Non-invasive ventilated patient monitoring system

Abstract

Title of the Invention: Non-Invasive Ventilation Monitoring System. The invention relates to a monitoring system for a person receiving non-invasive artificial ventilation (NIV) using a medical ventilator supplying a breathing mask with a respiratory gas. The medical ventilator provides ventilation variables. A computer server receives and processes these ventilation variables to determine several statistical indicators for each variable and derive a ventilation quality score using a mathematical model that compares these statistical indicators with stored thresholds obtained through machine learning. Finally, at least one significant ventilation variable is identified among these variables, impacting the ventilation quality score more than the other ventilation variables. Abstract Figure: Figure 1

Description

Title of the invention: Non-invasive ventilated patient monitoring system

[0001] The present invention relates to a system for monitoring a person artificially ventilated in a non-invasive manner by means of a medical ventilator supplying a breathing mask providing a breathing gas, such as air or a pressurized air / O2 mixture, to said person in need.

[0002] Non-invasive ventilation (NIV) is a mechanical aid to breathing by means of a respiratory device, i.e. a medical ventilator, which delivers pressurized air through a mask applied to the face of a person to be artificially ventilated, typically a patient.

[0003] Non-invasive ventilation (NIV) is a recognized treatment for chronic hypercapnic respiratory failure. This treatment improves symptoms by reducing the workload of the respiratory muscles and improving gas exchange.

[0004] Furthermore, it can be easily implemented at the patient's home, i.e., outside of a hospital setting. Therefore, in the context of long-term treatment, NIV can reduce the number of hospitalizations for patients with respiratory failure.

[0005] In all cases, the long-term success of NIV depends on the acceptance and participation of the patient and their family in the treatment. It is therefore necessary to monitor the patient remotely, particularly when they are treated at home, i.e., outside of a hospital setting.

[0006] Patient treatment monitoring or follow-up can be carried out by analyzing telemetry data transmitted by the medical ventilator when it is connected, as well as by collecting the patient's experience which is assessed with a questionnaire (or questionnaires), for example the one called "S3-NIV" which includes 11 questions relating to respiratory symptoms, sleep quality and side effects of NIV treatment, so as to determine a treatment score for each patient.

[0007] While a score is a very useful tool for monitoring a ventilated patient, it requires human intervention (i.e., intervention by a healthcare professional at the patient's home) in order to be collected. This is operationally demanding and therefore difficult to deploy on a large scale.

[0008] In view of this, a problem is to be able to carry out effective remote monitoring of a patient treated with NIV at home, which does not require human intervention and preferably allows the patient's ventilation quality score to be predicted from telemetry data provided by the ventilator delivering the NIV treatment to the ventilated patient in question.

[0009] One solution of the invention relates to a system for monitoring a person receiving artificial ventilation, i.e., a patient, in a non-invasive (NIV) manner using a medical ventilator supplying a breathing mask providing a respiratory gas, such as air or an air / O2 mixture, to said person, comprising: - the medical ventilator configured to provide ventilation variables including at least one compliance, mask leaks, apnea / hypopnea index (i.e., AHI), respiratory rate, spontaneous breathing rate (%), and tidal volume, and - at least one computer server configured to receive and process the ventilation variables provided by the medical ventilator.

[0010] Furthermore, said at least one computer server of the tracking system of the invention is configured to: a. Treat the ventilation variables as follows:

[0011] i) determining for each ventilation variable, several statistical indicators chosen from a mean, a standard deviation, an eccentricity (i.e. skewness), a kurtosis, a trend, a variance, a median and an amplitude (i.e. max-min),

[0012] ii) calculating a ventilation quality score from said statistical indicators using a mathematical model allowing a comparison of said statistical indicators to stored thresholds obtained by machine learning, and

[0013] iii) determining, among said ventilation variables, at least one significant ventilation variable impacting the ventilation quality score to a greater extent than the rest of the ventilation variables, a. order a display on display means, of the ventilation quality score determined in a) ii) and of said at least one significant ventilation variable, and b. generate a ventilation quality alert when the ventilation quality score is below a predefined threshold value stored within the computer server.

[0014] Depending on the embodiment considered, the invention may include one or more of the following features: - The medical ventilator includes a blower (i.e. turbine) delivering the respiratory gas. - breathing gas is air, typically air under pressure (>1 atm). - ventilation variables are "machine" data from the medical ventilator. - the statistical indicators preferentially include a mean, a standard deviation, an eccentricity (ie skewness), kurtosis and a trend. - ventilation variables include compliance, mask leaks, apnea / hypopnea index (i.e. AHI) and respiratory rate. - it further includes alerting means configured to trigger an alert when the ventilation quality score is above a predetermined threshold value, and said at least one computer server cooperates with the display device 6 to display the alert having been triggered by the alerting means. - the mathematical model includes a learning algorithm. - the display device is configured to simultaneously display the ventilation quality score and at least one significant ventilation variable impacting said ventilation quality score in a greater way than the other (i.e., the rest of the) ventilation variables. - the display device is configured to display a list of several patients ranked from lowest to highest ventilation score. - the display device is configured to display patients grouped according to their ventilation quality scores, preferably the patient groups are displayed in different colors. - the display device is configured to display a graphical representation of at least one ventilation variable of a given patient in the form of a curve or barograph over several days. - the computer means of data processing are configured to process the measurements of ventilation variables obtained over a time period of 4 to 30 days, preferably 4 to 15 days. - it includes alerting means configured to generate the ventilation quality alert. - the alert methods are configured to trigger an audible or visual alert at a healthcare professional operating remote patient monitoring (PSAD).

[0015] The invention will now be better understood with reference to the following detailed description, given by way of illustration but not limitation, with reference to the accompanying figures, among which:

[0016] [Fig.1] schematically illustrates an embodiment of a system according to the invention.

[0017] [Fig.2] schematizes a method of implementing a decision chain enabling predicting the ventilation quality score and determining its causes, implemented by a system according to the invention, and

[0018] [Fig.3] schematically illustrates one embodiment of the display and visualization means ventilation quality scores, ventilation variables and ventilation quality causes, implemented by a system according to the invention.

[0019] Figure 1 shows a schematic representation of a monitoring system 1 according to the invention for treating a patient P suffering from chronic hypercapnic respiratory failure by insufflation of pressurized gas, such as air or an air / O2 mixture, delivered by a medical ventilator 20, i.e., a respiratory support device. The pressurized gas is conveyed by a flexible conduit 22 from the medical ventilator 20 to a respiratory mask 21, which delivers the gas to the airways of patient P, such as a nasal mask.

[0020] System 1 of the invention makes it possible to calculate a ventilation quality score and to alert to a risk of poor ventilation perception by patient P during their treatment. This system 1 comprises a measuring device 2 configured to be arranged on the flexible conduit 22, i.e. on the path of the gas, in order to allow measurements to be taken, for example of pressure and / or flow rate, and then to determine values ​​of several ventilation variables (pb p2..., pû relating to patient P, namely compliance, mask leaks, apnea / hypopnea index (AHI), respiratory rate, spontaneous breathing rate, tidal volume, and possibly other variables or parameters.

[0021] Calculating such ventilation variables is known per se, notably from Manel Lujân, Cristina Lalmolda, and Begüm Ergan, 'Basic Concepts for Tidal Volume and Leakage Estimation in Non-Invasive Ventilation' Turkish Thoracic Journal 20, no. 2 (April 2019): 140-46, https: / / doi.org / 10.5152 / TurkThoracJ.2018.177, or Jean-Michel Amal, Mathilde Oranger, and Jésus Gonzalez-Bermejo, 'Monitoring Systems in Home Ventilation', Journal of Clinical Medicine 12, no. 6 (10 March 2023): 2163, https: / / doi.org / 10.3390 / jcml2062163.

[0022] More specifically, the measuring device 2 includes a housing 3 including suitable measuring means, such as one or more flow, pressure or other sensors, as well as an internal power source, such as a battery, supplying in particular the measuring means.

[0023] Data transmission means are also provided, configured to remotely transmit at least the ventilation variables provided by the measuring device 2. For example, these transmission means include a GSM or other type modem and a transmitting antenna. The transmitted variables are preferably routed via the internet or another network, such as GSM, LoRa, or Sigfox networks, and / or can be processed in the virtual computing space or “cloud”.

[0024] Data processing computer means are also provided, namely (at least) a computer server 4, comprising data reception means configured to receive ventilation variables teletransmitted by the data transmission means of the ventilator 1, as well as one or more processors enabling the processing of the values ​​of the ventilation variables measured by the measuring device 2 and deducing representative characteristics (ci,c2...,Cj) of the ventilation variables of patient P, which are then compared to threshold values ​​(sb s2,...sk) stored in storage means 7, such as a memory (e.g. hard drive, flash memory, cloud storage ... ) or other, in order to deduce a ventilation quality score for patient P between 0 and 100%.

[0025] The computer means for data processing or server 4 are electrically powered by means of supplying electrical current 5, such as the electrical network (110 / 220V) or one (or more) battery or storage battery(ies).

[0026] Furthermore, the system 1 of the invention also includes display means, also called display device 6, allowing information, data... to be displayed, for example a display screen of a fixed or portable computer, a multifunction phone (smartphone), a digital tablet or other.

[0027] The display device 6 receives the data to be displayed via a suitable communication system or means 8, for example, data transmission means configured to transmit data or other information, via a communication protocol, such as wifi, GSM (3G / 4G / 5G), Bluetooth®, the internet, an intranet or other.

[0028] [Fig.2] schematically illustrates a method of implementing the decision-making chain in within server 4 allowing to predict the quality of ventilation score of patient P and to identify its causes, that is to say the decision chain implemented by the system according to the invention, such as that of [Fig.1]

[0029] Once the measuring device 2 of the system of the invention 1 has operated flow and / or pressure measurements of the respiratory gas supplied to the patient, such as air, for a period of several, namely advantageously of at least 4 days, preferably between 4 and 15 days, the computer means of data processing 4, such as a remote server, process these flow and / or pressure measurements obtained over at least 4 days to determine or estimate quantities or values ​​of several ventilation variables pb p2..., p; of the patient P.

[0030] The ventilation variables pb p2..., p; thus determined are (at least) compliance, mask leaks, apnea / hypopnea index, respiratory rate, spontaneous breathing rate and tidal volume.

[0031] These magnitudes or values ​​of several ventilation variables pb p2..., p; are then used by the server 4, to deduce indicators cbc2.. .,cn linked to a ventilation quality score and compare them to threshold values ​​sb s2,.. .,sn stored in order to evaluate a specific profile for patient P.

[0032] This specific profile actually corresponds to one of several memorized Fl, F2, ..Fn profiles, which are each linked to a given SP predictive score of perceived ventilation quality, i.e. SP1, SP2...SPn.

[0033] These different predetermined profiles Fl, F2, ..., Fn, predictive scores SP1, SP2, ..., SPn, and the threshold values ​​sb, s2, ..., sk are stored by storage means, preferably by the server itself. They were obtained from the history of several patients whose adherence to treatment is known.

[0034] Each stored profile Fl, F2, ..., Fn is characterized by (at least) a predictive score SP of ventilation quality.

[0035] In other words, by proceeding in this way, thanks to the different predetermined Fl, F2, ..., Fn profiles, i.e. known and recorded, it is possible to easily determine which exact profile corresponds to the specific profile of the patient P considered and therefore what is the predictive score SP of perceived ventilation quality of this patient which is typically between 0 and 100%.

[0036] Next, as illustrated in [Fig.3], the display device 6 of the system 1 of the invention is configured to display the ventilation quality score (in %) for the patient(s) under consideration, for example here for at least 9 patients each identified by a unique identification (PatientlD).

[0037] In addition, the display device 6 of the system 1 of the invention is also configured to display a graphical representation, such as one or more curves as a function of time, of one (or more) ventilation variable (pb p2..., p;) of a patient selected from among all the patients displayed.

[0038] To do this, a user, typically a healthcare professional, selects the specific patient for whom they wish to have more information, in particular to be able to view the evolution of this (or these) ventilation variable(s) (pb p2..., p;). When this selection is made, the display device 6 displays the desired graphical representation, for example here the curves of several ventilation variables of the patient serving as a measurement history over several days, typically between 4 and 30 days.

[0039] The graphic representation is preferably associated with other patient information, including his / her identification (PatientlD), his / her ventilation quality score (%), and advantageously one or more causes of poor ventilation quality determined for the patient in question.

[0040] The causes of poor ventilation quality are primarily mask leaks or inadequate pressure settings, but also other ventilation variables. The causes of poor ventilation quality are determined by computer data processing means 4 from the ventilation score obtained for the patient in question.

[0041] In fact, each memorized profile is characterized by (at least) a predictive SP score of perceived ventilation quality and a cause of poor ventilation quality.

[0042] Advantageously, to facilitate reading and to enable the user, i.e. the nursing staff, to quickly distinguish the patients with the least good ventilation qualities, the display device 6 of the system 1 of the invention is also configured to display the patients in a list of several patients ranked or organized according to the ventilation quality score, i.e. by priority, i.e. from the highest (at the top of the list) to the lowest (at the bottom of the list), as illustrated in [Fig.3].

[0043] The following description provides a better understanding of the invention.

[0044] As illustrated in [Fig. 1], server 4 retrieves and pre-processes (in 4.1) first the ventilation variables (pb p2..., pû relating to patient P, such as compliance, mask leaks, apnea / hypopnea index, respiratory rate, spontaneous breathing rate and tidal volume,

[0045] For example, the pretreatment (4.1) may include steps of:

[0046] a) Scaling the values ​​of the ventilation variables (pl, p2..., pi) according to the fan used and its intrinsic characteristics, for example a median or average mask leakage, quantities expressed as a percentage or as absolute values.... Thus, for a ventilation variable p_i and a given machine m, we obtain: - moyem^p_i, m) / std(p_i, m)

[0047] where:

[0048] - average(p_i, m) is the empirical mean of the ventilation variable pi for the machine m considered (i.e. according to the manufacturer).

[0049] - std(p_i, m) is the standard deviation of the ventilation variable p_i for machine m considered.

[0050] - q_i is the value of the scaled ventilation variable.

[0051] b) Replacement of missing values ​​in the ventilation variables (pb p2 ..., Pi). For a ventilation variable p_i whose value is missing at a point t, several methods can be applied:

[0052] - Fixed-value imputation: p_i(t) = 0

[0053] - Imputation by mean: p_i(t) = mean(pi)

[0054] - Imputation by the previous value: p_i(t) = p_i(tl).

[0055] Next, these ventilation variables (pb p2..., p;) are processed and the representative characteristics (cb c2...,Cj), also called statistical indicators, are deduced, which are finally compared (in 4.2) to the threshold values ​​(sb s2,...sk) stored (in 7) to determine (in 4.3) the ventilation quality score.

[0056] For example, the treatment (4.2) includes steps for calculating statistical indicators on the ventilation variables p;(adherence, mask leaks, apnea / hypopnea index (AHI), respiratory rate, spontaneous breathing rate (%) and tidal volume), for example determination of mean, trend ... as given in [Table 1] below.

[0057] [Table 1] Representative characteristics (Cb C2...,Cj) Description Patient information Gender Male, Female Age Patient age Duration Treatment duration Variables for sale pi Leaks Mean Standard deviation Eccentricity (asymmetry) Flattening Tendency Eccentricity (asymmetry) Compliance Mean Standard deviation Eccentricity (asymmetry) Flattening Tendency AHI Mean Standard deviation Eccentricity (asymmetry) Flattening Tendency Respiratory rate Mean Standard deviation Eccentricity (asymmetry) Flattening Trend Spontaneous respiration rate Mean Standard deviation Eccentricity (asymmetry) Flattening Trend Tidal volume Mean Standard deviation Eccentricity (asymmetry) Flattening Trend

[0058]

[0059]

[0060]

[0061]

[0062]

[0063]

[0064]

[0065]

[0066]

[0067]

[0068] These statistical indicators are calculated, for example, using the following formulas, where D is the total number of days t, and X(t) is the breakdown variable recorded on a given day t. Thus: The mean p is calculated from the equation: p = (1 / D). The standard deviation θ is calculated from the equation: θ = (1 / D). 2 The variance V corresponds to: V = o2 The eccentricity S (the asymmetry) corresponds to: S = p3 / o3 The flattening corresponds to: S = p4 / o4 The tendency â corresponds to: â, b = argmin + bf The ventilation quality score obtained is used to trigger a poor ventilation quality alert (in 4.4) for the patient in question. The ventilation quality alert (4.4) can be transmitted to the display device 6, such as a computer screen or a multifunction phone (smartphone), which can then display it. The SP score can be transmitted via a communication protocol and / or network 8, such as web, GSM, or other. The triggering of a ventilation quality alert (in 4.4) is preferentially done via a Machine Learning process. This process uses a data history that includes at least the ventilation variables previously collected on patients whose outcome is known, that is to say that is, for which we know the S3-level score, after the target period of 4 to 30 days, typically 15 days.

[0069] A usable mathematical method, as illustrated in [Fig. 2], is based on prioritizing the representative characteristics (cb, c2..., Cj) extracted from the ventilation variables, such that the first ones are those with the greatest discriminatory power. More precisely, a characteristic c is chosen as the priority if it allows the largest number of patients to be separated into two groups using a threshold s, namely a group of patients with predominantly good ventilation quality and a group of patients with predominantly poor ventilation quality.

[0070] The determination of the priority characteristic c and the optimal choice of the threshold 5 are carried out with an optimization algorithm, such as that described by: Leo Breiman, Classification and regression trees, Monterey, CA, Wadsworth & Brooks / Cole Advanced Books & Software, 1984, 368 p. (ISBN 978-0-412-04841-8)

[0071] This method is applied sequentially to each group thus obtained to determine the priority characteristic and its optimal threshold for these groups, and then obtain 2 new subgroups. This is repeated several times in succession.

[0072] The process stops with a stopping criterion based on the number of iterations or when the patient subgroups correspond to sufficiently homogeneous patient profiles, i.e., patients who have predominantly good ventilation quality or predominantly poor ventilation quality. These sufficiently homogeneous profiles include all Fn profiles, i.e., Fl, F2, ..., Fn.

[0073] The predictive score SP_n associated with each Fn profile is calculated as the proportion of patients with poor perceived ventilation quality according to the formula:

[0074] SP_n(Profile Fn) = Nb-P poor_quality_ventilation (Profile Fn) / Nb-P (Profile Fn)

[0075] where: Nb-P is the number of patients

[0076] Finally, since the patients of each Fn profile in the database are previously well known by the healthcare staff, the main causes of their poor ventilation quality are generally known because they are common to all patients of the same profile (Fn).

[0077] Furthermore, to determine the most important factors for each prediction, i.e., those that led to the calculation of the ventilation quality score, one or more machine learning algorithms can be used, for example those described by:

[0078] - MT Ribeiro, S. Singh, C. Guestrin, Anchors: high-precision model-agnostic explanations, AAAI conference on artificial intelligence (AAAI); 2018, or

[0079] - MT Ribeiro, S. Singh, C. Guestrin, why should 1 trust be there?” explaining the predictions of any classifier, Proceedings of the 22ndACM SIGKDD international conference on knowledge discovery and data mining; 2016, pp. 1135-1144.

[0080] The non-adherence alert may be visual and / or audible. To this end, the system of the invention may include visual display means and / or audible means.

[0081] More generally, the system of the invention makes it possible to calculate a ventilation quality score and to alert to a risk of poor perception of ventilation of a patient P suffering from chronic hypercapnic respiratory failure, said patient P being treated by administration of pressurized gas, such as air or an air / O2 mixture, delivered by a respiratory assistance device of the medical ventilator type.

Claims

1. Demands Monitoring system (1) for a person (P) receiving non-invasive artificial ventilation (NIV) using a medical ventilator (20) powering a breathing mask (21) supplying a breathing gas to said person (P), comprising: - the medical ventilator (20) configured to provide ventilation variables including at least one compliance, mask leaks, apnea / hypopnea index, respiratory rate, spontaneous breathing rate and tidal volume, and - at least one computer server (4) configured to receive and process the ventilation variables provided by the medical ventilator, characterized in that said at least one computer server (4) is configured to: a. Treat the ventilation variables as follows: i) determining, for each ventilation variable, several statistical indicators chosen from a mean, a standard deviation, an eccentricity, a kurtosis, a trend, a variance, a median and a range, ii) calculating a ventilation quality score from said statistical indicators using a mathematical model allowing a comparison of said statistical indicators to memorized thresholds obtained by machine learning, and iii) determining, among said ventilation variables, at least one significant ventilation variable impacting the ventilation quality score to a greater extent than the rest of the ventilation variables, a. order a display on a display device, of the ventilation quality score determined in a) ii) and of said at least one significant ventilation variable, and b. generate a ventilation quality alert when the ventilation quality score is below a predefined threshold value stored within the computer server.

2. System according to claim 1, characterized in that the statistical indicators include a mean, a standard deviation, an eccentricity, kurtosis and a trend.

3. System according to claim 1, characterized in that the ventilation variables include compliance, mask leaks, apnea / hypopnea index and respiratory rate.

4. System according to claim 1, characterized in that it further comprises alerting means configured to trigger an alert when the ventilation quality score is greater than a predetermined threshold value, and said at least one computer server cooperates with the display device to display the alert having been triggered by the alerting means.

5. System according to claim 1, characterized in that the mathematical model comprises a learning algorithm.

6. System according to claim 1, characterized in that the display device is configured to simultaneously display the ventilation quality score and at least one significant ventilation variable impacting said ventilation quality score in a greater way than other ventilation variables.

7. System according to claim 6, characterized in that the display device is configured to display a list of several patients ranked from lowest to highest ventilation score.

8. System according to claim 7, characterized in that the display device is configured to display patients grouped according to their ventilation quality scores, preferably the patient groups are displayed in different colors.

9. System according to claim 6, characterized in that the display device is configured to display a graphical representation of at least one ventilation variable of a considered patient in the form of a curve or barograph over several days.

10. System according to claim 1, characterized in that the computer means for data processing are configured to process measurements of ventilation variables obtained over a time period of 4 to 30 days, and / or it includes alert means configured to trigger an audible or visual alert at a healthcare professional operating remote patient monitoring (RPM).

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