the model to predict an exacerbation within that period, ensuring that the predetermined period is long enough to take appropriate therapeutic steps, if necessary.

[0054] In some embodiments, biometric parameters are included in the asthma exacerbation prediction model to improve the accuracy of the weighted model. In such embodiments, the processor is configured to receive, for example, biometric parameters (parameter values). A data input unit is included in, for example, the system, to allow the subject and / or a medical professional to input the biometric parameters.

[0055] The asthma exacerbation prediction model is, for example, weighted so that the parameters related to the organism have lower significance in the probability calculation than the number of rescue inhalations. In other words, a third weighting factor is associated with the parameters related to the organism, and the third weighting factor is set to be smaller than the first weighting factor associated with the number of rescue inhalations. The third weighting factor is set to be larger or smaller than the second weighting factor associated with the parameters related to airflow.

[0056] In the asthma exacerbation prediction model, preferably, the third weighting factor is less than the second weighting factor. In order of predictive power, the use of rescue medication has the greatest influence, followed by inhalation parameters, and then by biometric parameters.

[0057] The biological parameters may be one or more parameters selected from, for example, weight, height, body mass index, blood pressure including systolic and / or diastolic blood pressure, sex, race, age, smoking history, sleep / activity patterns, exacerbation history, other treatments administered to the subject or other medications administered, etc. In one embodiment, the biological parameters include age, body mass index, and exacerbation history. In another embodiment, the biological parameters include exacerbation history and disease history, body mass index, and blood pressure, for example, systolic and / or diastolic blood pressure.

[0058] More generally, in calculating the probability of an asthma exacerbation, biological parameters have a significance / importance (e.g. weight) in the model of between 1% and 15%, preferably between 1% and 12%, more preferably between 3% and 10%, most preferably between 4% and 10%, for example about 5% or about 8%.

[0059] In a non-limiting example, in predicting asthma exacerbations, the number of rescue inhalations (e.g., including associated trends) has a significance / importance (e.g., weight) of 40% to 95% in the model, preferably 55% to 90%, more preferably 60% to 85%, most preferably 60% to 80%, such as about 60% or about 80%, inhalation parameters (e.g., including associated trends) have a significance / importance (e.g., weight) of 2% to 49%, preferably 2% to 45%, more preferably 5% to 40%, most preferably 10% to 35%, such as about 10% or about 35%, in the model, and biological parameters have a significance / importance (e.g., weight) of 1% to 15%, preferably 1% to 12%, more preferably 3% to 10%, most preferably 4% to 10%, such as about 5% or about 8% in the model.

[0060] More generally, additional data sources, such as environmental data on weather or pollution levels, may be added to the asthma exacerbation prediction model, with such additional data being weighted in the probability calculation to have less significance than data on the number of rescue inhalations, and optionally less significance than data on inhalation parameters.

[0061] Generally, the number of rescue inhalations (including, for example, trends related to the number of rescue inhalations) is the factor with the most significance in calculating the probability of an asthma exacerbation.

[0062] In a specific example, a decrease in maintenance medication adherence from 80% to 55%, a 67.5% increase in rescue inhaler use, a 34% decrease in peak flow rate, a 23% decrease in volume (all percentage changes from patient baseline), two exacerbations in the previous year, and a BMI greater than 28 would result in a 0.87 probability of an asthma exacerbation in the next 5 days using the ROC-AUC (see discussion of Figures 8 and 17 below).

[0063] Returning to the prediction of COPD exacerbations, the use of both the number of rescue inhalations and the parameters (as in the case of predicting asthma exacerbations) results in a more accurate prediction model than a model that ignores, for example, one of these factors. In addition, further clinical studies have shown that the parameters related to airflow during inhalation and the trends related to said parameters are more significant in calculating the probability of COPD exacerbations than the number of rescue inhalations. The number of rescue inhalations is still a significant factor in calculating the probability of exacerbations, but has a smaller impact on the probability than the parameters. Therefore, by weighting the model so that the parameters are more significant in the probability calculation than the number of rescue inhalations, the accuracy of the probability calculation is improved.

[0064] A COPD exacerbation prediction model, for example, has a first weighting factor related to the parameter and a second weighting factor related to the number of inhalations, and when standardized to account for the number of rescue inhalations (or trends in rescue medication use) and the different units used to quantify the parameter, the first weighting factor will be greater than the second weighting factor, thereby ensuring that the parameter is more significant than the number of rescue inhalations in calculating the probability of a COPD exacerbation.

[0065] The calculation of the probability of COPD exacerbation is based on a parameter related to airflow during rescue inhalation and / or during regular inhalation with the second inhaler, if the second inhaler is provided. The parameter corresponds to a single factor related to airflow during inhalation or includes a plurality of such factors. For example, the parameter is composed of at least one of the following: maximum inhalation flow rate, inhalation volume, inhalation duration, and inhalation rate. The maximum inhalation flow rate versus time, for example, provides a measure of inhalation rate.

[0066] Basing the probability calculation on parameters means that the model uses one or more factors related to the airflow during inhalation and / or one or more trends related to those factors, such trends corresponding to the variability of the respective factor.

[0067] The first weighting factor weights one or more factors related to airflow during inhalation and / or one or more trends related to those factors.

[0068] More generally, for calculating the probability of a COPD exacerbation, parameters relating to airflow during rescue inhalation and / or during regular inhalation (e.g. including trends relating thereto) have a significance / importance (e.g. weight) in the model (relative to other factors) of 55% to 95%, preferably 65% ​​to 90%, most preferably 75% to 85%, for example about 80%.

[0069] The calculation of the probability of a COPD exacerbation is also based in part on the number of rescue inhalations. Basing the probability calculation on the number of rescue inhalations means that the model uses the absolute number of rescue inhalations in the first time period and / or one or more trends based on the number of rescue inhalations. Such trends consist of the fluctuations in the number of rescue inhalations, rather than the number of rescue inhalations per se.

[0070] A second weighting factor weights one or more trends based on the absolute number of rescue inhalations and / or the frequency of rescue inhalations.

[0071] A trend based on the number of rescue inhalations may include, for example, the number of inhalations given within a particular period of the day, so that, for example, the number of inhalations given at night may be included as a factor in the number of inhalations.

[0072] More generally, for calculating the probability of a COPD exacerbation, the number of rescue inhalations (e.g. including any trends associated therewith) has a significance / importance (e.g. weight) in the model of between 2% and 30%, preferably between 5% and 25%, most preferably between 10% and 20%, for example about 15%.

[0073] The probability of a COPD exacerbation is the probability of an impending COPD exacerbation occurring within an exacerbation period following a first period. Thus, the model allows the calculation of the probability of a COPD exacerbation occurring within a pre-determined period, called the "exacerbation period", following a first period during which inhalation data, i.e. the number of rescue inhalations and parameter data, are collected. The exacerbation period may be, for example, 1-10 days, e.g., 5 days. The exacerbation period is selected based on the ability of the model to predict an exacerbation within that period, ensuring that the pre-determined period is long enough to take appropriate therapeutic steps, if necessary.

[0074] In some embodiments, biometric parameters are included in the asthma exacerbation prediction model to improve the accuracy of the model. In such embodiments, the processor is configured to receive, for example, biometric parameters (parameter values). A data input unit is included, for example, in the system, to allow the subject and / or a medical professional to input the biometric parameters.

[0075] The COPD exacerbation prediction model is, for example, weighted so that the parameters related to the organism have lower significance in the probability calculation than the parameters related to the airflow during inhalation. In other words, a third weighting factor is associated with the parameters related to the organism, and the third weighting factor is set to be smaller than the first weighting factor associated with the parameters. The third weighting factor is set to be larger or smaller than the second weighting factor associated with the number of rescue inhalations.

[0076] In the COPD exacerbation prediction model, preferably, the third weighting factor is less than the second weighting factor. In order of predictive power, the parameters related to airflow during inhalation have the greatest influence, followed by the number of rescue inhalations, and then the parameters related to the body of the patient.

[0077] As already described with respect to predicting asthma exacerbations, the biological parameters may be one or more parameters selected from, for example, weight, height, body mass index, blood pressure including systolic and / or diastolic blood pressure, sex, race, age, smoking history, sleep / activity patterns, exacerbation history, other treatments administered to the subject or other medications administered, etc. In one embodiment, the biological parameters include age, body mass index and exacerbation history.

[0078] More generally, in predicting COPD exacerbations, biological parameters have a significance / importance (e.g. weight) in the model of 1% to 12%, preferably 3% to 10%, most preferably 4% to 6%, for example about 5%.

[0079] Additional data sources, such as environmental data on weather or pollution levels, may be added to the COPD exacerbation prediction model, with such additional data being weighted in the probability calculation to have less significance than data on inhalation parameters, and optionally less significance than data on the number of rescue inhalations.

[0080] Regardless of the respiratory disease, the model is a linear or non-linear model. The model may, for example, be a machine learning model. A supervised model, such as a supervised machine learning model, may be used. Regardless of the specific form of the model used, as already explained, the model is configured to be more sensitive or responsive to the number of rescue inhalations or inhalation parameters depending on the respiratory disease. This sensitivity corresponds to the "weighting" of the weighted model.

[0081] In a non-limiting example, the model is configured to use decision trees, although other suitable techniques such as neural networks or deep learning models may also be considered by those skilled in the art.

[0082] Regardless of predicting a respiratory disease exacerbation, the processor of the system calculates the probability of an exacerbation based on the number of inhalations, the inhalation parameters, and an indicator representative of the pathology of the respiratory disease suffered by the subject. Including the indicator in the prediction increases the accuracy of the prediction, since the user-entered indicator helps to validate and make more reliable the predicted value of the probability assessment derived, for example, without the user-entered indicator, taking into account the number of inhalations and the inhalation parameters.

[0083] In one embodiment, the processor calculates an initial probability of a respiratory disease exacerbation based on the number of recorded inhalations and the received inhalation parameters (values), but not based on the index. The initial probability is calculated, for example, using a weighted model as described above for predicting asthma and COPD exacerbations. A probability, i.e., an overall probability, is then calculated based on the number of inhalations, the inhalation parameters, and the received index representative of symptoms of a respiratory disease suffered by the subject. For example, the overall probability is calculated based on the initial probability and the received index.

[0084] The initial probability determines the risk of exacerbation for example in the next 10 days. The overall probability taking into account indicators representative of the respiratory disease condition suffered by the subject determines the risk of exacerbation for example in the next 5 days. In other words, including indicators in the probability calculation allows for more accurate predictions in the shorter term.

[0085] Including user-entered metrics in the probability calculation enhances one or more of the following: positive and negative predictive value, prediction sensitivity, i.e., the ability of the system / method to correctly determine that a subject is at risk (true positive rate), and prediction specificity, i.e., the ability of the system / method to correctly determine that a subject is not at risk (true negative rate).

[0086] The number of inhalations and inhalation parameter data represent, for example, deviations from the subject's baseline values ​​for the 10 days preceding an exacerbation. By including user-entered indices in subsequent predictions, the positive and negative predictive values, as well as the sensitivity and specificity of the prediction system / method, are improved.

[0087] The processor is configured to control a user interface, for example, to output a prompt to the user and to receive input of the indicator from the user. The prompt is output based on an initial probability calculated based on the number of inhalations and the inhalation parameters, but not based on the indicator. The prompt is output based on an initial probability, for example, that a pre-determined threshold has been reached or exceeded. Thus, the user is prompted by the system to input the indicator based on an initial probability indicating the likelihood of an imminent exacerbation occurring. The user's input of the indicator helps to support and justify the initial probability with a (global) probability taking the indicator into account.

[0088] This may be considered, for example, an "analysis-driven use" of the indicator. User input is requested when the number of inhalations and inhalation parameter data indicate a possible exacerbation of the subject's respiratory disease.

[0089] The user interface may, for example, prompt the user or subject to input metrics by completing a short questionnaire via a pop-up notification. The logic determining when to issue a pop-up notification may be triggered by deviations in key variables, such as number and / or duration of rescue and / or maintenance inhalations, and changes in inhalation parameters.

[0090] Alternatively or additionally, the system is configured to receive the indicator when a user chooses to input the indicator through a user interface, for example when a medical professional determines that the indicator is useful to validate the initial probability calculation. This may be considered, for example, as an "on-demand" use of the indicator, where the request may be made by the patient or the patient's physician, for example prior to or during evaluation by a medical professional.

[0091] Thus, the user is only prompted to input the indicators when this is deemed necessary by the system and / or medical personnel. This advantageously reduces the burden on the subject and allows the subject to input the indicators when required or prompted, i.e., when such input is desirable with respect to monitoring the subject's respiratory disease. Entering the indicators in these embodiments is more feasible than if the subject were prompted to input the indicators on a routine basis.

[0092] In one embodiment, the user interface is configured to provide a plurality of user-selectable respiratory disease condition options, where the indication is determined by a user selection of at least one of the condition options.

[0093] For example, the user interface may display a questionnaire consisting of a number of questions, each answer of which corresponds to an indicator, and a user, e.g., a subject or a healthcare professional caring for the subject, may use the user interface to input answers to the questions.

[0094] The questionnaire is relatively short, i.e., it consists of a relatively small number of questions, in order to minimize the burden on the subject, but the number and content of the questions are nevertheless set so that the index allows the calculation of the probability of progression to be more reliable than if no index was entered at all.

[0095] More generally, the purpose of the questionnaire is to ascertain current or relatively recent (e.g., within the last 24 hours) indicators to provide a rapid understanding of the subject's (respiratory disease-related) well-being through a small number of timely questions that can be answered relatively quickly. The questionnaire will be translated into the subject's local language.

[0096] Common control questionnaires, particularly the ACQ / T (Asthma Control Questionnaire / Test) for asthma or the CAT (COPD Assessment Test) for COPD, tend to focus on recalling a patient's past condition, and this focus on the past rather than the present may negatively impact their value for predictive analytical purposes.

[0097] A non-limiting example of such a questionnaire is given below: For each question, subjects select one of the following options to indicate the situation: always (5), mostly (4), sometimes (3), rarely (2), and never (1). 1. How often do you experience shortness of breath or what is the rate at which your shortness of breath occurs? 2. How often do you experience a cough or what is your cough incidence? 3. How often do you experience wheezing and what is your percentage of wheezing episodes? 4. How often do you experience chest compressions or what is your rate of chest compression episodes? 5. How often do you experience nocturnal episodes / sleep impacts or what is your percentage of nocturnal episodes / sleep impacts? 6. How often do you experience restrictions at work, school or home, or what is the percentage of restrictions at your work, school or home?

[0098] Another example of a questionnaire is as follows: 1. Are you experiencing more respiratory symptoms than usual (yes / no)? If you answered "yes": 2. Are you experiencing more chest pressure or difficulty breathing (yes / no)? 3. Are you experiencing more coughing (yes / no)? 4. Are you experiencing more wheezing (yes / no)? 5. Is it affecting your sleep (yes / no)? 6. Does it limit your activities at home / work / school (yes / no)?

[0099] The answers to these questions may be used, for example, to calculate a score that is included in or corresponds to an index representative of the severity of the respiratory disease suffered by the subject.

[0100] In one embodiment, the user interface is configured to provide status options in the form of selectable icons, e.g., pictogram-type icons and / or checkboxes and / or sliders and / or dials. The user interface thus provides a direct and intuitive way to input indicators representative of the respiratory disease condition suffered by the patient. Such an intuitive input method is particularly advantageous when the subject inputs indicators himself / herself, since the relatively easy user input is hardly hindered by the deterioration of the subject's respiratory disease.

[0101] A suitable user interface is used for the purpose of allowing the user to input indicators representative of the respiratory disease condition suffered by the subject. For example, the user interface comprises or consists of a (first) user interface of a user-owned device. The user-owned device is, for example, a personal computer, a tablet computer and / or a smartphone. When the user-owned device is a smartphone, the (first) user interface corresponds, for example, to a touch screen of the smartphone.

[0102] In one embodiment, the processor of the system comprises at least in part a (first) processor within the user-owned device. Alternatively or additionally, the inhaler device, e.g. the first and / or second inhaler, comprises a (second) processor, and the processor of the system comprises at least in part a (second processor) contained within the inhaler device.

[0103] A method of the present invention is provided for calculating a probability of a respiratory disease exacerbation in a subject, the method comprising: recording an inhalation of a medicament performed by the subject, receiving measurements of a parameter related to airflow measured during the inhalation, receiving an indicator representative of a respiratory disease condition experienced by the subject, and calculating the probability of a respiratory disease exacerbation based on the recorded inhalation and the received measurements of the parameter and the received indicator.

[0104] Furthermore, there is provided a computer program having a computer program code adapted to perform the above-mentioned method, when the computer program is run on a computer. In a preferred embodiment the computer program takes the form of an application program, for example an application program for a mobile device such as a tablet computer or a smartphone.

[0105] Further, according to the present invention, there is provided a method of treating a respiratory disease exacerbation in a subject, comprising carrying out the above-mentioned method of calculating the probability of a respiratory disease exacerbation, determining whether the probability reaches or exceeds a predetermined upper limit, or determining whether the probability reaches or is lower than a predetermined lower limit, and treating the respiratory disease exacerbation based on the probability of reaching or exceeding the upper limit, or based on the probability of reaching or falling below the lower limit.

[0106] Treatment may include, for example, using an inhaler to administer rescue medication to the subject when the probability reaches or exceeds a predetermined upper limit.

[0107] The treatment modifies an existing treatment, the existing treatment having a first treatment regimen, and the modification of the existing treatment comprises changing from the first treatment regimen to a second treatment regimen based on a probability of meeting or exceeding a predetermined threshold, the second treatment regimen being created for respiratory disease exacerbations having a higher risk than the first treatment regimen.

[0108] More accurate risk calculations using weighted models facilitate more effective warning systems, resulting in appropriate medical interventions for subjects. More accurate assessment of the risk of exacerbation allows to guide interventions for subjects at significant risk. In particular, the intervention includes implementing a second treatment plan. This includes, for example, progressing the subject to a higher stage as defined by the GINA or GOLD guidelines. Such preemptive intervention means that the subject does not have to continue to suffer from an exacerbation or continue to be exposed to the associated risks in order to progress to a second treatment plan that is justified.

[0109] In one embodiment, the second treatment regimen comprises administering a biopharmaceutical to the subject. The relatively high cost of biopharmaceuticals means that stepping up a subject's treatment to include administration of a biopharmaceutical is likely to require careful consideration and justification. The systems and methods according to the present invention can justify the administration of a biopharmaceutical by providing a reliable prediction regarding the risk of a subject suffering an exacerbation, for example, when the calculated probability reaches or exceeds an upper limit representing a high risk exacerbation for a predetermined minimum number of times, the administration of the biopharmaceutical is quantitatively justified and the biopharmaceutical is administered accordingly.

[0110] More commonly, the biologics consist of any one or a combination of two or more of omalizumab, mepolizumab, reslizumab, benralizumab, and dupilumab.

[0111] Changing the existing respiratory disease treatment comprises changing from a first treatment plan to a third treatment plan designed for a lower risk of respiratory disease exacerbation than the first treatment plan based on a probability of reaching or being below a predetermined lower limit.

[0112] For example, if the probability of exacerbation is low over a relatively long period of time, a probability calculation with reasonable accuracy can be used to advise justifying downgrading or even elimination of an existing treatment plan. In particular, a subject is transitioned from a first treatment plan to a third treatment plan designed for a lower risk of respiratory disease exacerbation than the first treatment plan. This can include, for example, advancing the subject to a lower stage as defined by the GINA or GOLD guidelines.

[0113] The present invention also provides a method for diagnosing respiratory disease exacerbation, comprising carrying out the method for calculating the probability of respiratory disease exacerbation in a subject described above, determining whether the probability reaches or exceeds a predetermined upper limit representing respiratory disease exacerbation, and diagnosing respiratory disease exacerbation based on the probability that the upper limit has been reached or exceeded.

[0114] Further, according to the present invention, there is provided a method for diagnosing the severity of a respiratory disease in a subject, comprising carrying out the above-mentioned method for calculating the probability of respiratory disease exacerbation in a subject, determining whether the probability has reached or exceeded a predetermined upper limit representing a more severe respiratory disease, or determining whether the probability has reached or fallen below a lower limit representing a milder respiratory disease, and diagnosing asthma as having become more severe based on the probability of reaching or exceeding the upper limit, or diagnosing respiratory disease as having become milder based on the probability of reaching or falling below the lower limit.

[0115] The present invention further provides a method for delimiting a subpopulation of subjects, comprising carrying out the above-described method for each subject belonging to a population of subjects, thereby calculating a probability of respiratory disease exacerbation for each subject belonging to the population, setting a probability threshold or probability range that distinguishes the probability calculated for the subpopulation from the probability calculated for the remainder of the population, and using the probability threshold or probability range to define the subpopulation from the remainder of the population.

[0116] 1 is a block diagram of a system 10 according to one embodiment of the present invention. System 10 includes an inhaler device 100 and a processor 14. Inhaler device 100 includes a first inhaler for administering a rescue medication, such as a SABA, to a subject. The SABA may include, for example, albuterol. Alternatively or additionally, inhaler device 100 includes a second inhaler for administering a maintenance medication to the subject, as described above.

[0117] System 10 may, for example, alternatively be referred to as an "inhaler assembly."

[0118] The first inhaler, for example, may alternatively be referred to as the "rescue inhaler."

[0119] The second inhaler may, for example, alternatively be called a "maintenance inhaler" or a "control inhaler."

[0120] The number of rescue inhalations is measured by a usage detection system 12B included in the inhaler device 100.

[0121] The sensor system 12A is configured to measure a parameter. The sensor system 12A includes one or more sensors, such as, for example, one or more pressure sensors and / or temperature sensors and / or humidity sensors and / or directional sensors and / or acoustic sensors and / or optical sensors. Pressure sensors include air pressure sensors (e.g., atmospheric pressure sensors), differential pressure sensors, and absolute pressure sensors, etc. The sensors use Microelectromechanical Systems (MEMS) and / or Nanoelectromechanical Systems (NEMS) technology.

[0122] Pressure sensors are particularly suited to measuring parameters because they are monitored by measuring pressure changes related to airflow during inhalation by a subject. As described in more detail below with reference to Figures 18-22, the pressure sensor may be positioned, for example, in or in communication with the flow path through which air and medicament are inhaled by a subject during inhalation. Alternative methods of measuring parameters, such as using flow sensors, will also be apparent to those skilled in the art.

[0123] Optionally or additionally, the sensor system 12A includes a differential pressure sensor, which may comprise, for example, a dual port type sensor for measuring the pressure difference across a portion of the airflow path through which the subject inhales. Alternatively, a single port gauge type sensor may be used, which measures the pressure difference in the airflow path between during inhalation and when no airflow is present. The difference in measurements corresponds to the drop in pressure associated with inhalation.

[0124] The sensor system 12A is further configured to measure a parameter during regular inhalation, for example, the sensor system 12A includes another pressure sensor, such as another microelectromechanical system pressure sensor or another nanoelectromechanical system pressure sensor, for measuring a parameter during inhalation of a maintenance medication.

[0125] Thus, inhalation of either or both the rescue and maintenance medication is used to gather information regarding the subject's lung function and / or lung health. When the first and second inhalers are used, the accuracy of predicting impending exacerbations is improved by the additional inhalation data provided by monitoring both the regular and rescue inhalations.

[0126] Each regular inhalation and rescue inhalation reduces the pressure in the airflow path compared to when no inhalation is performed. The point at which the pressure is at its lowest corresponds to the maximum inhalation flow rate. The pressure sensor 12A detects this point during inhalation. The maximum inhalation flow rate varies from inhalation to inhalation and depends on the subject's medical condition. A lower maximum inhalation flow rate is recorded, for example, when the subject is approaching an exacerbation. The term "minimum maximum inhalation flow rate" as used herein means the smallest of the maximum inhalation flow rates recorded for inhalations performed with the inhaler device within a certain (second) period of time.

[0127] The pressure change associated with each inhalation is alternatively or additionally used to measure the inhalation volume. This is achieved, for example, by using the pressure change measured by the pressure sensor 12A during inhalation to measure the flow rate during inhalation and deriving the total inhalation volume therefrom. A smaller inhalation volume is recorded, for example, when the subject is approaching an exacerbation, because the subject's ability to inhale is reduced. The term "minimum inhalation volume" as used herein means the smallest inhalation volume recorded for inhalations performed with the inhaler device within a certain (third) period of time.

[0128] The pressure change associated with each inhalation is alternatively or additionally used to measure the duration of inhalation. For example, the time from the first drop in pressure measured by pressure sensor 12A, which corresponds to the start of inhalation, to the return to the pressure when no inhalation is occurring is recorded. A shorter inhalation duration is recorded, for example, when the subject is approaching an exacerbation, because the subject's ability to inhale for a long period of time is reduced. As used herein, the term "minimum inhalation duration" refers to the shortest inhalation duration recorded for inhalations performed with the inhaler device within a given (fourth) period of time.

[0129] In one embodiment, the parameters include, for example, peak inhalation flow rate versus time, instead of or in addition to peak inhalation flow rate and / or inhalation volume and / or inhalation duration, which is recorded from the first pressure drop measured by pressure sensor 12A, for example, corresponding to the start of inhalation, until the pressure reaches a minimum value corresponding to the peak flow rate. Subjects at greater risk of exacerbation take longer to achieve peak inhalation flow rate.

[0130] In a non-limiting example, the inhaler device is configured such that for a normal inhalation, each medication is administered within about 0.5 seconds following the start of inhalation, and a subject's inhalation reaching maximum inhalation flow rate after that 0.5 second period has elapsed, such as after about 1.5 seconds, is indicative of a partial impending exacerbation.

[0131] The use detection system 12B is configured to record inhalations by the subject (e.g., each rescue inhalation by the subject when the inhaler is a rescue inhaler, or each maintenance inhalation by the subject when the inhaler is a maintenance inhaler). In a non-exhaustive example, the first inhaler 100 includes a reservoir (not shown in FIG. 1) and a metering assembly (not shown in FIG. 1) configured to meter a dose of medication from the reservoir. The usage detection system 12B is configured to record the metering of a dose of medication by the metering assembly, each dose representing a rescue inhalation taken by the subject using the first inhaler 100. Thus, the inhaler 100 is configured to monitor the number of rescue inhalations of medication, since a dose of medication must be metered by the metering assembly before being inhaled by the subject. One non-limiting example of an arrangement for metering will be described in more detail with reference to FIGS. 18-22.

[0132] Alternatively or additionally, the use detection system 12B registers each inhalation in various ways and / or based on additional or alternative feedback that would be apparent to one of skill in the art. For example, the use detection system 12B is configured to register an inhalation by the subject when feedback from the sensor system 12A indicates that an inhalation by the user is occurring (e.g., when a pressure measurement or flow rate exceeds a predetermined threshold associated with a successful inhalation). Furthermore, in some examples, the use detection system 12B is configured to register an inhalation when an inhaler switch or an external device user input (e.g., a touch screen on a smartphone) is manually performed by the subject before, during, or after an inhalation.

[0133] A sensor (e.g., a pressure sensor) may be included in the use detection system 12B, for example, to register each inhalation. In such an example, the use detection system 12B and the sensor system 12A may use respective sensors (e.g., a pressure sensor) or a common sensor (e.g., a common pressure sensor) configured to perform both the functions of detecting use and detecting inhalation parameters.

[0134] When a pressure sensor is included in the usage detection system 12B, the pressure sensor may be used, for example, to confirm that a dose of medication metered by the metering assembly has been inhaled by the user or to assess the extent of such inhalation, as described in more detail with reference to Figures 18-22.

[0135] In one embodiment, the sensor system 12A and / or the usage detection system 12B comprises an acoustic sensor. In this embodiment, the acoustic sensor is configured to detect noises generated when a subject inhales using the respective inhaler device. The acoustic sensor may, for example, comprise a microphone.

[0136] In a non-limiting example, each inhaler device has a capsule arranged to rotate when a subject inhales using the inhaler device, the rotation of the capsule generating a noise detectable by an acoustic sensor, i.e., the rotation of the capsule provides a noise, e.g., a rattle, that can be appropriately interpreted to derive data on usage and / or inhalation parameters.

[0137] Algorithms are used, for example, to interpret the acoustic data to determine usage data (when an acoustic sensor is included in the usage detection system 12B) and / or to determine parameters related to airflow during inhalation (when an acoustic sensor is included in the sensor system 12A).

[0138] For example, an algorithm such as that described in Colthorpe et al., "Adding Electronics to the Breezhler; Satisfying the Needs of Patients," Respiratory Drug Delivery, 2018, pp. 71-79 may be used. Once a generated sound is detected, the algorithm processes the raw acoustic data and generates data for use and / or inhalation parameters.

[0139] The system 10 further comprises a user interface 13 that allows a user to input indicators representative of respiratory symptoms experienced by the subject. Any suitable user interface 13 may be used for this purpose, such as the touch screen of a smartphone.

[0140] A processor 14 included in the system 10 counts the number of inhalations and receives the parameters measured during each inhalation. As shown in Fig. 1 by the arrows between the sensor system 12A and the processor 14 and between the use detection system 12B and the processor 14, the processor 14 receives the inhalation and parameter data from the use detection system 12B and the sensor system 12A, respectively. The arrows between the user interface 13 and the processor 14 indicate that indicators representative of a respiratory disease condition experienced by the subject are received from the user interface 13 by the processor 14. The processor 14 is configured to calculate a probability of a respiratory disease exacerbation based on the number of rescue inhalations and the inhalation parameters and indicators, as previously described.

[0141] In a non-limiting example, the processor 14 is located independently of the inhaler device 100. In this case, the processor 14 receives rescue inhalation count and parameter data transmitted from the sensor system 12A and the usage detection system 12B. By processing the data in an external processing unit, such as a processing unit of an external device, inhaler battery life is advantageously preserved.

[0142] The inhaler device may, for example, include a suitable user interface for informing the subject of the results of the probability calculation. Rather than providing a numerical probability, in some instances, a more intuitive means of informing the subject of the risk is used, such as using different colored light sources depending on the calculated probability. That is, the inhaler device may, for example, prompt the subject to take preemptive steps, such as taking one or more inhalations of rescue medication, and / or to reduce or eliminate the risk of an exacerbation, and / or to input indicators using the user interface.

[0143] It is also possible that some of the functions of processor 14 are performed by an internal processing unit contained in the inhaler device, and other functions of the processor, such as the probability calculations themselves, are performed by an external processing unit (e.g. a first processor) in an external user device.

[0144] More generally, the system 10 may include a communication module (not shown in FIG. 1 ) configured to, for example, inform the subject and / or a medical professional, such as a physician, of the calculated probability, and the subject and / or physician may then take appropriate steps based on the calculated probability of a respiratory disease exacerbation. For example, when a smartphone processing unit is included in the processor, communication features of the smartphone, such as SMS, email, Bluetooth, etc., may be used to inform the medical professional of the calculated probability.

[0145] 2 illustrates a non-limiting example of a system 10 for calculating the probability of a respiratory exacerbation in a subject. A weighted model, optionally referred to as a respiratory exacerbation risk prediction model, is used to calculate the probability and the calculation result is provided to the subject and / or a caregiver and / or a medical professional.

[0146] The system 10 includes an inhaler device 100, an external device 15 (e.g., a mobile device), a public and / or private network 16 (e.g., the Internet, a cloud network, etc.), and a personal data storage device 17. The external device 15 includes, for example, a smartphone, a personal computer, a laptop computer, a wireless media device, a media streaming device, a tablet device, a wearable device, a Wi-Fi or wireless-enabled television, or any other suitable Internet protocol-enabled device. For example, the external device 15 is configured to transmit and / or receive RF signals through a Wi-Fi communication link, a Wi-MAX communication link, a Bluetooth or Bluetooth Smart communication link, a Near Field Communication (NFC) link, a cellular communication link, or a television white space (TVWS) communication link, or a combination of two or more thereof. The external device 15 transmits data to the personal data storage device 17 through the public and / or private network 16.

[0147] The inhaler device 100 includes communication circuitry, such as a Bluetooth radio, for transmitting data to an external device 15. The data includes the inhalation count and parameters described above.

[0148] The inhaler device 100 may also receive data, such as program instructions, operating system changes, dosage information, warnings or notifications, acknowledgements, etc., from, for example, an external device 15 .

[0149] The external device 15 includes at least a portion of the processor 14, thereby processing and analyzing the inhalation frequency and parameter data. For example, the external device 15 processes data, such as data for calculating the probability of a respiratory exacerbation, as represented by block 18A, and provides such information to the personal data store 17 for automatic storage.

[0150] In some non-limiting examples, the external device 15 also processes the data to identify no-inhale events and / or low-inhale events and / or good-inhale events and / or over-inhale events and / or exhale events, as represented by block 18B. The external device 15 also processes the data to identify insufficient use, excessive use, and optimal use, as represented by block 18C. The external device 15 processes the data to estimate, for example, the number of doses given and / or remaining, and to identify error conditions, such as an error condition related to a time-stamp error flag indicating that the subject failed to inhale a dose metered by the metering assembly. The external device 15 has a display and software for visually displaying the usage parameters through a GUI on the display. The usage parameters are stored as personalized data that is stored to predict future risk of exacerbation based on real-time data.

[0151] In some embodiments, although described as being stored in the personal data storage device 17, the probability of a respiratory disease exacerbation as indicated by block 18A, no inhalation events, low inhalation events, good inhalation events, over inhalation events and / or exhalation events as indicated by block 18B, and / or under-use events, over-use events and optimal use events as indicated by block 18C may also be stored in the external device 15.

[0152] FIG 3A is a flow diagram of a method 20 according to one embodiment of the present invention. Method 20 is performed by a system such as system 10 shown in FIG 1 and / or FIG 2. For example, one or more of the first and / or second inhaler and / or external device 15 and / or personal data storage device 17 are configured to perform all or part of method 20. That is, any combination of steps 22, 24 and 26 may be performed by any combination of the first inhaler and / or second inhaler and / or external device 15 and / or personal data storage device 17. Furthermore, it should be understood that steps 22 and 24 may be performed in any temporal order.

[0153] The method 20 comprises the steps of measuring 22 inhalations of a drug taken by a subject, receiving 23 an indicator representative of a respiratory disease condition experienced by the subject, receiving 24 a measurement value of an airflow-related parameter measured during inhalation, and calculating 26 a probability of a respiratory disease exacerbation based on the number of recorded inhalations and the received parameter measurements and the received indicator.

[0154] Although not described in method 20, system 10 is configured to inform the user when the probability of an exacerbation exceeds or falls below a threshold. For example, system 10 is configured to determine whether the probability reaches or exceeds a predetermined upper limit and / or whether the probability reaches or falls below a predetermined lower limit. In response, system 10 is configured to treat the patient (e.g., via a message to the patient's healthcare provider), for example, by switching the patient's treatment plan to a treatment plan created for a higher (lower) risk of asthma exacerbation than the original treatment plan.

[0155] System 10 may inform the user of the probability of an exacerbation through one or more techniques. For example, system 10 may be configured to display a message on a display of external device 15, transmit a message to a medical professional or third party associated with the user, or activate an indicator (e.g., a light or speaker) on inhaler device 100 to inform the user.

[0156] The method 20 further includes the step of preparing an inhaler device for administering medication to the subject, the inhaler device having a usage detection system configured to measure inhalations performed by the subject using the inhaler device.

[0157] The number of inhalations, e.g. emergency and / or regular inhalations, is metered by a usage detection system included in the inhaler device. A sensor system measures parameters related to airflow during inhalations, e.g. emergency and / or regular inhalations, as previously described.

[0158] The method 20 further comprises a step of calculating an initial probability of a respiratory disease exacerbation based on the number of recorded inhalations and the received parameter measurements but not on the index. The initial probability determines, for example, the risk of an exacerbation in the following 10 days. The step 26 of calculating the (overall) probability of a respiratory disease exacerbation is based on the initial probability and the received index, or on the number of inhalations and the parameter measurements and the index. The overall probability determines, for example, the risk of an exacerbation in the following 5 days using an index representative of the respiratory disease symptoms experienced by the subject.

[0159] Method 20 includes prompting a user to input an indicator based on an initial probability. The prompt is output based on, for example, an initial probability that a predetermined threshold has been reached or exceeded. This embodiment of method 20 corresponds to the "analysis-driven use" of the indicator described above, i.e., user input is prompted when the number of inhalations and inhalation parameter data indicate a possible worsening of the subject's respiratory condition. Alternatively or additionally, the indicator may be entered without a prompt based on the initial probability.

[0160] 3B is a flow diagram and timeline for a method according to another embodiment of the invention. The timeline shows the day of predicted exacerbation onset ("Day [0]"), the 5 days prior to the exacerbation ("Day [-5]"), and the 10 days prior to the exacerbation ("Day [-10]").

[0161] In Fig. 3B, block 222 represents an inhaler use notification, which is considered as a notification regarding rescue and / or maintenance medication use. Block 224 represents a flow rate notification, which corresponds to a parameter related to airflow during inhalation. Block 225 represents a "use" and "flow rate" notification, which is considered as a combined notification based on inhaler use and inhalation parameters.

[0162] Block 226 represents a notification of a prediction. This notification of a prediction is based on the calculation of the initial probability described above. FIG. 3B shows that the questionnaire is presented in block 223 on day [-10]. The presentation of the questionnaire includes outputting a prompt to the user to input the indicators through the questionnaire. Block 227 represents that the questionnaire result indicates that the risk of exacerbation remains after the user input. This means that in block 230 the presentation of the questionnaire continues, or the user is asked to input the indicators again, or is asked for further input regarding his respiratory disease condition. Block 231 represents a scenario in which the risk of exacerbation remains, for example after the calculation of the overall probability described above, and in block 233 the notification of a prediction of exacerbation continues.

[0163] Block 228 represents a scenario in which the calculated risk of progression returns to baseline based on user-entered indicators after presentation of the questionnaire in block 223. Correspondingly, risk notification ends in block 229.

[0164] Similarly, block 232 represents a scenario in which the risk of exacerbation returns to baseline after the continued or further presentation of the questionnaire in block 230 is terminated. Although not shown in FIG. 3B (for ease of illustration), risk notification ends in block 232 after the risk of exacerbation returns to baseline.

[0165] Clinical studies have been conducted to evaluate factors that influence the probability of an asthma exacerbation. The examples set forth below should be considered as illustrative, non-limiting examples.

[0166] Although the results of the study are generally applicable to other rescue medications administered using other types of devices, albuterol administered using the ProAir Digihalaer® device marketed by Teva Pharmaceutical Industries was used in the 12-week, open-label study.

[0167] Patients (aged 18 years or older) with exacerbable asthma were recruited for the study. Patients used Proair Digihaler (90 mcg of albuterol as the sulfate salt with lactose carrier, inhaled 1 to 2 times every 4 hours) on an as-needed basis.

[0168] The Digihaler's electronic module recorded parameters related to each use, each inhalation and the airflow during each inhalation: peak inhalation flow rate, inhaled volume, time vs. peak inhalation flow rate and inhalation duration. The data was downloaded from the inhaler along with clinical data and applied to a machine learning algorithm to develop a model for predicting impending exacerbations.

[0169] The diagnosis of clinical asthma exacerbation (CAE) in this example was based on the American Thoracic Society / European Respiratory Society statement (HK Reddel et al., Am J Respir Crit Care Med. 2009, 180(1), 59-99), which includes both "major CAE" and "moderate CAE".

[0170] A serious CAE is defined as a CAE involving worsening asthma requiring at least 3 days of oral steroids (prednisone or equivalent) and hospitalization. Moderate CAE requires at least 3 days of oral steroids (prednisone or equivalent) or hospitalization.

[0171] The generalized model was evaluated by receiver operating characteristic (ROC) curve analysis, as described in more detail below with reference to FIG.

[0172] The purpose and primary endpoint of the study was to examine the patterns and amounts of albuterol used preceding a CAE, captured by the DigiHaler, alone or in combination with other study data, such as parameters related to airflow, physical activity, and sleep during inhalation preceding a CAE. This study provides the first successful attempt to develop a model for the prediction of CAE derived from the use of a rescue medication inhalation device with integrated sensors and capable of measuring inhalation parameters.

[0173] FIG. 4 shows three timelines depicting different inhalation patterns recorded by their respective Digihalers for three different patients. The top timeline shows the patient in question taking one inhalation at a time. The bottom timeline shows the patient in question taking two or more consecutive inhalations in one session, where the term "session" is defined as a series of inhalations where successive inhalations are taken within a time interval of 60 seconds or less. The intermediate timelines show the patient in question taking inhalations in various patterns. That is, the Digihaler is configured to record not only the number of rescue inhalations, but also the pattern of use.

[0174] Results: 360 patients were found to have received at least one effective inhalation from the Digihaler. These 360 ​​patients were included in the analysis. Of these patients, 64 patients experienced a total of 73 CAEs. FIG. 5 is a graph 30 of the average number of rescue inhalations versus days since an asthma exacerbation. FIG. 5 shows data for a risk period of 14 days before and after the date of the exacerbation. Line 32 corresponds to the average number of rescue inhalations per day during the risk period. Line 32 is higher on the y-axis than the average number of rescue inhalations per day outside the baseline risk period, represented by line 34. This represents an increase in the average number of rescue inhalations per day as the risk of an exacerbation increases. For reference, FIG. 5 also shows the average number of rescue inhalations per day for patients who did not experience a baseline exacerbation, represented by line 36.

[0175] Figure 6 is another graph 30 of the average number of rescue inhalations versus days since an asthma exacerbation. Figure 6 shows data within a 50 day period before and after the date of an exacerbation. Figure 6 shows that the number of rescue inhaler uses increases significantly as the date of an exacerbation approaches, compared to the average number of daily rescue inhalations outside the baseline risk period, represented by line 34.

[0176] FIG. 7 shows four graphs of percent change from baseline in various parameters related to number of rescue inhalations and airflow versus days since asthma exacerbation.

[0177] Graph 40 plots the percent change in number of rescue inhalations from baseline (outside the risk period) versus days since asthma exacerbation. The number of rescue inhalations was found to increase by 90% from baseline just prior to an exacerbation.

[0178] Graph 42 plots the percent change in minimum daily peak flow rates relative to baseline versus days since an asthma exacerbation. Graph 42 shows that minimum daily peak flow rates generally decrease in the days leading up to an exacerbation. Minimum daily peak flow rates were found to decrease 12% relative to baseline just prior to an exacerbation.

[0179] Graph 44 plots the percent change in minimum daily intake from baseline versus days since exacerbation. Graph 44 shows that minimum daily intake generally decreases in the days leading up to an exacerbation. Minimum daily intake was found to decrease by 20% from baseline just prior to an exacerbation.

[0180] Graph 46 plots the percent change in minimum daily inhalation duration from baseline versus days since exacerbation. Graph 46 shows that minimum daily inhalation duration generally decreases in the days leading up to an exacerbation. Minimum daily inhalation duration was found to decrease by 15%-20% from baseline just prior to an exacerbation.

[0181] In constructing the first weighted prediction model, the strongest predictor of asthma exacerbations, especially within the 5-day period preceding a CAE, was found to be the average number of rescue inhalations per day. Airflow-related parameters, i.e., peak inhalation flow rate and / or inhalation volume and / or inhalation duration, were also found to have significant predictive value.

[0182] In the first weighted model, the characteristics most significant in calculating the probability of an asthma exacerbation were found to be number of rescue inhalations (61%), inhalation tendency (16%), peak inhalation flow rate (13%), inhalation volume (8%), and nocturnal use of albuterol (2%). These inhalation characteristics were collected by DigiHaler, recording peak inhalation flow rate, peak inhalation flow rate over time, inhalation volume, inhalation duration, nocturnal use, and trends in these parameters over time.

[0183] An inhalation trend is an artificially created or engineered parameter, such as the percentage change in the amount of inhalation today compared to the last three days. Another example is the percentage change in the number of rescue inhalations today compared to the last three days. In these examples, the respective trends are not the amount of inhalation and the number of rescue inhalations per se, but the respective changes in the amount of inhalation and the number of rescue inhalations.

[0184] Based on the above results, a first weighted predictive model was developed to calculate the probability of an asthma exacerbation. Supervised machine learning techniques and gradient boosting were used to solve the classification problem (whether an exacerbation will occur in the next x days (exacerbation duration)).

[0185] Gradient boosting techniques are well known in the art, see JH Friedman, Computational Statistics & Data Analysis, 2002, 38(4), 367-378, and JH Friedman et al., The Annals of Statistics, 2000, 28(2), 337-407. It produces a predictive model in the form of an ensemble (multiple learning algorithms) of basic predictive models consisting of decision trees (tree-shaped models and their possible consequences). It forms a single powerful learner model in an iterative manner by using an optimization algorithm to minimize some suitable loss function (a function of the difference between the estimated value and the true value for the data examples). The optimization algorithm calculates the expected value of the loss function using a training set of known values ​​of the response variable (whether an exacerbation will occur in the next x days) and their corresponding predictive values ​​(a list of features and designed features). The learning process successively fits new models, providing more accurate predictions of the response variable.

[0186] Table 1 lists, by example, the factors included in the first weighted predictive model, along with their relative weighting to one another.

[0187] [Table 1]

[0188] Although the key factors in the predictive model for calculating the probability of an impending asthma exacerbation are the number of rescue inhalations and the trend in the number of rescue inhalations, the predictive model was made more robust by supplementing it with parameters relating to airflow during inhalation. Figure 8 shows a receiver operating characteristic (ROC) curve analysis of the model, which evaluates the quality of the model by plotting the true positive rate against the false positive rate. This first weighted prediction model used the relevant features described above to predict impending exacerbations over the next 5 days with an AUC value of 0.75. When using features based only on the number of rescue inhalations, the AUC value is 0.69.

[0189] Therefore, although its effect on the probability is smaller than that of the number of rescue inhalations, the parameters related to the airflow during inhalation are shown to be significant factors that increase the accuracy of the calculation of the probability of an asthma exacerbation, as are other factors other than the number of rescue inhalations.

[0190] A second weighted prediction model was developed using the same data to improve the first weighted prediction model. Biometric parameters were included in the model. In particular, case report form (CRF) data such as medical history, body mass index (BMI) and blood pressure were combined with Digihaler data and applied to a machine learning algorithm to improve the prediction model.

[0191] The algorithm was trained not only on age, BMI, blood pressure and number of exacerbations and hospitalizations in the last 12 months, but also on patient-specific inhalation information collected by the Digihaler. Comparisons between baseline and pre-prediction characteristics, and the trends of change in those characteristics, were applied to a supervised machine learning algorithm. A four-fold cross-validation technique was used to compare performance metrics, and gradient boosting was selected as the optimal algorithm. As before, the generalized model was evaluated by analysis of the receiver operating characteristic curve (ROC) and the area under the curve (AUC).

[0192] Table 2 lists, by example, the factors included in the second weighted predictive model, along with their relative weighting to one another.

[0193] [Table 2]

[0194] The second weighted prediction model predicted impending exacerbations in the following 5 days with an AUC value of 0.83. The second weighted prediction model had a sensitivity of 68.8% and a specificity of 89.1%. Thus, the second weighted prediction model was shown to be an improved asthma exacerbation prediction model over the first weighted prediction model described above with an AUC value of 0.75. The additional improvement of the second weighted prediction model is at least partially attributable to the introduction of biometric parameters.

[0195] More typically, the first period over which the number of rescue inhalations should be measured is from 1 to 15 days, preferably from 3 to 8 days. Monitoring the number of rescue inhalations over the first period is particularly useful in calculating the probability of an asthma exacerbation.

[0196] When the inhalation parameter includes a maximum inhalation flow rate, the method 20 further includes calculating a maximum inhalation flow rate, such as a minimum or average maximum inhalation flow rate, from the maximum inhalation flow rates measured for inhalations performed within the second time period. The term "second" with respect to the second time period is intended to distinguish the time period during which the maximum inhalation flow rate is sampled from the first time period during which the number of rescue inhalations is sampled. The second time period may at least partially overlap with the first time period, or the first and second time periods may be coincident.

[0197] Thus, step 26 of calculating the probability of an asthma exacerbation is based in part on the minimum or average peak inhalation flow rate. The second time period is, for example, 1-5 days, such as 1 day. The second time period is selected according to the time it takes to collect data for the appropriate values ​​of peak inhalation flow rate, in a manner similar to the considerations described above with respect to the first time period.

[0198] Calculation of the probability of an asthma exacerbation is based in part on the change in minimum or average peak flow rate relative to a baseline peak flow rate, for example graph 42 of FIG.

[0199] For improved accuracy of exacerbation prediction, the change in the minimum or average maximum inhalation flow rate relative to the reference maximum inhalation flow rate may be, for example, 10% or more (such as 50% or more or 90% or more). The reference value is calculated using the minimum daily maximum inhalation flow rate measured over a period during which no exacerbations occurred, for example 1 to 20 days, preferably 10 days. Alternatively or additionally, the minimum or average maximum inhalation flow rate is evaluated relative to an absolute value.

[0200] The method 20 further includes calculating an inhalation volume, such as a minimum or average inhalation volume, from the inhalation volumes measured for inhalations made within a third time period. The term "third" with respect to the third time period is intended to distinguish the time period during which the inhalation volume is sampled from the first time period during which the number of rescue inhalations is sampled, and the second time period during which the peak inhalation flow data is sampled. The third time period may at least partially overlap with the first time period and / or the second time period, or the third time period may coincide with at least one of the first time period and the second time period.

[0201] Thus, step 26 of calculating the probability of an asthma exacerbation is based in part on the minimum or average inhalation dose. The third time period is, for example, 1-5 days. The third time period is selected according to the time it takes to collect an appropriate value of minimum inhalation data, in a manner similar to the considerations described above with respect to the first time period.

[0202] The calculation of the probability of an asthma exacerbation may be based in part on a baseline inhaled dose, such as, for example, graph 44 in FIG.

[0203] For improved accuracy in predicting exacerbations, the change in minimum or average inhalation volume relative to the baseline inhalation volume may be, for example, 10% or more (such as 50% or more or 90% or more). The baseline value is calculated using the minimum daily inhalation volume measured over a period during which no exacerbations occurred, for example 1-10 days. Alternatively or additionally, the minimum or average maximum inhalation volume is evaluated relative to an absolute value.

[0204] The method 20 further includes calculating a minimum or average inhalation duration from the inhalation durations measured for inhalations over a fourth time period. The term "fourth" with respect to the fourth time period is intended to distinguish the time period during which the minimum inhalation duration is sampled from the first time period during which the number of rescue inhalations is sampled, the second time period during which the maximum inhalation flow rate data is sampled, and the third time period during which the inhalation volume data is sampled. The fourth time period may at least partially overlap with the first time period and / or the second time period and / or the third time period, or the fourth time period may coincide with at least one of the first time period and the second time period and the third time period.

[0205] Thus, calculating 26 the probability of an asthma exacerbation is based in part on the minimum or average inhalation duration. The fourth time period is, for example, 1-5 days, such as 1 day. The fourth time period is selected according to the time required to collect suitable benchmark minimum inhalation duration data in a manner similar to the considerations described above with respect to the first time period.

[0206] Calculation of the probability of an asthma exacerbation is based in part on the change in minimum or average inhalation duration relative to a baseline inhalation duration, for example, graph 46 of FIG.

[0207] For improved accuracy of exacerbation prediction, the change in minimum or average inhalation duration relative to the baseline inhalation duration may be, for example, 10% or more (such as 50% or more or 90% or more). The baseline value is calculated using the minimum daily inhalation duration measured over a period during which no exacerbations occurred, for example 1-20 days. Alternatively or additionally, the minimum or average maximum inhalation duration is evaluated relative to an absolute value.

[0208] To better understand the factors that influence the prediction of COPD exacerbations, another clinical trial was conducted. The examples described below should be considered as illustrative, non-limiting examples.

[0209] Albuterol administered using the ProAir Digihaler® device marketed by Teva Pharmaceutical Industries was used in this 12-week, multicenter, open-label study, although the results of the study are generally applicable to other rescue medications administered using other types of devices.

[0210] The Digihaler was enabled to record the total number of inhalations, the peak inhalation flow rate, the peak inhalation flow rate versus time, the volume of inhalation and the duration of inhalation. Data was downloaded from the Digihaler's electronic module at the end of the study.

[0211] Impending exacerbation of COPD (AECOPD) was the primary outcome measure in this study. In this study, AECOPD consisted of either the occurrence of "severe AECOPD" or "moderate AECOPD." "Mild AECOPD" was not used as a measure of AECOPD in this study.

[0212] Significant AECOPD was defined as an event involving worsening respiratory disease requiring treatment with systemic corticosteroids (SCS, at least 10 mg prednisone equivalent above baseline) and / or systemic antibiotics for at least 2 consecutive days and hospitalization for treatment of AECOPD.

[0213] Moderate AECOPD is defined as an event that includes a worsening of respiratory disease requiring treatment with SCS (at least 10 mg prednisone equivalent above baseline) and / or systemic antibiotics for at least 2 consecutive days and an unscheduled contact for the treatment of AECOPD (such as a telephone call, hospital visit, urgent care visit, or emergency care visit) but not hospitalization.

[0214] Patients with COPD (age 40 or older) were recruited for the study. Patients used Proair Digihaler (90 mcg albuterol as sulfate with lactose carrier, inhaled 1 to 2 times every 4 hours) as needed.

[0215] The following inclusion criteria were required: patients had at least one moderate or severe AECOPD over the last 12 months prior to screening, were receiving at least one LABA, ICS / LABA, LAMA or LABA / LAMA in addition to a SABA, could demonstrate appropriate use of albuterol from Dizyhalar, and could discontinue use of all other rescue or maintenance SABAs or short-acting antimuscarinics for the duration of the study and switch them to Dizyhalar provided for the study.

[0216] Patients were excluded from the study if they had any clinically significant condition (treated or untreated) that, in the investigator's opinion, would preclude participation in the study, e.g., if they had an underlying confounding pulmonary disorder other than COPD, if they had used an investigational drug within 5 half-lives of dosing discontinuation or within two 1-month visits, whichever was longer, if they had congestive heart failure, if they were pregnant or breastfeeding, or if there was any possibility of becoming pregnant during the study.

[0217] Two sub-populations of approximately 100 patients were required to wear accelerometers on their ankles to measure physical activity (Total Daily Steps (TDS)) or on their wrists to measure sleep disturbances (Sleep Disturbance Index (SDI)).

[0218] General factors of interest regarding rescue medications are: (1) Total number of inhalations per day preceding the peak of AECOPD (2) Number of days when increased albuterol use preceded the peak of AECOPD (3) Number of albuterol uses in the 24 hours preceding AECOPD It was.

[0219] Approximately 400 patients were enrolled. This resulted in 366 eligible patients completing the study. 336 successful inhalations with Digihaler were recorded. Further details in this regard are shown in Table 3.

[0220] [Table 3]

[0221] 98 patients who completed the study experienced an AECOPD event and used DigiHalar. A total of 121 moderate / severe AECOPD events were recorded. Further details are shown in Table 4.

[0222] [Table 4]

[0223] Of the 366 patients who completed the study, 30 (8%) patients never used an inhaler, 268 (73%) patients used an inhaler up to an average of 5 times per day, and 11 (3%) patients used an inhaler an average of 10 or more times per day.

[0224] FIG. 9 is a graph 30b of the average number of rescue inhalations per subject versus days since a COPD exacerbation. FIG. 9 shows data within a risk period of 14 days before and after the date of an exacerbation. Line 32b corresponds to the average number of rescue inhalations per day within the risk period. Line 32b is higher on the Y-axis than the average number of rescue inhalations per day outside the baseline risk period, represented by line 34b. This represents an increase in the average number of rescue inhalations per day as the risk of an exacerbation increases. For reference, FIG. 9 also shows the average number of rescue inhalations per day for patients who did not experience a baseline exacerbation, represented by line 36b.

[0225] Figure 10 is another graph 30b of the average number of rescue inhalations per subject versus days since a COPD exacerbation. Figure 10 shows data within a 30-day risk period before and after the date of an exacerbation. Figure 10 shows a significant increase in rescue inhaler use as the date of exacerbation approaches, compared to the average daily number of rescue inhalations outside the baseline risk period, represented by line 34b.

[0226] The data show that the number of rescue medication inhalations increases during the approximately 2 weeks preceding the onset of an exacerbation. The rate of increase is smaller during the approximately 1 week preceding the onset of an exacerbation. Table 5 provides further details regarding the association between increased rescue medication use and AECOPD.

[0227] [Table 5]

[0228] [1] For patients who experienced an AECOPD event, albuterol use was initiated before the event reached maximum symptom severity. For patients who experienced multiple events, only the first event was included in the analysis. Baseline albuterol use was defined as the average number of inhalations during the first 7 days of the study. If no inhalations were taken during these first 7 days, the first available inhalation after that 7-day period was used. If no inhalations were taken during the study period, the baseline was set to zero. [2] All inferential statistics, odds ratios, p-values, and C-statistics for goodness of fit were calculated using incremental and baseline albuterol use as study variables from the logistic regression model. Odds ratios greater than 1 indicate that patients with daily albuterol use 20% above baseline were more likely to experience an AECOPD event than patients with daily albuterol use less than 20% above baseline. Patients who experienced an AECOPD between days 1 and 7 of the study were excluded from the analysis.

[0229] FIG. 11 is a graph 40b of the average peak flow rate per subject versus days since a COPD exacerbation. FIG. 11 shows data for the risk period of 14 days before and after the date of onset of an exacerbation. Line 42b is slightly higher on the Y-axis than the average peak flow rate outside the baseline risk period, represented by line 44b, but this difference appears to be of minor importance. FIG. 11 also shows the average peak flow rate for patients who did not experience a baseline exacerbation, represented by line 46b.

[0230] Figure 12 is another graph 60b of the mean peak inhalation flow rate per subject versus days since a COPD exacerbation. Figure 12 shows data for 30 days before and after the date of the exacerbation. Figure 12 shows a relatively stable, low mean peak inhalation flow rate before the onset of an exacerbation.

[0231] FIG. 13 is a graph 60b of the average amount inhaled per subject versus days since a COPD exacerbation. FIG. 13 shows data within a risk period of 14 days before and after the date of onset of an exacerbation. Line 62b corresponds to the average amount inhaled within the risk period. Line 62b is lower on the Y-axis than the average amount inhaled within the baseline risk period, represented by line 64b. FIG. 13 also shows the average amount inhaled for subjects who did not experience a baseline exacerbation, represented by line 66b.

[0232] Figure 14 is another graph 60b of the mean dose inhaled per subject versus days since a COPD exacerbation. Figure 14 shows data for a period of 30 days before and after the onset of an exacerbation.

[0233] FIG. 15 is a graph 70b of the average duration of inhalation per subject versus days since a COPD exacerbation. FIG. 15 shows data within a risk period of 14 days before and after the date of onset of an exacerbation. Line 72b corresponds to the average duration of inhalation within the risk period. Line 72b is lower on the y-axis than the average duration of inhalation outside the baseline risk period, represented by line 74b. FIG. 15 also shows the average duration of inhalation for subjects who did not experience a baseline exacerbation, represented by line 76b.

[0234] Figure 16 is another graph 70b of the mean inhalation duration versus days since a COPD exacerbation per subject. Figure 16 shows data within a 30 day period before and after the onset of an exacerbation.

[0235] 13-16 reveal a relatively long-term (demonstrated over approximately 30 days) linear decrease in inhaled volume and duration prior to AECOPD.

[0236] Table 6 compares the inhalation parameters and number of inhalations of rescue medication recorded for patients inside and outside the ±14-day window of AECOPD with those for patients who did not experience AECOPD.

[0237] [Table 6]

[0238] The baseline mean number of inhalations of albuterol per day for patients who experienced an exacerbation was slightly higher than for patients who did not experience an exacerbation, and the baseline mean volume and duration of inhalations for patients who experienced an exacerbation was slightly lower than for patients who did not experience an exacerbation.Within the ±14-day AECOPD window, patients took a higher mean number of inhalations of albuterol per day than baseline (outside the ±14-day AECOPD window) and than patients who did not experience an AECOPD.

[0239] In contrast to the predictive models for asthma exacerbations described above, the strongest predictors of COPD exacerbations were found to be airflow-related parameters, such as peak inspiratory flow and / or inhaled volume and / or inhalation duration. Also, the number of rescue inhalations was found to have significant predictive value.

[0240] Based on the above results, a weighted model was developed to calculate the probability of a COPD exacerbation. A supervised machine learning technique, Gradient Boosting Trees, was used to solve the classification problem (whether an exacerbation will occur in the next x days (exacerbation duration)).

[0241] Table 7 shows an exemplary list of factors included in the weighted model, along with their relative weighting to one another.

[0242] [Table 7]

[0243] The generalized model was evaluated by receiver operating characteristic (ROC) curve analysis. The most significant factors in the prediction model for calculating the probability of impending COPD exacerbation were the inhalation parameters, which were strengthened by supplementing them with data on the number of rescue inhalations. Figure 17 shows the receiver operating characteristic (ROC) curve analysis of the model, evaluating the quality of the model by plotting the true positive rate against the false positive rate. The model predicted impending exacerbation over the following 5 days with an area under the ROC curve (AUC) value of 0.77.

[0244] When predicting COPD exacerbations, the number of rescue inhalations is a significant factor in improving the accuracy of exacerbation probability calculations, even though it has a smaller effect on the overall probability than inhalation parameters.

[0245] If the parameter includes a maximum inhalation flow rate, the method 20 is further configured to determine a maximum inhalation flow rate, such as a minimum or average maximum inhalation flow rate, from the maximum inhalation flow rates measured for inhalations performed within the second time period. The term "second" with respect to the second time period means that the time period for sampling the maximum inhalation flow rate should be distinct from the first time period during which the number of rescue inhalations is sampled. The second time period may at least partially overlap with the first time period, or the first and second time periods may be coincident.

[0246] Thus, calculating the probability of a COPD exacerbation at step 26 is based in part on the minimum or average peak inhalation flow rate. The second period is selected according to the period required to collect data for an appropriate value of peak inhalation flow rate, similar to the considerations made above with respect to the first period.

[0247] Calculation of the probability of a COPD exacerbation is based in part on the change in minimum or mean peak inhalation flow rate relative to baseline inhalation flow rate, for example as shown in FIGS.

[0248] To improve the accuracy of exacerbation prediction, it is preferred that the change in minimum or average inhalation flow rate from baseline is 10% or more, e.g., 50% or more, or 90% or more. The baseline is determined using the minimum daily maximum inhalation flow rate measured over a period of no exacerbation, e.g., 1 to 20 days, e.g., 10 days. Alternatively or additionally, the minimum or average maximum inhalation flow rate is evaluated against an absolute value.

[0249] The method 20 includes determining an inhalation volume, such as a minimum or average inhalation volume, from the inhalation volumes measured for inhalations made within a third time period. The term "third" with respect to the third time period is meant to distinguish the time period for sampling the inhalation volume from the first time period during which the number of rescue inhalations is sampled and the second time period during which peak inhalation flow data is sampled. The third time period may at least partially overlap with the first time period and / or the second time period, or may coincide with at least one of the first time period and the second time period.

[0250] Thus, calculating the probability of a COPD exacerbation 26 is based in part on the minimum or average inhaled volume. The third time period is selected according to the period required to collect data for the appropriate inhaled volume, similar to the considerations made above with respect to the first time period.

[0251] The calculation of the probability of a COPD exacerbation is based in part on the change in minimum or average inhaled volume relative to the baseline inhaled volume, for example as shown in Figures 13 and 14.

[0252] To improve the accuracy of exacerbation prediction, it is preferred that the minimum or average inhalation changes from the baseline value by 10% or more, e.g., 50% or more, or 90% or more. The baseline value is determined using the minimum daily inhalation measured over a period of no exacerbation, e.g., 1 to 20 days, e.g., 10 days. Alternatively or additionally, the minimum or average inhalation is evaluated against an absolute value.

[0253] The method 20 includes determining a minimum or average inhalation duration from the inhalation durations measured for inhalations made within a fourth time period. The term "fourth" with respect to the fourth time period is meant to distinguish the time for sampling the inhalation duration from the first time period during which the number of rescue inhalations is sampled, the second time period during which the peak inhalation flow rate is sampled, and the third time period during which the inhalation volume is sampled. The fourth time period may at least partially overlap with the first time period and / or the second time period and / or the third time period, or may coincide with at least one of the first time period and the second time period and the third time period.

[0254] Calculating the probability of a COPD exacerbation 26 is based in part on the minimum or average inhalation duration. The fourth period is selected depending on the period required to collect data for an appropriate minimum inhalation duration, similar to the considerations made above with respect to the first period.

[0255] Calculation of the probability of a COPD exacerbation is based in part on the change in minimum or average inhalation duration relative to baseline, for example, as shown in FIGS.

[0256] To improve the accuracy of exacerbation prediction, it is preferred that the minimum or average inhalation duration changes from the baseline value by 10% or more, e.g., 50% or more, or 90% or more. The baseline value is determined using the average inhalation duration measured over a period of time during which no exacerbation has occurred, e.g., 1 to 20 days, e.g., 10 days. Alternatively or additionally, the minimum or average inhalation duration is evaluated against an absolute value.

[0257] 18-22 show non-limiting examples of inhaler devices that may be included in system 10. FIG.

[0258] FIG. 18 is a perspective view of an inhaler device (hereafter referred to as "inhaler") 100 according to a non-limiting example. The inhaler 100 is, for example, a self-inhaling inhaler. The inhaler 100 has a top cap 102, a main housing 104 and / or a mouthpiece 106 and / or a mouthpiece cover 108 and / or an electronic module 120 and / or a ventilation opening 126. The mouthpiece cover 108 is connected to the main housing 104 by a hinge such that the mouthpiece 106 is exposed or retracted by opening and closing the mouthpiece cover. Although a hinge connection is used in this example, the mouthpiece cover 108 may be connected to the inhaler 100 by other types of connection. Furthermore, although the electronic module 120 is housed in the top cap 102 at the top of the main housing 104 in this example, the electronic module 120 may be integrated with and / or housed inside the main housing 104 of the inhaler 100.

[0259] FIG. 19 is a longitudinal cross-sectional view of the inhaler 100 shown in FIG. 18. Inside the main housing 104, the inhaler 100 includes a drug reservoir 110 (e.g., a hopper), a bellows 112, a bellows spring 114, a yoke (not shown), a dosing cup 116, a dosing chamber 117, a disintegrator 121, and a flow passage 119. The drug reservoir 110 contains a drug, such as a dry powder drug, for administration to a subject. When the mouthpiece cover 108 is moved from the closed position to the open position, the bellows 112 supplies a dose of the drug from the drug reservoir to the dosing cup 116. The subject then inhales through the mouthpiece 106 to inhale the dose of the drug.

[0260] Airflow caused by the subject's inhalation causes the disintegrator 121 to aerosolize a dose of the drug by breaking up agglomerates of the drug in the dosing cup 116. The disintegrator 121 is configured to aerosolize the drug when airflow through the flow passage 119 reaches or exceeds a particular velocity or has a velocity within a particular range. When aerosolized, the dose of drug flows from the dosing cup 116 through the flow passage 119 into the dosing chamber 117 and is then delivered to the subject through the mouthpiece 106. When airflow through the flow passage 119 does not reach or exceed a particular velocity or is not within the particular range, the drug remains in the dosing cup 116. If the drug in the dosing cup 116 is not aerosolized by the disintegrator 121, a next dose of drug will not be delivered from the drug reservoir 110 when the mouthpiece cover 108 is subsequently opened. That is, the dose of medicine remains in the dosing cup until it is aerosolized by disintegrator 121. When the dose of medicine has been administered, confirmation that the dose has been administered is stored in memory in inhaler 100 as dosing confirmation information.

[0261] When the subject inhales through the mouthpiece 106, air enters through the vent hole, creating an airflow for delivering medicament to the subject. The flow passage 119 extends from the dosing chamber 117 to the end of the mouthpiece 106 and includes the dosing chamber 117 and an interior portion of the mouthpiece 106. The dosing cup 116 is within or adjacent to the dosing chamber 117. The inhaler 100 further includes a dose counter 111 configured to initially set a total number of doses of medicament in the medicament reservoir 110 and to subtract one from the total number of doses each time the mouthpiece cover 108 is moved from the closed position to the open position.

[0262] The top cap 102 is attached to the main housing 104. The top cap 102 is attached to the main housing 104, for example, using one or more clips that may engage recesses in the main housing 104. When coupled, the top cap 102 overlaps a portion of the main housing 104 such that, for example, a substantially airtight seal is created between the top cap 102 and the main housing 104.

[0263] Figure 20 is an exploded perspective view of the inhaler 100 shown in Figure 18 with the top cap 102 removed to expose the electronic module 120. As shown in Figure 20, the top surface of the main housing 104 has one or more (e.g., two) orifices 146. One orifice 146 is configured to receive a slider 140. For example, when the top cap 102 is attached to the main housing 104, the slider 140 protrudes from the top surface of the main housing 104 through one orifice 146.

[0264] FIG. 21 is an exploded perspective view of the top cap 102 and electronic module of the inhaler 100 shown in FIG. 18. As shown in FIG. 21, the slider 140 forms an arm 142, a stopper 144, and a distal end 145. The distal end 145 forms the bottom of the slider 140. The distal end 145 of the slider 140 is configured to abut a yoke inside the main housing 104 (e.g., when the mouthpiece cover 108 is in a closed or partially open position). The distal end 145 is configured to abut an upper surface of the yoke when the yoke is in any of the radial directions. For example, the upper surface of the yoke has a plurality of apertures (not shown), and the distal end 145 of the slider 140 is configured to abut an upper surface of the yoke, for example, whether or not one aperture is aligned with the slider 140.

[0265] The top cap 102 has a slider spring 146 and a slider guide 148 configured to receive the slider 140. The slider spring 146 is located within the slider guide. The slider spring 146 engages an inner surface of the top cap 102 and engages an upper portion (e.g., a proximal end) of the slider 140. When the slider 140 is disposed within the slider guide 148, the slider spring 146 is partially compressed between the upper portion of the slider 140 and the inner surface of the top cap 102. For example, the slider spring 146 is configured such that the distal end 145 of the slider 140 contacts the yoke when the mouthpiece cover 108 is closed. The distal end 145 of the slider 145 is also configured to contact the yoke while the mouthpiece cover 108 is open or closed. The stopper 144 of the slider 140 engages with the slider guide 148, for example, such that the slider 140 is retained within the slider guide 148 through opening and closing of the mouthpiece cover 108 and vice versa. The stopper 144 and the slider guide 148 are configured to limit the up-down (e.g., axial) movement of the slider 140. This limited movement distance is less than the limited up-down movement distance of the yoke. That is, when the mouthpiece cover 108 is moved to a fully open position, the yoke continues to move up-down toward the mouthpiece 106, but the stopper 144 stops the up-down movement of the slider 140, such that the distal end 145 of the slider 140 no longer contacts the yoke.

[0266] More generally, the yoke is mechanically coupled to the mouthpiece cover 108 and is configured to compress the bellows spring 114 when the mouthpiece cover 108 is opened from the closed position, and then release the compressed bellows spring 114 when the mouthpiece cover 108 reaches the open position, thereby causing the bellows 112 to dispense a dose of medication from the medication reservoir 110 to the dosage cup 116. The yoke contacts the slider 140 when the mouthpiece cover 108 is in the closed position. The slider 140 is adapted to be moved by the yoke when the mouthpiece cover 108 is opened from the closed position, and to be moved away from the yoke when the mouthpiece cover 108 assumes the fully open position. This configuration is considered a non-limiting example of the metering assembly already described above, since opening the mouthpiece cover 108 results in the metering of a dose of medication.

[0267] Movement of the slider 140 during metering of medication causes the slider 140 to engage and activate the switch 130. The switch 130 activates the electronic module 120 to record the metered dose of medication. The slider 140 and the switch 130, together with the electronic module 120, correspond to a non-limiting example of the usage detection system 12B described above. In this example, the slider 140 is considered as a means by which the usage detection system 12B is configured to record the metering of a dose of medication by the metering assembly, whereby each metered dose represents an inhalation taken by the subject using the inhaler 100.

[0268] Actuation of switch 130 by slider 140 transitions electronic module 120 from a first power state to a second power state, for example, and detects inhalation through mouthpiece 108 by the subject.

[0269] The electronic module 120 includes a printed circuit board (PCB) assembly 122 and / or a switch 130 and / or a power source (e.g., battery 126) and / or a battery holder 124. The PCB assembly 122 includes surface mount components such as a sensor system 128 and / or wireless communication circuitry 129 and / or a switch 130 and / or an indicator (not shown) such as one or more light emitting diodes (LEDs). The electronic module 120 includes a controller (e.g., a processor) and / or memory. The controller and / or memory may be physically separate components of the PCB 122. Alternatively, the controller and memory may be part of a separate chipset mounted on the PCB 122, e.g., the wireless communication circuitry 129 includes the controller and / or memory for the electronic module 120. The controller of electronic module 120 may include a microcontroller, a programmable logic device (PLD), a microprocessor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or any suitable processing device or control circuitry.

[0270] The controller reads data from and records data in the memory. The memory includes any type of suitable memory, such as non-removable and / or removable memory. Non-removable memory includes random access memory (RAM), read only memory (ROM), hard disk, or any other type of storage device. Removable memory includes subscriber identity module (SIM) cards, memory sticks, secure digital (CD) memory cards, and the like. The memory is located within the controller. The controller also reads data from and records data in memory that is not physically located within the electronic module 120, such as on a server or a smartphone.

[0271] The sensor system 128 includes one or more sensors. The sensor system 128 is an example of the sensor system 12A. The sensor system 128 includes one or more different types of sensors, such as, for example, one or more pressure sensors and / or temperature sensors and / or humidity sensors and / or directional sensors and / or acoustic sensors and / or optical sensors. The one or more pressure sensors include air pressure sensors (e.g., atmospheric pressure sensors) and / or differential pressure sensors and / or absolute pressure sensors and / or the like. The sensors use Microelectromechanical Systems (MEMS) and / or Nanoelectromechanical Systems (NEMS) technology. The sensor system 128 is configured to provide instantaneous measurements (e.g., pressure measurements) and / or measurements aggregated over a period of time (e.g., pressure measurements) to the controller of the electronic module 120. As shown in FIG. 19 and FIG. 20, the sensor system 128 is disposed outside the flow path 119 of the inhaler 100, but is pneumatically coupled to the flow path.

[0272] A controller in the electronic module 120 receives signals corresponding to the measurements from the sensor system 128. The controller uses the signals received from the sensor system 128 to calculate or determine airflow metrics that represent the airflow profile through the flow path 119 of the inhaler 100. For example, when the sensor system 128 records an air pressure change of 0.3 kilopascals (kPa), the electronic module 120 calculates that the air pressure change corresponds to an air flow rate of approximately 45 liters per minute (Lpm) through the flow path 119.

[0273] Figure 22 is a graph of airflow versus pressure. The airflow and profile shown in Figure 22 are merely exemplary and the calculated velocities will depend on the size, shape and design of the inhaler 100 and its components.

[0274] The controller of the electronic module 120 generates real-time personalized data by comparing the signals received from the sensor system 128 and / or the calculated airflow metrics to one or more thresholds or ranges of values ​​as part of an evaluation of how the inhaler 100 has been used and / or whether its use may result in a full dose of medication. For example, when the calculated airflow metrics correspond to an inhalation with an airflow rate below a certain threshold, the electronic module 120 determines that there is no inhalation or that there is insufficient inhalation through the mouthpiece 106 of the inhaler 100. When the calculated airflow metrics correspond to an inhalation with an airflow rate above a certain threshold, the electronic module 120 determines that there is too much inhalation through the mouthpiece 106 of the inhaler 100. When the calculated airflow metrics correspond to an inhalation with an airflow rate within a certain range, the electronic module 120 determines that there is a good inhalation or that there may have been a full dose.

[0275] The pressure measurements and / or calculated airflow related metrics are indicative of the quality or strength of the inhalation from the inhaler 100. For example, when compared to certain thresholds or numerical ranges, the measurements and / or metrics are used to classify the associated inhalation into a certain event type, such as a good inhalation event, a poor inhalation event, a no inhalation event or an excessive inhalation event. The classification of the inhalation is a useful parameter recorded as personalized data for the subject.

[0276] A no-inhale event is associated with a pressure measurement and / or airflow metric below a particular threshold, such as an airflow rate below 30 Lpm. A no-inhale event occurs when the subject does not inhale through the mouthpiece 106 after opening the mouthpiece cover 108 and during a measurement cycle. A no-inhale event also occurs when the subject's inhalation is insufficient to ensure proper administration of medication through the flow path 119, such as when the subject's inhalation generates insufficient airflow to activate the disintegrator 121, i.e., to aerosolize the medication in the dosing cup 116.

[0277] A low inhalation event is associated with pressure measurements and / or airflow metrics that fall within a particular range, such as 30 Lpm to 45 Lpm. A low inhalation event occurs when the subject inhales through mouthpiece 106 after opening mouthpiece cover 108, and the subject's inhalation results in only at least a portion of the dose of medication being dispensed through flow path 119. That is, the inhalation is insufficient to activate disintegrator 121 to aerosolize at least a portion of the medication from dosing cup 116.

[0278] A successful inhalation event is associated with pressure measurements and / or airflow metrics above a low inhalation event, such as between 45 Lpm and 200 Lpm. A successful inhalation event occurs when the subject inhales through mouthpiece 106 after opening mouthpiece cover 108 and the subject's efforts result in sufficient inhalation to ensure proper dosing of medication through flow path 119, such as when the subject's efforts generate sufficient airflow to activate disintegrator 121 and aerosolize an entire dose of medication in dosing cup 116.

[0279] An excessive inhalation event is associated with a pressure measurement and / or airflow related metric above a good inhalation event, such as an airflow rate above 200 Lpm. An excessive inhalation event occurs when a subject inhales beyond the normal operating parameters of the inhaler 100. An excessive inhalation event also occurs when the inhaler 100 is not properly positioned or held during use, even if the subject inhales within the normal range. For example, the calculated airflow rate exceeds 200 Lpm when the vent is blocked or obstructed (e.g., by a finger or thumb) while the subject is inhaling through the mouthpiece 106.

[0280] Any suitable threshold or range of values ​​may be used to classify a particular event. Some or all of the events may be used. For example, a no inhalation event may be associated with an airflow rate below 45 Lpm, and a good inhalation event may be associated with an airflow rate within the range of 45 Lpm to 200 Lpm. Thus, a low inhalation event may not be used at all.

[0281] The pressure measurements and / or calculated airflow metrics also indicate the direction of airflow through the flow passage 119 of the inhaler 100. For example, when the pressure measurements reflect a negative pressure change, the pressure measurements represent airflow exiting the mouthpiece 106 through the flow passage 119. When the pressure measurements reflect a positive pressure change, the pressure measurements represent airflow entering the mouthpiece 106 through the flow passage 119. Thus, the pressure measurements and / or airflow metrics can be used to determine if the subject is exhaling air into the mouthpiece 106, thereby indicating that the subject is not using the inhaler 100 correctly.

[0282] The inhaler 100 may include a spirometer or similar device to allow for the measurement of metrics related to lung function. For example, the inhaler 100 may perform measurements to obtain metrics related to the subject's lung capacity. The spirometer or similar device may measure the volume of air inhaled and / or exhaled by the subject. The spirometer or similar device may use a pressure transducer, ultrasound, or water level sensor to detect changes in the volume of inhaled and / or exhaled air.

[0283] Personalized data collected from or calculated based on use of the inhaler 100 (e.g., pressure metrics, airflow metrics, lung function metrics, medication confirmation information, etc.) may further be calculated and / or evaluated (in part or in whole) by an external device. More generally, the wireless communication circuitry 129 of the electronic module 120 includes not only a transmitter and / or a receiver (e.g., a transceiver) but also additional circuitry. For example, the wireless communication circuitry 129 includes a Bluetooth chipset (e.g., a Bluetooth low power chipset), a ZigBee chipset, a Thread chipset, and the like. Thus, the electronic module 120 wirelessly provides personalized data, such as pressure measurements and / or airflow metrics and / or lung function metrics and / or medication confirmation information and / or other aspects of the use of the inhaler 100, to an external device, including a smartphone. The personalized data is provided to the external device in real time, which allows prediction of exacerbation risk based on personalized data about the user of the inhaler, such as real time data from the inhaler 100 indicating the number of uses and how the inhaler 100 was used, and real time data about the subject's lung function and / or treatment. The external device includes software for processing the received information and providing compliance feedback to the user of the inhaler 100 via a graphic user interface (GUI).

[0284] The airflow metrics include personalized data collected in real time from the inhaler 100, such as one or more of the average inhalation / exhalation flow rate and / or the maximum inhalation / exhalation flow rate (e.g., maximum inhalation flow rate received) and / or the inhalation / exhalation volume and / or time vs. inhalation / exhalation maximum and / or duration of inhalation / exhalation. The airflow metrics also represent the direction of airflow through the flow path 119. That is, a negative pressure change corresponds to inhalation from the mouthpiece 106, while a positive pressure change corresponds to exhalation into the mouthpiece 106. In calculating the airflow metrics, the electronic module 120 is configured to eliminate or minimize distortions caused by environmental conditions. For example, the electronic module 120 performs calculations to take into account changes in atmospheric pressure before or after calculating the airflow metrics. One or more pressure measurements and / or airflow metrics are time-stamped and then recorded in the memory of the electronic module 120.

[0285] In addition to the airflow metrics, the inhaler 100 or another computing device uses the airflow metrics to generate additional personalized data. For example, the controller of the electronic module 120 of the inhaler 100 translates the airflow metrics into other metrics that represent the subject's lung function and / or lung health, which may be interpreted as medical parameters, such as, for example, maximum inspiratory flow rate and / or maximum expiratory flow rate and / or forced expiratory volume in one second (FEV1). The electronic module 120 of the inhaler 100 calculates the subject's lung function and / or lung health measures using a mathematical model, such as a regression model. The mathematical model identifies a correlation between the total inhaled volume and FEV1. The mathematical model identifies a correlation between the maximum inhaled flow rate and FEV1. The mathematical model identifies a correlation between the total inhaled volume and maximum expiratory flow rate. The mathematical model identifies a correlation between the maximum inhaled flow rate and maximum expiratory flow rate.

[0286] The battery 126 provides power to the components of the PCB 122. The battery 126 may comprise any suitable power source for providing power to the electronic module 120, such as, for example, a coin cell battery. The battery 126 may be rechargeable or non-rechargeable. The battery 126 is housed in a battery holder 124. The battery holder 124 is attached to the PCB 122 such that the battery 126 is in continuous contact with the PCB 122 and / or in electrical contact with components of the PCB 122. The battery 126 has a particular battery capacity that affects the lifespan of the battery 126. As will be further explained below, the distribution of power from the battery 126 to one or more components of the PCB 122 is managed to ensure that the battery 126 powers the electronic module 120 for the useful life of the inhaler 100 and / or the shelf life of the medication contained therein.

[0287] Once connected, the communication circuitry and memory are powered on and the electronic module 120 is paired with an external device such as a smartphone. The controller retrieves data from the memory and wirelessly transmits the data to the external device. The controller retrieves and transmits the data most recently stored in the memory. The controller also retrieves and transmits a portion of the data most recently stored in the memory. For example, the controller can determine what portions of the data have already been transmitted to the external device and then transmit the portions of the data that have not yet been transmitted. Alternatively, the external device requests specific data from the controller, such as data collected by the electronic module 120 after a specific time or after the most recent transmission to the external device. The controller retrieves the specific data, if any, from the memory and transmits the retrieved data to the external device.

[0288] The data stored in the memory of the electronic module 120 (e.g., signals generated by the switches and / or pressure measurements obtained by the sensor system 128 and / or airflow related metrics calculated by the controller of the PCB 122) are transmitted to an external device. The external device processes and analyzes the data and calculates useful parameters related to the inhaler 100. Additionally, a mobile application residing on the mobile device generates feedback to the user based on the data received from the electronic module 120. For example, the mobile application may generate daily, monthly, or yearly reports and / or identify or notify the subject of error events and / or provide useful feedback to the subject and / or the like.

[0289] The above-described embodiment is merely an example for explaining the configuration of the present invention, and therefore does not limit the configuration of the present invention. Other variations of the above-described embodiment can be understood and achieved by those skilled in the art in the practice of the present invention from studying the drawings, the specification and the appended claims. The fact that certain measures are recited in mutually different dependent claims does not indicate that a combination of these measures cannot be used to advantage.

Claims

1. 1. A system for calculating a probability of a respiratory exacerbation in a subject, comprising: an inhaler device for administering a medicament to the subject, the inhaler device having a usage detection system configured to measure inhalations performed by the subject using the inhaler device; a sensor system configured to measure a parameter related to airflow during said inhalation; a user interface configured to receive an input of an indicator representative of a respiratory disease symptom experienced by the subject; recording each of said inhalations; receiving the parameter measured during the inhalation; receiving the indicator; and a processor configured to calculate a probability of the respiratory disease exacerbation based on the recorded inhalation and the received parameter measurements and the received indicator.

2. The system of claim 1, wherein the processor is configured to first calculate an initial probability of a respiratory disease exacerbation based on the recorded inhalations and the measurements of the received parameters but not based on the indicator, and then calculate the probability of the respiratory disease exacerbation based on the recorded inhalations and the measurements of the received parameters and the received indicator.

3. 3. The system of claim 2, wherein the processor is configured to control the user interface to output a prompt for inputting the indicator based on the initial probability, and optionally based on the initial probability reaching or exceeding a predetermined threshold.

4. The system of any one of claims 1 to 3, characterized in that the user interface is configured to provide a plurality of user-selectable respiratory disease condition options, and the indicator is defined by a user's selection of at least one of the respiratory disease condition options.

5. 5. The system of claim 4, wherein the user interface is configured to provide the respiratory disease condition options in the form of selectable icons and / or checkboxes and / or sliders and / or dials.

6. The system of any one of claims 1 to 5, characterized in that the user interface is at least partially comprised of a first user interface of a user device in communication with the inhaler device, and optionally, the user device is comprised of at least one of a personal computer, a tablet computer and a smartphone.

7. 7. The system of claim 6, wherein the processor is at least partially contained in a first processor contained in the user device.

8. A system as described in any one of claims 1 to 7, characterized in that the user interface is at least partially comprised by a second user interface included in the inhaler device and / or the processor is at least partially comprised in a second processor included in the inhaler device.

9. The system of any one of claims 1 to 8, characterized in that the inhaler device comprises a first inhaler for administering a rescue medication to the subject, the usage detection system is configured to measure inhalation of the rescue medication, and the probability of a respiratory disease exacerbation is based on the recorded inhalation of the rescue medication and measurements of the received parameters and the received indicators, and optionally the sensor system is configured to measure the parameters during inhalation of a pre-rescue medication.

10. The system of any one of claims 1 to 9, characterized in that the inhaler device has a second inhaler for administering a maintenance medication to the subject, the usage detection system is configured to measure regular inhalation of the maintenance medication, and the probability of a respiratory disease exacerbation is based on the recorded inhalation of the maintenance medication and measurements of the received parameters and the received indicators, and optionally a sensor system is configured to measure the parameters during inhalation of the maintenance medication.

11. The system according to any one of claims 1 to 10, characterized in that the parameters comprise at least one of a maximum inhalation flow rate, an inhalation volume, and an inhalation duration.

12. The system of claim 11, wherein the processor is further configured to determine a minimum maximum inhalation flow rate from the maximum inhalation flow rates measured during multiple inhalations, and the probability of a respiratory disease exacerbation is based in part on the minimum maximum inhalation flow rate, and optionally, the processor is configured to calculate the probability of a respiratory disease exacerbation based in part on a change in the minimum maximum inhalation flow rate relative to a baseline maximum inhalation flow rate.

13. The system of claim 11 or claim 12, wherein the processor is further configured to determine a minimum inhalation volume from the inhalation volumes measured during a plurality of the inhalations, and the probability of the respiratory disease exacerbation is based in part on the minimum inhalation volume, and optionally the processor is configured to calculate the probability of the respiratory disease exacerbation based in part on a change in the minimum inhalation volume relative to a baseline minimum inhalation volume.

14. The system of any one of claims 11 to 13, wherein the processor is further configured to determine a minimum inhalation duration from the inhalation durations measured during multiple inhalations, and the probability of a respiratory disease exacerbation is based in part on the minimum inhalation duration, and optionally the processor is configured to calculate the probability of a respiratory disease exacerbation based in part on a change in the minimum inhalation duration relative to a baseline inhalation duration.

15. A system as described in any one of claims 1 to 14, characterized in that the sensor system comprises a pressure sensor, and optionally, the usage detection system comprises another pressure sensor, the pressure sensor and the another pressure sensor having the same configuration or different configurations from each other.

16. The inhaler device comprises: A chemical tank, a metering assembly configured to meter a dose of drug from the drug reservoir; A system as described in any one of claims 1 to 15, characterized in that the usage detection system is configured to record the metering of the dose of medication by the metering assembly, whereby the metering of the dose of medication is indicative of the inhalation performed by the subject using the inhaler device.

17. 1. A method for calculating the probability of a respiratory exacerbation in a subject, comprising: recording inhalations of medication performed by said subject; receiving a measurement of a parameter related to airflow measured during said inhalation; receiving input of an indicator representing a symptom of a respiratory disease experienced by the subject; The method further comprises calculating a probability of said respiratory exacerbation based on said recorded inhalations and said received parameter measurements and said received index.

18. 18. The method of claim 17, further comprising the steps of: calculating an initial probability of a respiratory disease exacerbation based on the recorded inhalations and the measurements of the received parameters but not based on the index; and then calculating the probability of the respiratory disease exacerbation based on the recorded inhalations and the measurements of the received parameters and the received index.

19. 20. The method of claim 17, further comprising prompting a user to input the index based on the initial probability, optionally based on the initial probability meeting or exceeding a predetermined threshold.

20. A computer program having a computer program code adapted to perform the method according to any of claims 17 to 19, when the computer program is run on a computer.

21. 1. A method of treating a respiratory exacerbation in a subject, comprising: Carrying out the method according to any one of claims 17 to 19, determining whether the probability reaches or exceeds a predetermined upper limit, or determining whether the probability reaches or is below a predetermined lower limit; treating said respiratory exacerbation based on said probability reaching or exceeding said upper limit or based on said probability reaching or falling below said lower limit.

22. 22. The method of claim 21, wherein the treatment comprises switching the subject from a first treatment plan to a second treatment plan based on the probability that the upper limit has been reached or exceeded, the second treatment plan being designed to address a higher risk of respiratory disease exacerbation than the first treatment plan.

23. 23. The method of claim 22, wherein the second treatment regimen includes administration of a biopharmaceutical, and optionally, the biopharmaceutical comprises any one of omalizumab, mepolizumab, reslizumab, benralizumab, and dupilumab, or a combination of two or more thereof.

24. 22. The method of claim 21, wherein the treatment comprises switching the subject from a first treatment plan to a third treatment plan based on the probability reaching or being below the lower limit, the third treatment plan being designed to address a lower risk of respiratory disease exacerbation than the first treatment plan.

25. 1. A method for diagnosing a respiratory disease exacerbation, comprising: Carrying out a method according to any one of claims 17 to 19, determining whether the probability reaches or exceeds a predetermined upper limit indicative of the respiratory disease exacerbation; and diagnosing said respiratory exacerbation based on said probability of said upper limit being reached or exceeded.

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