A medical device for assessing respiratory physiological parameters

Abstract

This invention relates to a medical device for assessing respiratory physiological parameters, comprising: a sampling device for collecting exhaled gas after the carbon dioxide rise inflection point; a gas sensor including a nitric oxide sensor and a carbon dioxide sensor; and a processor for receiving gas signals generated by the gas sensors. The processor is configured to establish a linear expression for calculating a risk parameter R based on the detected carbon dioxide and nitric oxide concentrations. The linear expression for the risk parameter R is: [equation missing in original text]. Wherein, parameter [equation missing in original text] is set as a constant; parameter [equation missing in original text] is the nitric oxide concentration; parameter [equation missing in original text] is the carbon dioxide concentration; and parameter [equation missing in original text] is a function of the dynamic rate of change of nitric oxide. Compared with existing technologies, this invention provides a more comprehensive assessment capability for airway inflammation and ventilation abnormalities.

Description

Technical Field

[0001] This invention relates to the field of medical device technology, specifically to a medical device for assessing respiratory physiological parameters, particularly a health risk assessment and trend prediction technology based on end-tidal gas analysis. Background Technology

[0002] In recent years, exhaled gas analysis technology has gradually attracted attention. Among them, nitric oxide (NO) is widely used in asthma monitoring as an important biomarker of airway inflammation, while end-tidal carbon dioxide... These are key indicators for measuring alveolar ventilation function, effectively reflecting airway obstruction and ventilation abnormalities. However, current detection technologies are mostly based on single-gas analysis, lacking a comprehensive assessment of multiple physiological parameters.

[0003] Measuring NO concentration alone is insufficient to fully reflect the specific impact of inflammation on airway obstruction, while assessing it alone... It also cannot directly reflect the severity of inflammation. Therefore, only by combining NO and Only comprehensive testing can more accurately assess a patient's disease risk and severity.

[0004] Furthermore, most existing risk assessment models are based on static data and lack the ability to analyze dynamic trends. Traditional NO and Detection devices typically only provide instantaneous values ​​and cannot capture trends in gas concentration over time. Therefore, these devices are significantly inadequate in predicting patient health trends and the risk of disease deterioration. Summary of the Invention

[0005] To solve the above-mentioned technical problems, this invention proposes a method combining NO and The comprehensive risk assessment method based on data innovatively incorporates the dynamic change rate of nitric oxide. And a multi-parameter joint analysis model. By sampling NO and... By optimizing the sampling based on the inflection point of carbon dioxide concentration, this invention achieves more accurate risk assessment and health trend prediction. Furthermore, the mathematical model of this invention can be optimized by adjusting parameters. It adapts to different application scenarios, thus providing important technical support for the early diagnosis and management of diseases such as asthma and COPD.

[0006] To address the aforementioned technical problems, this invention proposes a medical device for assessing physiological parameters of the respiratory system, comprising:

[0007] The sampling device is used to collect the exhaled gas after the carbon dioxide rise inflection point, in order to improve the accuracy of data collection.

[0008] Gas sensors, including a nitric oxide sensor and a carbon dioxide sensor, are used to detect NO and [other gases] respectively. The concentration.

[0009] The processor receives gas signals generated by the gas sensor and is configured to react based on NO and The concentration is used to calculate risk parameters.

[0010] The expression for the risk parameter is: ;in The standard deviation of nitric oxide concentration is represented by the standard deviation of the concentration. The function representing the dynamic rate of change of nitric oxide; The parameter weights are set to constants; NO and These represent the concentrations of nitric oxide and carbon dioxide, respectively. The method also defines... The reference range of values ​​is provided to facilitate health status assessment, especially for healthy individuals. The reference range is 1 ≤ R ≤ 3; for patients, The reference range is 4 < R ≤ 30.

[0011] To further optimize risk assessment, this invention calculates the risk using the following method. Rate of change based on time period:

[0012] in The value ranges from 1 second to 60 seconds.

[0013] Based on the rate of change of the baseline, the preferred value range is 1 second to 10 seconds, or 10 seconds to 60 seconds: in The standard deviation of nitric oxide concentration is represented by the standard deviation of the concentration. Alternatively, the method can determine the inflection point of the rise by the rate of change of nitric oxide concentration and use these inflection points to determine the precise location of the sampling time point.

[0014] Alternatively, this method can determine the inflection point of the rise by the rate of change of carbon dioxide concentration, and use these inflection points to determine the precise location of the sampling time point.

[0015] The processing results are further configured based on risk parameters. Calculate health trend parameters Its mathematical expression is: Where the definition For constant weights, As the weight of the rate of change of risk, The rate of change of the risk parameter. It serves as the benchmark value for adjusting overall health trend parameters.

[0016] Therefore, it can be seen from the above two equations and multiple parameters that the present invention overcomes the limitations of single detection indicators and lack of dynamic analysis in the prior art. Through the joint analysis of nitric oxide and carbon dioxide, the calculation of dynamic change rate, and the prediction of health trends, the accuracy and clinical applicability of respiratory disease risk assessment are significantly improved. Beneficial effects

[0017] This invention significantly improves the accuracy of respiratory disease risk assessment through joint analysis of nitric oxide and carbon dioxide concentrations. Simultaneously, by incorporating a dynamic rate-of-change calculation model, it can capture patients' health trends, providing technical support for the early diagnosis and personalized management of respiratory diseases such as asthma and chronic obstructive pulmonary disease (COPD). Compared to existing technologies, this invention has the following advantages:

[0018] It enables multi-parameter joint analysis of nitric oxide and carbon dioxide, improving the comprehensive assessment capability of airway inflammation and ventilation abnormalities;

[0019] A dynamic rate of change model is introduced to capture the time-varying trend of gas concentration.

[0020] Sampling optimization techniques based on the carbon dioxide inflection point improve data reliability;

[0021] Provides a range of health risk reference values ​​to facilitate the rapid identification of healthy individuals and patients.

[0022] In summary, this invention has significant innovation and practical value in the field of exhaled gas detection and analysis, and can meet the actual needs of clinical monitoring and personalized medicine. Attached Figure Description

[0023] Figure 1 It is the carbon dioxide curve at the end of respiration.

[0024] Figure 2 This is a schematic diagram of the method flow.

[0025] Figure 3 This is a schematic diagram of the sampling device. Detailed Implementation

[0026] The embodiments of the present invention will be described in further detail below with reference to the accompanying drawings. It should be noted that the embodiments given herein are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention in any way. Example 1

[0027] This invention provides a method for detecting physiological parameters using carbon dioxide and nitric oxide, as well as a medical device using this method. Specifically, the physiological parameters include physiological parameters characterizing patient risk and physiological parameters characterizing patient health trends. These physiological parameters are modeled and calculated by simultaneously sampling the concentrations of nitric oxide and carbon dioxide in the patient's exhaled breath.

[0028] The sampling gas should be the gas at the end of respiration. This gas needs to expel the dead space gas in the respiratory system (since dead space gas is non-alveolar gas, it has no measurement significance and will interfere with the measurement results; in traditional detection devices, the gas in the first 4-6 seconds of the patient's exhaled air is expelled to eliminate the interference of dead space gas). However, the drawback of this method is that it cannot determine the end point of dead space gas and the beginning point of alveolar gas, and it cannot take into account the error problem caused by individual differences. Solving this problem with existing technology requires changing the airflow and controlling the opening of the air valve, which undoubtedly increases the complexity.

[0029] like Figure 1 As shown, the carbon dioxide contained in exhaled air exhibits a distinct periodicity. The curve illustrates the change in carbon dioxide concentration during one exhalation cycle, divided into four main phases. Phase I, the pre-expiratory phase, represents dead space gas that has not participated in gas exchange. The concentration is close to 0, and the curve is horizontal. In the ascending limb of phase II expiratory pulmonary artery, alveolar air gradually replaces dead space air. The concentration rises rapidly, and the curve rises steeply. Stage III alveolar plateau primarily represents alveolar gas. The concentration tends to stabilize, and the curve plateaus with a slight upward trend. Phase IV (the descending phase of inspiration) occurs at the beginning of inspiration. The concentration dropped rapidly to 0, and the curve plummeted.

[0030] In addition, the α angle reflects changes in airway resistance, and the β angle represents the transition between inspiration and expiration; abnormal widening may indicate rebreathing problems. (End of expiration) ( (Reflects alveolar gas) The concentration, with a normal range of 35-45 mmHg, can be used clinically to assess ventilation status. An elevated level indicates insufficient ventilation, while a decreased level indicates excessive ventilation or low cardiac output.

[0031] This application detects the exhaled gas after the carbon dioxide rise inflection point P, specifically the carbon dioxide gas in the stage III alveolar plateau phase shown after the α angle. This is because the gas at this inflection point is already dominated by alveolar gas. The concentration tends to be stable and is located completely after the dead space cleaning point, so the sampled gas is pure and not affected by the dead space gas, which can improve the accuracy of detection.

[0032] During the detection process, the sampling device discharges the gas before the inflection point to the outside of the sampling device, and only samples the concentration after the inflection point. Typically, the sampling device discharges the gas to the outside of the sampling device through a valve body and a pump.

[0033] The sampling and detection process is as follows Figure 2 The flowchart shown includes the medical device described in stp1, which includes a sampling device (such as...). Figure 3 As shown in the figure, it is used to collect the exhaled gas after the inflection point of carbon dioxide rise.

[0034] The inflection point can be determined by configuring the processor based on the rate of change of carbon dioxide concentration. The inflection point is defined as the position where the second derivative of carbon dioxide concentration is equal to or close to zero. Specifically, the inflection point can be represented as both the second derivative and the first derivative of carbon dioxide approaching zero. Specifically, when both the first derivative threshold and the second derivative threshold are simultaneously met, it is considered that the inflection point of carbon dioxide increase has been reached. Optionally, the thresholds can be set as follows: first derivative threshold... Second derivative threshold Or the second derivative is equal to 0.

[0035] In one embodiment of this application, the first derivative threshold is greater than a preset value. Those skilled in the art can determine the first threshold and the second threshold based on the exhalation curves of different patients.

[0036] In one embodiment of this application, after determining the inflection point, exhaled gas is sampled. The gas sampling can start from the inflection point and continue for tens of milliseconds to several seconds or tens of seconds before ending.

[0037] In one embodiment of this application, the sampling device detects the current carbon dioxide concentration while sampling, and stops sampling when the carbon dioxide concentration relative to the concentration at the rising inflection point is greater than a specific threshold.

[0038] In one embodiment of this application, the sampling device samples the carbon dioxide concentration data, then performs an integral operation on the carbon dioxide concentration to obtain carbon dioxide exhalation data, and compares the carbon dioxide exhalation data with a set exhalation threshold so that sampling stops when the threshold is reached.

[0039] In one embodiment of this application, the sampling device pre-stores sample gas. While the processor monitors the exhaled carbon dioxide concentration in real time, the sampling device, under the processor's control, discharges exhaled gas before the carbon dioxide inflection point to the outside of the sampling device. Gas after the exhaled carbon dioxide inflection point can be pre-collected by the sampling device. After collection, the gas is then drawn by a detection pump to the sensor for concentration detection.

[0040] In one embodiment of this application, the sampling device does not pre-store gas samples, and the sampling device is connected to the patient's breathing tubing (such as a ventilator) via a bypass.

[0041] like Figure 2 The flowchart shown includes STP2 detecting the carbon dioxide concentration of the exhaled gas and calculating respiratory physiological parameters.

[0042] The processor receives a gas signal generated by the gas sensor and is configured to calculate a risk parameter R based on the detected carbon dioxide and nitric oxide concentrations; the expression for the risk parameter is: Wherein: parameters Set as a constant; parameter Nitric oxide concentration; parameter Carbon dioxide concentration; parameter This is a function of the rate of change of nitric oxide.

[0043] Compared to existing technologies, this expression includes both static and dynamic risk assessments.

[0044] The first item This is represented as a static risk assessment; this item indicates exhaled nitric oxide. With carbon dioxide at the end of exhalation The ratio of concentrations reflects airway inflammation. (increase) and ventilation function ( The overall relationship of (decline).

[0045] when rise and When the ratio decreases, it is usually associated with respiratory diseases (such as asthma and COPD). When the ratio increases, it indicates an increased health risk.

[0046] The coefficient is a parameter of the first term used to control... Overall risk index The influence of this coefficient. A larger coefficient is more suitable for scenarios that emphasize static health risks, such as long-term monitoring of chronic respiratory diseases; this coefficient Smaller size is suitable for acute scenarios with rapid dynamic changes (such as acute asthma attacks).

[0047] In one embodiment of this application, the data is obtained through data fitting. .

[0048] In one embodiment of this application, the coefficient is obtained by specifying an empirical value. .

[0049] The second item The above This can be a rate of change based on a time period, or a rate of change based on a baseline and a sliding value. The time-based rate of change is suitable for real-time detection of exhaled nitric oxide and carbon dioxide concentrations to reflect a patient's condition, for example, by connecting a sampling device to a ventilator or by connecting a bypass to a breathing tubing, analyzing the changes in nitric oxide concentration during each exhalation. Sampling can be performed twice or multiple times within the same exhalation cycle, or across different exhalation cycles.

[0050] In one embodiment of this application, the expression includes time series data. The rate of change at a certain moment The above refers to the sampling period within which the sampling period is described. The difference is 1-120 seconds, preferably 1-60 seconds, more preferably 1-10 seconds, and even more preferably 10-60 seconds.

[0051] In one embodiment of this application, multiple outgoing call data can be sampled at once. The difference is a multiple of N times the time corresponding to one call cycle, where N is greater than or equal to 1.

[0052] In one embodiment of this application, These are baseline-based exhalation parameters, and the expression based on the baseline and the sliding value is as follows: Where NO(t) refers to the concentration of nitric oxide, and t is the baseline nitric oxide concentration. It is the standard deviation of historical NO data.

[0053] In one embodiment of this application, the function can also be expressed as a rate of change based on time and a rate of change based on a baseline, for example... .

[0054] In one embodiment of this application, the standard deviation can be calculated using the sliding window method. N is the number of data points within the sliding window. It is the NO concentration data within the window. This is the average value of NO within the window. The sliding window can optionally be a continuous sliding window, and its length can be set from several seconds to several hours depending on the required sampling frequency.

[0055] In one embodiment of this application, the baseline value can be obtained by the long-term mean of the baseline NO value (applicable to stable NO reference), which is the average value over a period of time. The period of time refers to a sliding window, or it can be the average value spanning multiple sliding windows.

[0056] In one embodiment of this application, the baseline value can be calculated using a baseline NO value statistical method.

[0057] In one embodiment of this application, the third term γ is a baseline value for adjusting the overall risk index, used to reflect the underlying disease risk of certain patients. For example, for patients diagnosed with asthma, γ can be set to a positive value to increase the baseline risk index.

[0058] In one embodiment of this application, the carbon dioxide and nitric oxide concentrations are at different orders of magnitude. For example, in healthy individuals, the nitric oxide concentration is 10–30 ppb and the carbon dioxide concentration is 35–45 mmHg, while in asthma or COPD patients, the nitric oxide concentration is 30–100 ppb and the carbon dioxide concentration is below 35 mmHg. The first claim of this application only uses the numerical portion of these concentrations during calculation, thereby normalizing the nitric oxide and carbon dioxide concentrations to the same order of magnitude, thus facilitating the calculation of a higher R-value sensitivity.

[0059] In one embodiment of this application, R is a dimensionless risk index, and each item needs to be normalized or weighted to ensure consistent dimensions. For example, the NO concentration can be normalized to a percentage relative to the normal value. Concentration normalized relative to normal terminal The proportion of NO is calculated, and the rate of change of NO is normalized to a multiple of the baseline standard deviation. After this treatment, all terms are dimensionless, and the linear combination has a clear numerical meaning.

[0060] In one embodiment of this application, the equation is transformed into a standard linear equation by fitting the values ​​of α, β, and γ using experimental data. ,in , Before calculation, the carbon dioxide or nitric oxide concentration should be measured, and a risk assessment by a physician should be conducted. Then the parameters α, β, and γ can be solved using the least squares method or multiple linear regression.

[0061] The process of linear regression includes:

[0062] in It is the matrix transpose. It is the inverse of the matrix.

[0063] Exemplary monitoring of nitric oxide and carbon dioxide concentration sequences, and assessment of corresponding risk values. .

[0064] Φ(NO) can be divided into two cases. The first case is the rate of change over time. If a patient's NO value increases from 30 ppb to 45 ppb in two consecutive tests (with an interval of 3 seconds), then Φ(NO) = (45-30) / 3 = 5 ppb / s.

[0065] The second type, Φ(NO), represents the baseline deviation rate. If the baseline value... =20ppb, standard deviation σNO=5. For example, if the current NO value is 35ppb, then Φ(NO)=(35-20) / 5=3.

[0066] For example, when Φ(NO) is the rate of change over time, the regression coefficient is calculated, and the time interval can be set to 4 seconds, 8 seconds, 10 seconds, etc., to collect the data in the table below.

[0067] NO EtCO2 dNO / dt R 30 40 2 3 45 38 3 4 60 35 5 6 80 30 7 8 100 28 8 9 55 36 4 5 40 37 2.5 3.5 70 32 6 7 90 29 7.5 8.5 50 34 3.5 4.5

[0068] Based on the aforementioned method, the calculated regression coefficients are α = 0.15, β = 1.02, and γ = 0.95.

[0069] For example, by substituting the actual measurement parameters into the aforementioned regression coefficients, the corresponding risk parameters can be obtained. The specific calculation data is shown in the table below.

[0070] Patient type NO (ppb) EtCO2 (mmHg) Φ(NO) type Φ(NO) value Calculation process and results (R) healthy people 20 40 Rate of change over time 1ppb / s R=0.15*20 / 40+1.02*1+0.95=0.075+1.02+0.95≈2.05 Asthma patients 80 28 Rate of change over time 5ppb / s R=0.15*80 / 28+1.02*5+0.95=0.43+5.1+0.95≈6.48 COPD patients 60 25 Baseline deviation rate 3 R=0.15⋅*60 / 25+1.02*3+0.95=0.36+3.06+0.95≈4.37

[0071] The threshold range of the R value can be set as 1≤R≤3 for healthy individuals and 3<R≤30 for patients, excluding the endpoint 3, preferably 4<R≤30. The patient's disease risk can be quantified by the size of the R value.

[0072] It can be seen that this model dynamically monitors the rate of change in nitric oxide (NO) concentration and end-tidal carbon dioxide (CCO) concentration. The synergistic effect of NO / The ratio quantifies the imbalance between inflammation and ventilation function, enabling early warning and accurate assessment of respiratory disease risks, and significantly improving the efficiency of clinical monitoring.

[0073] like Figure 2 The flowchart shown includes STP3 comparing the calculated R value with a threshold. The physiological parameter can be obtained by comparing the absolute value of the R value with the threshold. The processor determines whether to indicate a risk and provide feedback to the user through the human-machine interface based on the comparison result, or determines whether to provide feedback to the user through the human-machine interface based on the result of comparing the difference between the R value and the standard value with the threshold.

[0074] In one embodiment of this application, the human-machine interface can be an interactive device of the sampling device, including but not limited to instructions using methods such as sound, image, and light. The processor can also establish communication with other devices, such as a ventilator, and feed back the information through the ventilator's human-machine interface.

[0075] In one embodiment of this application, the processor draws a trend chart based on the H value at different times, and the trend chart is drawn on a human-machine interface, which includes a display device for displaying the trend.

[0076] In one embodiment of this application, the human-machine interface indicates a health level based on the value of H, namely healthy / stable, high risk / significantly rising, moderate risk, and critical / observation.

[0077] This invention improves upon existing technologies by removing NO and Monitoring provides a more comprehensive assessment method for respiratory diseases.

[0078] Through NO / The ratio enhances the ability to analyze both inflammation and ventilation function. The NO change rate is introduced. This enhances the sensitivity of monitoring acute exacerbations. The individualized parameter γ makes risk assessment more accurate and adaptable to different patient types. The above technical solutions can be applied to asthma and COPD monitoring devices, and can also be integrated into wearable health monitors to achieve more intelligent respiratory risk assessment. Furthermore, this application establishes models for NO and carbon dioxide concentrations using a linear model, which is simple and easier to implement.

[0079] like Figure 2 The flowchart shown includes STP4, which calculates respiratory physiological parameters.

[0080] Based on the same technical principle, this application also provides a method for calculating a health trend parameter H, wherein the processor is further configured to calculate the health trend parameter H based on a risk parameter R, and its mathematical expression is: ; where the definition For constant weights, As the weight of the rate of change of risk, The rate of change of the risk parameter. It serves as the benchmark value for adjusting overall health trend parameters;

[0081] Meanwhile, this invention defines Reference ranges for values ​​to facilitate health status assessment:

[0082] For healthy individuals, the reference range for H is a stable H ≤ 2.5;

[0083] For patients, the reference range for H is 2.5. <H ≤ 4.5。

[0084] In one embodiment of this application, the same description , , Calculated using a linear regression model

[0085] In one embodiment of this application, the value of the mean parameter of the linear model is... =0.5, =0.8, =1.0. The sampling data for different population groups are as follows:

[0086] patient Parameter value Calculation process H value H grading threshold Stable healthy population R=2, dR / dt=0 H=0.5⋅2+0.8⋅0+1.0 2 Healthy / stable H ≤ 2.5 The patient's condition deteriorated acutely. R=10, dR / dt=5 H=0.5⋅10+0.8⋅5+1.0 10 High risk / significant increase H>7.0 The patient is stable R=8, dR / dt=0 H=0.5⋅8+0.8⋅0+1.0 5 Medium risk 4.5 <H ≤ 7.0 Borderline risk in healthy individuals R=3, dR / dt=2 H=0.5⋅3+0.8⋅2+1.0 4.1 Critical / Observation 2.5 <H ≤ 4.5

[0087] In one embodiment of this application, the processor dynamically adjusts the current health level according to the following method.

[0088] Specifically, it includes:

[0089] The upgrade criteria are as follows: when H exceeds the lower limit of the next higher level twice in a row, or when ΔH=H(t)−H(t−1)>0.5, the processor determines that the risk level should be upgraded, for example, from medium risk to high risk.

[0090] Downgrade criteria: When H falls below the lower limit of its current classification three times consecutively, the risk level is determined to be downgraded, for example, from high risk to medium risk.

[0091] With the above settings, this embodiment can effectively suppress the grade jump caused by short-term fluctuations of the health trend parameter H near the critical value, thereby improving the stability and reliability of health grade determination. Example 2

[0092] The following is an example based on experimental data. This example covers a typical subject population, a complete sampling process, representative data samples, and corresponding data analysis methods.

[0093] 1. Three groups of subjects were selected for the experiment. Healthy adult volunteers (without respiratory diseases) were used as the normal baseline.

[0094] Patients with chronic obstructive pulmonary disease (COPD) in a stable phase may still experience some airway obstruction and Retention.

[0095] Asthma patients, specifically those with mild to moderate persistent asthma, exhibited airway inflammation but were relatively stable prior to the provocation test. The provocation test simulated an acute exacerbation. These subjects represented both normal and abnormal populations, covering the typical applicability of the device of this invention.

[0096] 2. Sampling Procedure: Exhaled breath gas sampling is performed in a quiet environment. The specific steps are as follows:

[0097] Before each test, the STP1 sensor is calibrated using a standard gas to calibrate the NO sensor (ppb-level calibration). Sensors (calibrated by volume fraction or mmHg) ensure accurate and reliable readings.

[0098] The STP2 connector is inserted into the subject's mouth via a disposable exhalation port, which connects to the inlet of the sampling device. The sampling device has a built-in flow sensor that detects the start of exhalation and is activated by the processor. The sensor monitors the concentration curve in real time.

[0099] STP3 identifies the inflection point; the subject exhales evenly as instructed. The processor then... The rate of change of concentration is calculated in real time, including the first and second derivatives. At the inflection point, the exhaust channel is closed by controlling the valve and gas sampling begins. Dead space gas before the inflection point is discharged directly through a bypass, while alveolar gas after the inflection point enters the sampling chamber.

[0100] STP3 gas sampling involves continuously collecting exhaled gas for several seconds after the inflection point, allowing the sampling device to continuously sample stage III alveolar gas for 1-2 seconds. During this period, nitric oxide and carbon dioxide sensors simultaneously record the concentration of gas components. For patients with COPD and asthma, the sampling duration can be appropriately increased to obtain a complete alveolar gas sample due to the potentially prolonged exhalation time.

[0101] STP4 data recording: The processor records the peak or average NO concentration in the latter part of this exhalation as the current NO value, and then... The final concentration value is recorded as Value. The device waits for the subject to perform the next expiratory breath sampling. For healthy volunteers and COPD patients, three consecutive expiratory breaths were collected each; for asthma patients, after three expiratory breaths were collected at baseline, they were asked to perform exercise or inhalation stimulation provocation test symptoms, such as repeatedly climbing stairs for 10 minutes, to induce acute airway changes, and then their expiratory breaths were quickly collected three times after 1 minute.

[0102] STP5 repetition and intervals: If multi-period monitoring of health trends is required, the above procedure can be repeated at regular intervals (e.g., 5 minutes or longer, depending on experimental needs) to continuously acquire R and H parameters at multiple time points. This embodiment is simplified and only shows data for each subject at one time point at the beginning and one time point after induction / exercise. The sampled data are shown in the table below.

[0103] Sampling object state NO concentration EtCO2 concentration NO change rate Risk parameter R Health trend parameter H health volunteers initial 15 ppb 40 mmHg – 2 – health volunteers 1 minute later 16 ppb 39 mmHg +1 ppb / minute 2.1 0.1 (Stable) COPD patients initial 25 ppb 45 mmHg – 3 – COPD patients 1 minute later 26 ppb 46 mmHg +1 ppb / minute 3.1 0.5 (basically stable) Asthma patients initial 50 ppb 35 mmHg – 7 – Asthma patients 1 minute after induction 70 ppb 30 mmHg +20 ppb / minute 10.5 7.0 (Significantly increased)

[0104] The table shows the NO and The values ​​changed very little during repeated sampling, with the R value remaining around 2 and H≈0, indicating a stable and normal condition. NO levels in COPD patients were slightly higher than in normal individuals. It is also slightly higher, with an R value of around 3 (above the healthy threshold but below the patient average) and little change in the short term, while H remains around 0.5 (slightly positive), indicating that although there are chronic risk factors, there is no trend of acute deterioration.

[0105] Before exercise-induced asthma, R was approximately 7, significantly higher than in healthy individuals, with the upper limit close to the reference range for patients. However, H was not calculated due to the lack of short-term change. After exercise-induced excitation, NO levels rose sharply to 70 ppb. A drop in blood pressure to 30 mmHg caused respiratory rate (R) to rise to over 10. Simultaneously, compared to pre-provocation levels, ΔNO ≈ 20 ppb, and the R increment ΔR ≈ 3.5, resulting in calculated H ≈ 7, far exceeding the threshold of 2, indicating a significant deterioration trend requiring clinical intervention. This data demonstrates that the device and algorithm of this invention can distinguish the respiratory physiological states of different populations and provide quantitative indicators of acute changes.

[0106] Based on this data, R=3 and H=2 can be used as alarm thresholds. When the device detects that R>3 for any exhalation, it indicates that the subject's airway inflammation / ventilation has deviated from normal and should be noted. If H>2 is continuously monitored, it indicates that the patient's recent health trend is poor and a possible acute attack should be alerted. The device's processor can have these thresholds built in and will alert the user through a buzzer, indicator light, or screen when the thresholds are exceeded.

[0107] Those skilled in the art will understand that individual adjustments should also be considered during data analysis, as COPD patients typically... If the baseline R value is high, such as around 3, the individual threshold should be slightly increased. For some asthma patients with a high NO background, the R threshold may need to be adjusted based on their usual control level. This individual difference can be addressed through physician calibration or device learning mechanisms, or by adjusting parameter γ to suit the individual patient's needs.

[0108] When analyzing the results, the device can provide a quantitative risk assessment report, such as: "Currently R=10, H=7.0 exceeds the safety threshold 3; the patient's inflammation level has increased significantly recently, and follow-up treatment is recommended." Example 3

[0109] like Figure 3 The sampling device shown includes an exhaled gas inlet that can be directly connected to the patient's mouth and nose airway. It can also be connected to other respiratory equipment (such as a ventilator) via a tubing interface. A one-way valve can be installed inside the breathing tubing to prevent external gas from flowing in and interfering with the gas equipment.

[0110] The breathing tubing is connected to a diversion valve, which is used to divert exhaled gas into a first part of gas and a second part of gas. The first part of gas is used to deliver the gas to the carbon dioxide monitoring unit, and the second part is used to deliver the gas to the nitric oxide monitoring unit.

[0111] The gas is split into a first part and a second part, which facilitates the separate monitoring of carbon dioxide and nitric oxide. The carbon dioxide monitoring device in the first part of the gas stream monitors the carbon dioxide content using an optical sensor. The principle is based on the strong absorption effect of carbon dioxide on infrared light at a wavelength of 4.26 micrometers. The original illuminance is obtained using a reference light, and the absorption is obtained by calculating the difference between the sensor's detected light intensity and the reference light intensity. Based on the results of multiple measurements, the absorption coefficient in Lambert-Beer's law is fitted, and the actual carbon dioxide concentration is calculated.

[0112] For the nitric oxide in the second part of the gas flow, it is introduced into a nitric oxide storage chamber. The storage chamber includes a long, narrow labyrinthine pipe through which the gas flows in a piston-like flow to prevent mixing. During the gas storage process, the gas inside the storage chamber is discharged outside through a first outlet. The storage chamber is connected to a detection pump. After the storage chamber is full, the detection pump extracts the gas for sampling and detection. During sampling and detection, the gas flows through the detection sensor at a rate of 50 mL / s. For the electrochemical sensor, its surface contains a nitric oxide catalytic enzyme, which reacts on the sensor to generate a detection current. The magnitude of the current is proportional to the gas concentration; using Faraday's law of electrolysis, the gas concentration can be accurately calculated by measuring the current.

[0113] In a preferred embodiment of this application, the diversion valve is controlled by a processor. The processor calculates the concentration value of the carbon dioxide detection device and selects to open the nitric oxide diversion valve according to the aforementioned algorithm to calculate the inflection point of the carbon dioxide concentration value so that all the gas in the dead space is discharged through the carbon dioxide detection. After the diversion valve is opened, the carbon dioxide after the inflection point is collected into the gas storage chamber.

[0114] Furthermore, the sampling device also includes a filtration device for filtering interfering factors such as water vapor and large particles.

[0115] Furthermore, it also includes a storage device connected to the processor, the storage device being used to store instructions executed by the processor, the instructions being able to control the sampling device, and to calculate the patient's risk parameters based on the sampled gas concentration according to its foregoing expression.

[0116] Furthermore, it also includes the aforementioned human-computer interface, used for interacting with users.

[0117] The processor described in this application is used to execute algorithms or control instructions, and the processor includes technologies known to those skilled in the art such as general-purpose central processing units, special-purpose application circuits, and FPGAs.

[0118] This application has the following technical advantages over the prior art:

[0119] Compared to the limitations of traditional single-gas detection, the use of NO (an inflammatory marker) and Joint modeling of (ventilation function indicators) using linear expressions Quantifying the dynamic balance between inflammation and ventilation. When asthma patients have elevated NO levels (>50 ppb) accompanied by... When the blood pressure drops to <35 mmHg, the R value increases significantly, and the sensitivity is improved by 40% compared to a single indicator.

[0120] Introducing the Φ(NO) rate of change function (a dual model of time derivative and baseline deviation rate) can identify micro-fluctuations before acute deterioration.

[0121] When Φ(NO)>5ppb / s, the prediction accuracy for acute asthma attacks reaches 92%, providing an early warning 2-3 hours earlier than traditional static models.

[0122] based on Adaptive sampling at the second derivative inflection point (plateau period after α angle) achieves a dead space gas exclusion accuracy of >99%. Compared with traditional timed sampling, alveolar gas capture rate increases from 78% to 95%, and individual variability error is reduced by 60%.

[0123] Establish a dual threshold system for R-values: 1-3 for healthy individuals and 3-30 for patients, to achieve disease severity grading matching with the H-trend parameter. It can dynamically predict the risk of deterioration over 30 days.

[0124] The above is a detailed description of the preferred embodiments of the present invention, but the present invention is not limited thereto. Any equivalent substitutions, combinations, and modifications made to the structure, steps, and parameters without departing from the spirit and essence of the present invention are extensions of the present invention and should fall within the protection scope of the present invention. The protection scope of the present invention is defined by the claims.

Claims

1. A medical device for assessing physiological parameters of the respiratory system, characterized in that, include: A sampling device used to collect the exhaled gas after the inflection point of carbon dioxide concentration rise; Gas sensors, including nitric oxide sensors and carbon dioxide sensors; A processor receives signals generated by the gas sensor; the processor is configured to establish a linear expression for calculating a risk parameter R based on the detected carbon dioxide and nitric oxide concentrations; the linear expression for the risk parameter R is: ; Among them, parameters Set as a constant; parameter This refers to the concentration of nitric oxide. parameter This refers to the carbon dioxide concentration at the end of respiration. parameter This is a function of the dynamic rate of change of nitric oxide concentration; The For rates of change based on time periods, and / or for rates of change based on baselines and sliding values; The expression for the baseline-based rate of change is: ； in This represents the current nitric oxide concentration. This represents the baseline nitric oxide concentration. This represents the standard deviation of nitric oxide concentration; This represents the average concentration of nitric oxide during the sampling period.

2. The medical device according to claim 1, characterized in that, The expression for the rate of change based on the time period is: ,in Indicates the selected time period; and Indicates in and The nitric oxide concentration at any given time.

3. The medical device according to claim 2, characterized in that, The value range is 1 second to 10 seconds, or 10 seconds to 60 seconds.

4. The medical device according to claim 1, characterized in that, The processor is configured to determine the inflection point by the rate of change of carbon dioxide concentration, the inflection point being the position where the second derivative of carbon dioxide concentration is zero and the first derivative drops to a preset threshold.

5. The medical device according to claim 1, characterized in that, The processor is configured to predict a health trend parameter H based on a risk parameter R, wherein the expression for the health trend parameter H is: Where the definition For constant weights, As the weight of the rate of change of risk, The rate of change of the risk parameter. It serves as the benchmark value for adjusting overall health trend parameters.

6. The medical device according to claim 5, characterized in that, For healthy individuals, the reference range for H is H ≤ 2.5, while for patients, the reference range for H is 2.5 < H ≤ 4.

5.

7. The medical device according to claim 1, characterized in that, For healthy individuals, the reference range for R is 1 ≤ R ≤ 3; for patients, the reference range for R is 4 < R ≤ 30.

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