Method, device, computer readable storage medium and electronic device for predicting efficacy of povic drug
By using regression analysis of pulmonary function indicators FEF50% and FeNO, suitable treatment regimens for compound methoxyphenamine combined with ICS/LABA or compound methoxyphenamine alone were screened, solving the problem of individualized treatment for cough after novel coronavirus infection, improving treatment efficacy and reducing adverse reactions.
Patent Information
- Authority / Receiving Office
- CN · China
- Patent Type
- Patents(China)
- Current Assignee / Owner
- SHANGHAI FIRST PEOPLES HOSPITAL
- Filing Date
- 2025-07-14
- Publication Date
- 2026-07-10
AI Technical Summary
How to select appropriate medications for different patient groups to achieve individualized and precise treatment of post-COVID-19 cough (POVIC) and reduce ineffective treatment and adverse drug reactions.
By obtaining the patient's pulmonary function indicators FEF50% and FeNO, regression analysis was performed to obtain the P-value. The size of the P-value was used to determine whether to use compound methoxyphenamine in combination with ICS/LABA or to use compound methoxyphenamine alone for treatment.
This approach enables individualized and precise PIVIC treatment, improves the effectiveness of drug therapy, reduces adverse reactions, simplifies the examination process, and reduces the number of examinations and costs for patients.
Smart Images
Figure CN120878032B_ABST
Abstract
Description
Technical Field
[0001] This invention relates to the field of clinical medicine, particularly to the treatment of cough following novel coronavirus infection. Specifically, this invention provides a method, apparatus, computer-readable storage medium, and electronic device thereof for screening patients who respond well to treatment of cough following novel coronavirus infection using a combination of methoxyphenamine and inhaled corticosteroids / long-acting β2-agonists (ICS / LABA). Background Technology
[0002] Since its outbreak in 2019, the novel coronavirus (SARS-CoV-2) has posed an unprecedented challenge to human health. With continuous mutations, the Omeprone variant has become the dominant circulating strain. Compared to previous variants, the Omeprone variant has stronger transmissibility and immune evasion capabilities, but weaker pathogenicity. Therefore, most patients only experience mild infection, with cough being the most common symptom. An online cross-sectional survey in China showed that 91.7% of those infected with the Omeprone variant presented with cough, and as many as 13% experienced persistent cough (cough lasting >3 weeks). A small percentage of patients even developed refractory cough. Coughing not only accelerates viral transmission but also causes urinary incontinence, chest pain, headaches, and even severe insomnia, anxiety, and depression in some patients, seriously affecting their quality of life and mental and physical health.
[0003] The pathogenesis of post-Omeprone infection cough (POVIC) involves numerous complex factors, including airway and lung tissue inflammation, airway mucus hypersecretion, and neuroimmunity. The pathogenic mechanisms of these factors are as follows: Omeprone variants act on the vagal sensory nerve endings in the airway, triggering the release of neuropeptides such as substance P, neurokinin, and calcitonin gene-related peptide through local axonal reflexes. Sensory neuropeptides recruit inflammatory cells to release inflammatory mediators (interleukins, interferon-gamma, etc.), inducing and exacerbating the inflammatory response, and partially acting directly on airway tissue to cause cough. Sensory neuropeptides and inflammatory mediators stimulate receptors on the vagal nerve endings in the airway, creating a positive feedback loop for axonal reflexes and simultaneously enhancing impulse afferent to the central nervous system, thereby increasing the hypersensitivity of the medullary cough center. This, in turn, leads to increased airway cough hypersensitivity by enhancing vagal efferent nerve activity. The hypersensitivity of the airway and cough center after viral infection is an important basis for selecting cough treatment drugs. Therefore, compound methoxyphenamine and ICS / LABA are good choices for treating post-infectious cough (POVIC) due to their respective antitussive, bronchodilator, and anti-inflammatory effects. Previous studies have found that compound methoxyphenamine has a 90% efficacy rate in treating post-infectious cough. However, in clinical practice, the efficacy rate of compound methoxyphenamine in treating POVIC has been found to be much lower than the data from previous studies; and in patients who do not respond to treatment, the combined use of compound methoxyphenamine and ICS / LABA often results in cough relief or even disappearance in a large proportion of patients. Therefore, how to select appropriate medications for different patient groups to achieve individualized and precise drug treatment, reduce ineffective treatment and adverse drug reactions, is one of the major challenges faced by respiratory physicians.
[0004] In preliminary clinical studies, the invention team of this application discovered that pulmonary function and exhaled nitric oxide, as commonly used, simple, economical, and effective test indicators, can be used to assess the early diagnosis and prognosis of non-severe Omeprone variant infections. Recent clinical studies suggest that the high sensitivity of PIVIC is reflected in changes in pulmonary function, specifically decreased forced expiratory volume in one second (FEV1), forced vital capacity (FVC), mid-expiratory flow rate, and diffusion capacity, as well as increased exhaled nitric oxide. However, whether changes in these parameters can predict the efficacy of trimethoprim-methyl combined with ICS / LABA in the treatment of PIVIC remains unknown.
[0005] Therefore, it is necessary to design a convenient and effective method for judging objective clinical indicators to assist in screening the population suitable for different drug treatments for POVIC. Summary of the Invention
[0006] The main objective of this invention is to address the above-mentioned problems by providing a method, apparatus, computer-readable storage medium, and electronic device for predicting the efficacy of PIVIC drugs.
[0007] To achieve the above objectives, a first aspect of the present invention provides a method for predicting the efficacy of PovIC drug, characterized in that the prediction method includes: obtaining the lung function index FEF of the predicted subject. 50％ Regression was performed on the three datasets (FeNO) to obtain the P-value. When the P-value was less than or equal to the preset value, the drug predicted to be effective in treating PovIC was the combination of methoxyphenamine and ICS / LABA. When the P-value was greater than the preset value, the drug predicted to be effective in treating PovIC was the use of methoxyphenamine alone.
[0008] Preferably, the regression processing model is specifically as follows:
[0009] Where P represents the probability of effective treatment with compound methoxyphenamine and ICS / LABA, FeNO represents exhaled nitric oxide, and FEF represents exhaled nitric oxide. 50％ It represents the expiratory flow rate when forcefully exhaling 50% of lung capacity, where e is a natural constant.
[0010] Preferably, the preset value is 0.36805.
[0011] A second aspect of the present invention provides a device for predicting the efficacy of PovIC drug, characterized in that the predicting device comprises:
[0012] The data generation unit is used to acquire the lung function index FEF. 50％ and FeNO;
[0013] The data processing unit is used to process the obtained lung function index FEF 50％ Regression was performed on FeNO to obtain the P-value;
[0014] The prediction unit predicts that when the P-value is less than or equal to the preset value, the effective drug for treating PovIC is: compound methoxyphenamine and ICS / LABA combination; when the P-value is greater than the preset value, the effective drug for treating PovIC is: compound methoxyphenamine alone.
[0015] The prediction result display unit is used to display the P-value and the prediction result.
[0016] Preferably, the data generation unit includes a data acquisition module and an internal storage unit. The data acquisition module is connected to a data input device via a data bus and processes the acquired lung function index FEF. 50％ The FeNO index information is transmitted to the internal storage for storage.
[0017] Preferably, the data processing unit includes a data extraction module, a regression processing module, and a classification and storage module. The data extraction module is used to extract a dataset from the internal memory of the data generation unit, and transmit the dataset to the regression processing module. The regression model is used to perform regression calculation to obtain the P-value, and the P-value is transmitted to the classification and storage module.
[0018] Preferably, the regression processing model is specifically as follows:
[0019] Where P represents the probability of effective treatment with compound methoxyphenamine and ICS / LABA, FeNO represents exhaled nitric oxide, and FEF represents exhaled nitric oxide. 50％ It represents the expiratory flow rate when forcefully exhaling 50% of lung capacity, where e is a natural constant.
[0020] Preferably, the preset value is 0.36805.
[0021] A third aspect of the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program, when executed by a processor, implements the various steps of the method for predicting the efficacy of the PIVIC drug.
[0022] A fourth aspect of the present invention provides an electronic device comprising:
[0023] Processor; and
[0024] Memory for storing the executable instructions of the processor;
[0025] The processor is configured to execute the various steps of the POWIC drug efficacy prediction method by executing the executable instructions.
[0026] Compared with the prior art, the present invention has the following beneficial technical effects:
[0027] The combined use of FeNO and FEF50% can screen for patients suitable for the treatment of PovIC with compound methoxyphenamine and ICS / LABA, helping clinicians to achieve individualized and precise treatment of PovIC, thereby improving the effectiveness of drug therapy, reducing adverse reactions, and maximizing clinical benefits for patients. Both clinical examination indicators used can be obtained from hospital pulmonary function test reports, eliminating the need for complex additional examinations and greatly reducing the number of examinations and examination costs for patients. Attached Figure Description
[0028] Figure 1This is a flowchart illustrating the process of screening and differentiating patients who respond well to treatment with compound methoxyphenamine and ICS / LABA or who respond well to treatment with compound methoxyphenamine alone. A regression model was established using FEF50% and FeNO to obtain the probability P-value. When the P-value is less than or equal to 0.36805, compound methoxyphenamine and ICS / LABA triple therapy is used; when the P-value is greater than 0.36805, compound methoxyphenamine is used alone to treat POVIC.
[0029] Figure 2 This is a schematic diagram of the predictive device of the present invention for screening suitable patients for treatment with compound methoxyphenamine and ICS / LABA and predicting their treatment effects.
[0030] Figure 3 The experimental group of this invention consists of FeNO and FEF. 50％ The ROC curve of predicting the efficacy of PICC after ineffective treatment with compound methoxyphenamine alone or in combination, followed by continued ICS / LABA treatment. FeNO and FEF 50％ Combined application of modeling to predict the effective AUC of POVIC in combination therapy with methoxyphenamine and ICS / LABA Model =0.839, (95% CI, 0.712-0.925); AUC FeNO =0.767 (95% CI, 0.631 to 0.872; P = 0.1236, compared with FeNO and FEF) 50％ Compared to models using combined applications; AUC FEF50％ =0.691 (95% CI, 0.550 to 0.811; P = 0.4090 and = 0.0178, respectively, compared with the model and FeNO alone).
[0031] Figure 4 The verification group for this invention consists of FeNO and FEF. 50％ The ROC curve of predicting the efficacy of PICC after ineffective treatment with compound methoxyphenamine alone or in combination, followed by continued ICS / LABA treatment. FeNO and FEF 50％ Combined application of modeling to predict the AUC of POVIC treated with methoxyphenamine and ICS / LABA Model =0.843, (95% CI, 0.717-0.929); AUC FeNO =0.746 (95% CI, 0.608 to 0.856; P = 0.0547, compared with FeNO and FEF) 50％ Compared to models using combined applications; AUC FEF50％ =0.761 (95% CI, 0.625 to 0.868; P = 0.1195 and = 0.8632, respectively, compared with the model and FeNO alone). Detailed Implementation
[0032] To provide a clearer understanding of the technical content of this invention, the following embodiments are provided in detail. However, it is important to note that these descriptions are merely for further illustrating the features and advantages of this invention, and not for limiting the scope of the claims.
[0033] Unless otherwise specified, the reagents and methods involved in the examples are all commonly used in the art.
[0034] This invention provides a method for rapidly screening drug treatments for post-COVID-19 cough (POVIC) based on lung function and FeNO test indicators, particularly for screening patients who respond well to treatment with methoxyphenamine combined with inhaled corticosteroids / long-acting β2-agonists (ICS / LABA). This method is simple, efficient, and objective, and can assist in the rapid screening and early prediction of PVIC patients suitable for methoxyphenamine and ICS / LABA.
[0035] The present invention provides a method for predicting the efficacy of PIVIC drugs, namely, a method for screening PIVIC patients suitable for combined methoxyphenamine and ICS / LABA therapy and for assessing drug efficacy. Its main feature is that the prediction device includes:
[0036] The data generation unit is used to acquire the lung function index FEF. 50％ and FeNO;
[0037] The data processing unit is used to process the obtained lung function index FEF 50％ Regression was performed on FeNO to obtain the P-value;
[0038] The prediction unit predicts that when the P-value is less than or equal to the preset value, the effective drug for treating PovIC is: compound methoxyphenamine and ICS / LABA combination; when the P-value is greater than the preset value, the effective drug for treating PovIC is: compound methoxyphenamine alone.
[0039] The prediction result display unit is used to display the P-value and the prediction result.
[0040] Perform the prediction method as follows:
[0041] The data generation unit acquires the patient's lung function and FeNO index information; FeNO and FEF 50％ All indicators are routine clinical examination indicators, and all are cross-sectional data;
[0042] The obtained lung function index FEF 50％ The FeNO index information is stored in the internal memory of the data generation unit;
[0043] The data processing unit is activated to extract the classified drug treatment model dataset from the internal storage of the data generation unit.
[0044] Regression processing was performed in the data processing unit to obtain the P-value, which represents the effective probability of treatment with compound methoxyphenamine and ICS / LABA. The regression calculation method is as follows:
[0045] Where P represents the probability of effective treatment with compound methoxyphenamine and ICS / LABA, FeNO represents exhaled nitric oxide, and FEF represents exhaled nitric oxide. 50％ This represents the expiratory flow rate when forcefully exhaling 50% of vital capacity, where e is a natural constant.
[0046] The P-values are sent to the classification and storage modules inside the result display unit and the data processing unit, respectively, for result display and classification storage.
[0047] like Figure 1 and Figure 2 The following is a method for screening patients who respond well to the combined treatment of compound methoxyphenamine and ICS / LABA for post-infectious cough of Omeprazole, as described in this invention, and is carried out according to the following steps:
[0048] Data acquisition module 1-1 of data generation unit 1 acquires the patient's name, age, lung function, and FeNO parameter information, where the lung function parameter is FEF. 50％ Both FeNO and FeNO are commonly used clinical examination indicators.
[0049] The data classification module 1-2 classifies the acquired name, age, lung function and exhaled nitric oxide information according to different treatment plans as classification conditions. The treatment plans are divided into compound methoxyphenamine group and compound methoxyphenamine and ICS / LABA combined use group. The classified treatment plan dataset is obtained and stored in the internal storage 1-3 in the data generation unit 1.
[0050] Start data processing unit 2, and data extraction module 2-1 extracts the classified model dataset of effective treatment of compound methoxyphenamine and ICS / LABA group from the internal storage 1-3 of data generation unit 1.
[0051] Regression processing is performed in regression processing module 2-2 of data processing unit 2. The calculation method for regression processing is as follows: Where P represents the probability of treatment with compound methoxyphenamine and ICS / LABA, FeNO represents exhaled nitric oxide, FEF50% represents the expiratory flow rate when forcefully exhaling 50% of vital capacity, and e is the natural constant.
[0052] The effective probability of treatment is sent to the result display unit 3 and the classification and storage module 2-3 inside the data processing unit for result display and classification and storage. In the classification and storage module 2-3, the classification condition is to classify and store the data according to the patient's name.
[0053] Lung function and FeNO levels can be obtained by doctors in outpatient or inpatient departments prescribing tests for patients initially diagnosed with post-Omeprón infection and cough, or by patients going to a general hospital for testing.
[0054] experimental group
[0055] Clinicians initially enrolled patients with post-infectious cough (PIP) who visited Shanghai First People's Hospital, Shanghai First People's Hospital Jiuquan Branch, and Shanghai First People's Hospital Jiuquan Branch-Xincheng Branch between December 2022 and September 2024 and met the criteria. In this experimental group, a total of 55 subjects were included, with 28 patients receiving the combination of methoxyphenamine and ICS / LABA, and 27 patients receiving methoxyphenamine alone. The inclusion and exclusion criteria for this experimental group are as follows:
[0056] (a) Inclusion criteria:
[0057] (1) Age 18-75 years old;
[0058] (2) Nasopharyngeal swab reverse transcription polymerase chain reaction (RT-PCR) test showed positive for novel coronavirus nucleic acid (Ct value less than 35);
[0059] (3) Coughing is the main symptom after COVID-19 infection and has persisted for more than 3 weeks without significant improvement;
[0060] (4) Serum influenza virus IgM test was negative;
[0061] (5) Within three days of the visit, blood routine, CRP, tIgE, pulmonary function, chest CT and FeNO tests should be performed.
[0062] (II) Exclusion Criteria:
[0063] (1) Patients with active pulmonary tuberculosis, bronchiectasis, neoplastic diseases, asthma, chronic obstructive pulmonary disease, asthma-chronic obstructive pulmonary disease overlap syndrome, interstitial lung disease, allergic rhinitis, autoimmune diseases, etc.
[0064] (2) Has undergone immunotherapy, drug and food allergies, or used antibiotics in the past 6 months;
[0065] (3) Pregnant women;
[0066] (4) Hypertensive patients who have inhaled or taken hormones in the past 28 days, taken antitussives in the past 7 days, or are currently taking angiotensin-converting enzyme inhibitors;
[0067] (5) Suffering from potential infectious diseases such as urinary tract infection or intestinal obstruction.
[0068] (III) Specific Implementation Steps
[0069] (1) First, the blood routine, pulmonary function parameters and FeNO index of the two groups were compared: the group using compound methoxyphenamine and ICS / LABA in combination and the group using compound methoxyphenamine alone. The results showed that the eosinophil count (EOS) and percentage (EOS%), the percentage of the measured value in one second to the predicted value (FEV1, % predicted), the percentage of the measured value in forced vital capacity (FVC, % predicted), and FEF were significantly improved. 50％ FEF 25％ FEF 25％-75％ There was a significant statistical difference between FeNO and FeNO.
[0070] (2) Analyze the parameters with statistically significant differences using ROC curves (receiver operating characteristic curves) and receiver operating characteristic curves, and screen out the FEF (Features, Analyses, and Results). 50％ The combined FeNO has the highest AUC (area under the ROC curve). Therefore, FEF is chosen. 50％ FeNO was used as a predictive indicator for the combined use of compound methoxyphenamine and ICS / LABA.
[0071] (3) Figure 3 The results shown are from the experimental group of this invention. The combined application of FeNO (>26ppb) and FEF... 50％ (≤79%) Selecting patients who responded to treatment with compound methoxyphenamine and ICS / LABA, this assessment method had a sensitivity of 67.86%, a specificity of 88.00%, a positive predictive value of 86.40%, a negative predictive value of 71.00%, and an area under the receiver operating characteristic curve (ROC) of 0.839, making it fully suitable as an adjunct tool for rapid screening in selecting this drug combination. FEF alone or in combination 50％ The specific results of the FeNO predictive value analysis of the combined use of compound methoxyphenamine and ICS / LABA for the treatment of post-infectious cough in Omeprone (experimental group) are shown in Table 1.
[0072] Table 1
[0073]
[0074] Note: FEF 50％ %, % predicted, expiratory flow rate at 50% of vital capacity; FeNO: exhaled nitric oxide; AUC: area under the ROC curve; Cut-off values: threshold; sensitivity; specificity;
[0075] PPV: Positive predictive value; NPV: Negative predictive value; +LR: Positive likelihood ratio; -LR: Negative likelihood ratio.
[0076] Verification group
[0077] Clinicians enrolled patients with post-infectious cough (PIP) who visited Shanghai First People's Hospital, Shanghai First People's Hospital Jiuquan Branch, and Shanghai First People's Hospital Jiuquan Branch-Xincheng Branch between October 2024 and June 2025 and met the criteria. In the validation group, a total of 53 subjects were included, with 29 patients receiving methoxyphenamine and ICS / LABA treatment, and 24 patients receiving methoxyphenamine alone.
[0078] (1) The inclusion and exclusion criteria are the same as those for the experimental group;
[0079] (2) Figure 4 The external validation results of this invention are shown. FeNO (>23ppb) and FEF50% (≤79.2%) predicted the efficacy of treatment with compound methoxyphenamine and ICS / LABA. The sensitivity of this method was 68.97%, the specificity was 87.50%, the positive predictive value was 87.00%, the negative predictive value was 70.00%, and the area under the ROC curve was 0.843. The specific results of the value analysis of FEF50% and FeNO alone or in combination in predicting the efficacy of combined compound methoxyphenamine and ICS / LABA in treating post-Omeprazole cough (validation group) are shown in Table 2.
[0080] Table 2
[0081]
[0082] Note: FEF 50％ %, % predicted, forceful exhalation of 50% of vital capacity expiratory flow rate; FeNO: exhaled nitric oxide;
[0083] AUC: Area under the ROC curve; Cut off values: Threshold; Sensitivity: Sensitivity; Specificity: Specificity; PPV: Positive predictive value; NPV: Negative predictive value; +LR: Positive likelihood ratio; -LR: Negative likelihood ratio.
[0084] The above results confirm that the combined use of FeNO and FEF 50％ It can screen suitable patients for the treatment of PovIC with compound methoxyphenamine and ICS / LABA, helping clinicians to achieve individualized and precise treatment of PovIC, thereby improving the effectiveness of drug treatment, reducing adverse reactions, and maximizing clinical benefits for patients. In addition, the two clinical examination indicators used can be obtained from the hospital's pulmonary function test report, without the need for complicated additional examinations, which greatly reduces the number of examinations and examination costs for patients.
[0085] This invention is simple to implement and applicable not only to clinicians but also to patients with post-COVID-19 cough and their families. It allows for medication selection using relevant test results, enabling individualized and precise disease diagnosis and treatment. It also empowers patients and their families to participate actively in treatment, thereby improving treatment adherence and maximizing clinical benefit. Furthermore, this invention achieves individualized and precise treatment of post-COVID-19 cough, improving treatment efficiency. Its simple implementation allows even non-professionals to quickly master it, enabling self-triage, improving treatment adherence, and enhancing disease prognosis.
[0086] In this specification, the invention has been described with reference to specific embodiments thereof. However, it will be apparent that various modifications and variations can be made without departing from the spirit and scope of the invention. Therefore, this specification should be considered illustrative rather than restrictive.
Claims
1. A method for predicting the efficacy of PovIC drug, characterized in that, The prediction method includes: obtaining the lung function index FEF of the prediction subject. 50% The data for exhaled nitric oxide (FeNO) and exhaled nitric oxide (FeNO) were used to perform regression analysis to obtain the probability P-value. When the P-value was less than or equal to a preset value, the predicted effective drug for treating PovIC was the combination of compound methoxyphenamine and ICS / LABA; when the P-value was greater than the preset value, the predicted effective drug for treating PovIC was compound methoxyphenamine alone. The specific regression model was as follows: Where P represents the probability of effective treatment with compound methoxyphenamine and ICS / LABA, FeNO represents exhaled nitric oxide, and FEF represents exhaled nitric oxide. 50% It represents the expiratory flow rate when forcefully exhaling 50% of lung capacity, where e is a natural constant.
2. The method for predicting the efficacy of PIVIC drug according to claim 1, characterized in that, The preset value is 0.36805.
3. A device for predicting the efficacy of PIVIC drug, characterized in that, The prediction device includes: The data generation unit is used to acquire the lung function index FEF. 50% and FeNO; Data processing unit, used to process the obtained FEF 50% Regression was performed on FeNO to obtain the P-value; The prediction unit predicts that when the P-value is less than or equal to the preset value, the effective drug for treating PovIC is: compound methoxyphenamine and ICS / LABA combination; when the P-value is greater than the preset value, the effective drug for treating PovIC is: compound methoxyphenamine alone. The prediction result display unit is used to display the P-value and the prediction result; The regression model is as follows: Where P represents the probability of effective treatment with compound methoxyphenamine and ICS / LABA, FeNO represents exhaled nitric oxide, and FEF represents exhaled nitric oxide. 50% It represents the expiratory flow rate when forcefully exhaling 50% of lung capacity, where e is a natural constant.
4. The prediction device according to claim 3, characterized in that, The data generation unit includes a data acquisition module and an internal storage unit. The data acquisition module is connected to a data input device via a data bus and processes the acquired lung function index FEF. 50% The FeNO index information is transmitted to the internal storage for storage.
5. The prediction device according to claim 3, characterized in that, The data processing unit includes a data extraction module, a regression processing module, and a classification and storage module. The data extraction module is used to extract a dataset from the internal memory of the data generation unit and transmit the dataset to the regression processing module. The regression model is used to perform regression calculations to obtain the P-value, and the P-value is transmitted to the classification and storage module.
6. The prediction device according to claim 3, characterized in that, The preset value is 0.36805.
7. A computer-readable storage medium, characterized in that, It stores a computer program that, when executed by a processor, implements the steps of the method for predicting the efficacy of PIVIC drug as described in any one of claims 1 to 2.
8. An electronic device, comprising: processor; as well as Memory for storing the executable instructions of the processor; The processor is configured to implement the steps of the method for predicting the efficacy of POWIC drug according to any one of claims 1 to 2 by executing the executable instructions.