Prognostic / cost-utility evaluation methods and models for chronic obstructive pulmonary disease

Abstract

The present application relates to the field of medical evaluation technology, and specifically discloses a chronic obstructive pulmonary disease prognosis and cost-utility evaluation model. The model is implemented by combining modules such as data collection, data design, and prognosis analysis / cost-utility evaluation. The model can effectively predict the prognosis and cost-utility of chronic obstructive pulmonary disease treatment.

Description

Technical Field

[0001] The present application relates to the field of medical evaluation technology, and in particular to a prognosis / cost-utility evaluation method and model for chronic obstructive pulmonary disease. Technical Background

[0002] Chronic obstructive pulmonary disease (COPD) is a heterogeneous disease, with symptoms varying from patient to patient. Some patients present with frequent exacerbations, while others primarily experience dyspnea. The COPD phenotype is defined as a characteristic or set of disease features that describe differences between COPD patients and are associated with clinical outcomes (e.g., symptoms, exacerbations, efficacy, rate of disease progression, and mortality). Clinical outcomes vary for patients with different COPD phenotypes. Therefore, it is crucial to clarify the phenotypic characteristics of various COPD patients, select more personalized treatment options, and provide more accurate prognostic assessments and monitoring indicators for patients with different manifestations, thereby improving clinical efficacy and enhancing patients' quality of life.

[0003] In the context of disease management, acute exacerbations are a significant clinical event in COPD, significantly contributing to a decline in quality of life. Studies have also shown that compared with healthy subjects, COPD patients experience impairment in all aspects of quality of life, as assessed by daily living activities, emotional functioning, social role functioning, and recreational pursuits. The concept of "quality of life" in medicine refers to the comprehensive state of a patient's physical, psychological, and social well-being during illness or treatment. Pulmonary function tests are currently recognized as objective indicators for detecting airflow limitation and are the "gold standard" for diagnosing COPD. They are also the most commonly used indicators for assessing COPD severity, monitoring disease progression, prognosis, and treatment response. Studies have suggested that using only clinical function indicators may underestimate a patient's clinical manifestations and overestimate the effectiveness of treatment. Furthermore, in some cases, the clinical manifestations of some patients do not fully correspond to their pulmonary function level.

[0004] Acute exacerbations of COPD (COPD) are managed according to international and domestic guidelines, including the Global Initiative for Chronic Obstructive Lung Disease (GOLD), the Chinese Expert Consensus on the Diagnosis and Treatment of Acute Exacerbations of Chronic Obstructive Lung Disease (AECOPD) (2017 Update), and the Guidelines for Primary Care Diagnosis and Treatment of COPD (2018). However, due to variations in the promotion and adherence of GOLD guidelines by physicians, as well as the impact of medication availability and medical insurance policies, COPD treatment in actual clinical practice varies across regions and patients. Furthermore, the research evidence guiding clinical diagnosis and treatment in international and domestic COPD guidelines is largely based on randomized controlled trials with strict inclusion criteria. Most of these included patients with "standard" COPD, excluding other respiratory conditions such as asthma, bronchiectasis, and lung cancer. In clinical practice, patient profiles vary, and the applicability of conclusions drawn from well-designed randomized controlled trials to real-world COPD patients remains unclear. There are currently insufficient real-world clinical practice data to determine its efficacy in the treatment of COPD.

[0005] In view of this, this application is filed. Summary of the Invention

[0006] To address the above technical issues, this application investigates the current status of diagnosis and treatment of AECOPD patients in Shanxi Province, establishes a disease database for COPD patients in Shanxi Province, analyzes patients' disease characteristics and clinical outcomes, and thereby establishes a model for predicting the risk of acute exacerbations, explores individualized treatment models for COPD, and provides a reference for the formulation of medical and health policies. Based on these research results, this application specifically proposes the following technical solutions:

[0007] The present application first provides a method for evaluating the prognosis of chronic obstructive pulmonary disease (COPD), a model for evaluating the prognosis of chronic obstructive pulmonary disease (COPD), characterized in that the model includes the following modules:

[0008] 1) Data collection module: used to collect clinical medical records and follow-up data of patients with acute exacerbation of COPD;

[0009] 2) Data design module: used to design baseline data questionnaire;

[0010] 3) Prognostic analysis module: including a. variable assignment and preprocessing for baseline data questionnaire; and b. prognostic analysis of adverse outcomes and factors affecting quality of life in COPD patients.

[0011] In some aspects, the 1) data collection module is specifically used to: collect clinical medical records and follow-up data of patients with acute exacerbation of COPD based on a prospective observational study design;

[0012] Preferably, the follow-up data are the follow-up data at 3, 6, 9, 12, 15, 18, 21, and 24 months after discharge;

[0013] More preferably, the acute exacerbation of COPD refers to a patient with an acute worsening of respiratory symptoms, resulting in the need for additional treatment; the patient has at least two or more symptoms of cough, increased sputum volume, purulent sputum, dyspnea or wheezing, including exacerbation of existing symptoms and new symptoms, which persist for at least 48 hours, exceed daily variation, and result in the need for additional treatment;

[0014] Further preferably, the inclusion and exclusion criteria of the patients are as follows:

[0015] Inclusion criteria: (1) age ≥ 40 years, regardless of gender; (2) patients diagnosed with COPD by pulmonary function test, with FEV1 / FVC < 0.7 after inhalation of bronchodilators; (3) patients confirmed to have acute exacerbation of COPD; (4) subjects voluntarily participated in this clinical trial and signed the informed consent form.

[0016] Exclusion criteria: (1) severe mental or neurological disorders that affect informed consent and / or expression or observation of adverse reactions; (2) subjects who are unable to complete the initial questionnaire and undergo pulmonary function tests; (3) subjects who are currently participating in other drug clinical trials or interventional studies.

[0017] Exit cohort criteria: (1) The subject requests to withdraw from the clinical trial; (2) Serious adverse events or death occur during the trial; (3) The patient is lost to follow-up; (4) The researcher or (and) the sponsor believes that the patient is not suitable to continue participating in this application.

[0018] In some aspects, the 2) data design module includes two questionnaires: a baseline questionnaire and a follow-up questionnaire:

[0019] The contents of the baseline questionnaire included the patient's basic information, laboratory test results, treatment measures, medical expenses, SGRQ questionnaire, CAT questionnaire, PEACE questionnaire, and mMRC questionnaire;

[0020] The contents of the follow-up questionnaire included the patient's basic information, CAT questionnaire, SGRQ questionnaire, adverse outcomes and other follow-up information.

[0021] The basic information in the questionnaire includes:

[0022] The patient's basic information includes: gender, age, occupation, medical insurance, marital status, admission route, length of hospital stay, smoking index (number of cigarettes smoked per day × number of years of smoking), body mass index (BMI), personal history (including drinking history, family history, allergy history, surgical history, etc.) and whether the current illness has received treatment.

[0023] The test results include: red blood cell count (WBC), white blood cell count (BRC), hemoglobin concentration (HGB), hematocrit (HCT), platelet count (PLT), absolute lymphocyte count (LYMPH#), absolute neutrophil count (NEUT#), absolute eosinophil count (EO#), lymphocyte percentage (LYMPH%), neutrophil percentage (NEUT%), eosinophil percentage (EO%), alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALT / AST, albumin (ALB), albumin (ALB), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), urea (UREA), creatinine (CREA), potassium (K), sodium (Na), chloride (Cl), calcium (Ca), urine specific gravity (SG), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR) and D-dimer (D-Dimer).

[0024] The treatment measures include: blood pressure monitoring, blood oxygen monitoring, short-acting bronchodilators, glucocorticoids, antibiotics (including whether used in combination), theophylline drugs, expectorants or antioxidants (including ambroxol, eucalyptol, bromhexine, N-acetylcysteine, carbocysteine, fudosteine, erdosteine) and nebulization therapy.

[0025] Medical treatment costs include: total cost, service fee, diagnosis fee, drug fee and triple fee (service fee + diagnosis fee + drug fee).

[0026] The adverse outcomes were acute exacerbation, outpatient visit, rehospitalization, or death within one year after discharge.

[0027] In some aspects, in the 3) prognostic analysis module: the variable assignment and preprocessing of the baseline data questionnaire SGRQ specifically includes:

[0028] The variable assignment and preprocessing of the baseline data questionnaire are as follows:

[0029]

[0030]

[0031] In some aspects, in the 3) prognostic analysis module, the COPD patient adverse outcome prognostic analysis comprises the following steps:

[0032] Adverse outcomes were set as acute exacerbation, outpatient visit, rehospitalization, and death. The inclusion test level was set at 0.05, and the exclusion test level was set at 0.10. A stepwise screening strategy was used to screen out the variables: length of hospitalization, treatment for the current episode, hematocrit, urine specific gravity, and ambroxol. Age, gender, BMI, and smoking index were then included to construct a Cox proportional hazard model for univariate and multivariate analysis. Preferably, smoking index, length of hospitalization, and urine specific gravity were screened out to have statistical significance for prognosis.

[0033] Preferably, the steps further include:

[0034] 1) After adjusting adverse outcomes to include acute exacerbation, rehospitalization, and death, a Cox proportional hazard model was constructed based on the same method as above to conduct univariate and multivariate analyses to confirm whether prognostic factors changed after adjustment for adverse outcomes;

[0035] 2) Adjustment of screening strategy: The stepwise screening strategy was changed to include the test level at 0.05 and exclude the test level at 0.15, and to include the test level at 0.10 and exclude the test level at 0.15, respectively. The effects of different screening strategies on the screening of factors affecting the original adverse outcomes and the adjusted adverse outcomes were analyzed.

[0036] Combined with the adjustments, the main prognostic factors were comprehensively determined:

[0037] When the adverse outcomes were acute exacerbation, outpatient visit, rehospitalization, and death, the basic prognostic factors were smoking index, length of hospitalization, and urine specific gravity; when the adverse outcomes were acute exacerbation, rehospitalization, and death, the main prognostic factors were length of hospitalization, SGRQ total score, and NAC.

[0038] In some aspects, in the 3) prognostic analysis module, in the analysis of factors affecting quality of life:

[0039] For the SGRQ total score, the unit value of difference (UVD) of the SGRQ total score was constructed based on the minimal clinically important difference (MCID):

[0040]

[0041] Among them, SGRQ i is the total SGRQ score at follow-up time i, MCID SGRQ is the MCID of the total SGRQ score; SGRQThe dependent variable was included in the model for analysis, which means: when other variables remain unchanged, the total score of SGRQ increases (or decreases) by several units due to a certain factor change; when UVD is included in the model, SGRQ When the absolute value of the change is greater than 1, it indicates that the change in the total SGRQ score may have clinical significance;

[0042] The analysis of factors affecting quality of life includes the following steps:

[0043] 1) The dependent variable is set as the unit change value of the SGRQ total score (UVD SGRQ );

[0044] 2) Independent variables include patient basic information, test results, and treatment measures;

[0045] 3) Constructing the generalized estimation model (GEE) to analyze the factors affecting quality of life;

[0046] 4) During the variable screening process, the model with the smallest QIC and QICC was selected as the optimal model; the factors affecting quality of life were determined based on the model;

[0047] Preferably, the factors affecting the quality of life are age, smoking index, mMRC score and follow-up duration.

[0048] The present application also provides a model for cost-utility evaluation of chronic obstructive pulmonary disease (COPD), which includes the following modules:

[0049] 1) Data collection module: used to collect clinical medical records and follow-up data of patients with acute exacerbation of COPD;

[0050] 2) Data design module: used to design COPD baseline data questionnaire;

[0051] 3) Cost-utility analysis module: a) used for variable assignment and preprocessing of the baseline data questionnaire, and b) used for cost-utility analysis.

[0052] In some aspects, the costs include total charges, service charges, diagnostic charges, drug charges, and triple charges (service charges + diagnostic charges + drug charges).

[0053] In some aspects, the data collection module and the data design module are the same as the data collection module and the data design module in the aforementioned prognostic evaluation model.

[0054] In some aspects, in the 3) cost-utility analysis module, the variable assignments of the baseline data questionnaire are as follows:

[0055]

[0056]

[0057] Among them, the SGRQ change score per 100 yuan is defined as the difference between the baseline and three-month SGRQ values ​​as a utility indicator for quantifying hospitalization;

[0058] The pretreatment included: deleting deceased cases and cases without recorded hospitalization expenses from the entire study population, selecting cases with three-month follow-up records to form a cost-utility subgroup, and dividing the subgroup into a COPD acute exacerbation group and a non-COPD acute exacerbation group according to the main discharge diagnosis.

[0059] In some aspects, in the 3) cost-utility analysis module, the cost-utility analysis comprises the following steps:

[0060] a. Based on whether short-acting bronchodilators and glucocorticoids were used together, the patients were divided into a combined medication group and a non-combination medication group, and a cost-effectiveness analysis was performed;

[0061] b. Patients with acute exacerbation of COPD were divided into an antibiotic use group and a non-antibiotic use group, and the cost-effectiveness difference between the two groups was analyzed;

[0062] c. Analyze the factors affecting cost-effectiveness of patients with acute exacerbation of COPD;

[0063] Preferably, in the above a, whether there is a difference in cost-effectiveness between the combined medication group and the non-combination medication group, the total cost of a hospitalized patient is C1, the drug cost is C2, the diagnosis fee is C3, the service fee is C4, the triple fee (service fee + diagnosis fee + drug cost) is C5, the difference in the patient's baseline and three-month SGRQ is U, and the formula Y is used. i =100U / C and calculate Y1, Y2, Y3, Y4, Y5 respectively. i Two independent sample t tests were performed with as the dependent variable and concomitant medication (combination of short-acting bronchodilators and glucocorticoids) as the independent variable;

[0064] In b, is there a difference in cost-utility between the antibiotic group and the non-antibiotic group for patients with acute exacerbation of COPD? i The settings are the same as above, with Y i As the dependent variable, whether antibiotics were used was used as the independent variable, and two independent sample t tests were performed;

[0065] In the above c, for the factors affecting the cost-effectiveness of patients with acute exacerbation of COPD, Y i The settings are the same as above, with Y iA regression equation was established with age, BMI, dyspnea, and exercise capacity as the dependent variable and general linear regression and multiple linear regression methods were used to analyze the possible influencing factors of cost-effectiveness.

[0066] More preferably, the cost-utility analysis determined that only dyspnea was statistically significant for total cost, service fee, diagnostic fee, drug fee, and triple fee.

[0067] The present application also provides a model for prognosis assessment and cost-utility assessment of chronic obstructive pulmonary disease (COPD), which includes the aforementioned prognosis assessment model and cost-utility assessment model. BRIEF DESCRIPTION OF THE DRAWINGS

[0068] Figure 1 , registration study flowchart for patients with acute exacerbation of COPD. DETAILED DESCRIPTION

[0069] The embodiments of the present application will be described in detail below with reference to the examples, but it will be understood by those skilled in the art that the following examples are merely illustrative of the present application and should not be construed as limiting the scope of the present application. In the examples, if no specific conditions are specified, the conditions are carried out according to conventional conditions or manufacturer recommendations. The reagents or instruments used are not specified by the manufacturer and are conventional products that can be purchased on the market.

[0070] Definition of some terms

[0071] Unless otherwise defined below, the meaning of all technical terms and scientific terms used in the specific embodiments of the present application is intended to be the same as that generally understood by those skilled in the art. Although it is believed that the following terms are well understood by those skilled in the art, the following definitions are still set forth to better explain the present application.

[0072] As used in this application, an indefinite or definite article when referring to a singular noun eg "a" or "an", "the" includes a plural of that noun.

[0073] As used in this application, the terms "comprises," "comprising," "having," "containing," or "involving" are inclusive or open-ended and do not exclude other unrecited elements or method steps. The term "consisting of" is considered a preferred embodiment of the term "comprising." If a group is defined below as comprising at least a certain number of embodiments, this should also be understood to disclose a group that preferably consists only of these embodiments.

[0074] The term "about" in this application indicates an accuracy range that can be understood by those skilled in the art and still ensures the technical effect of the characteristics discussed. This term usually indicates a deviation from the indicated value by ±10%, preferably ±5%.

[0075] Furthermore, the terms first, second, third, (a), (b), (c), and the like, in the specification and claims, are used to distinguish between similar elements and are not necessarily intended to describe a sequential or chronological order. It is understood that the terms so used are interchangeable under appropriate circumstances, and that the embodiments described herein can be practiced in other sequences than described or illustrated herein.

[0076] The above terms or definitions are provided merely to aid understanding of the present application and should not be construed as having a scope less than that understood by a person skilled in the art.

[0077] The present application is described below with reference to specific embodiments.

[0078] Example 1 Patients and data collection

[0079] This application adopts a prospective observational study design. All patients with acute exacerbation of COPD who visit the research center hospital are enrolled after signing the informed consent. The patients fill out the baseline questionnaire during the visit, and follow up the patients at 3, 6, 9, 12, 15, 18, 21, and 24 months after discharge, which can be done by telephone or on-site visit. Each hospital has a respiratory physician as the person in charge, who is responsible for conducting patient surveys after brief training. Participants are required to complete the scale independently. See the specific process. Figure 1 .

[0080] It should be noted that the term "acute exacerbation of COPD" as used herein refers to patients with acute worsening of respiratory symptoms, leading to the need for additional treatment. Specifically, patients present with at least two or more major symptoms (such as cough, increased sputum volume, purulent sputum, dyspnea, wheezing, etc.), including exacerbation of existing symptoms and new symptoms, lasting for at least 48 hours, exceeding daily variability, and requiring additional treatment, excluding other diseases such as heart failure, arrhythmia, pulmonary embolism, pneumothorax, pleural effusion, etc.

[0081] The specific inclusion and exclusion criteria for patients are as follows:

[0082] Inclusion criteria: (1) age ≥ 40 years, regardless of gender; (2) patients diagnosed with COPD by pulmonary function test, with FEV1 / FVC < 0.7 after inhalation of bronchodilators; (3) confirmed acute exacerbation of COPD; (4) subjects voluntarily participated in this clinical trial and signed the informed consent form. The researcher judged that the subject's compliance met the requirements of this application.

[0083] Exclusion criteria: (1) severe mental or neurological disorders that affect informed consent and / or expression or observation of adverse reactions; (2) subjects who are unable to complete the initial questionnaire and undergo pulmonary function tests; (3) subjects who are currently participating in other drug clinical trials or interventional studies.

[0084] Exit cohort criteria: (1) The subject requests to withdraw from the clinical trial; (2) Serious adverse events or death occur during the trial; (3) The patient is lost to follow-up; (4) The researcher or (and) the sponsor believes that the patient is not suitable to continue participating in this application.

[0085] Ethics and informed consent

[0086] This study was approved by the institutional review board of the principal investigator's hospital (the Second Hospital of Shanxi Medical University, approval number (2019) YX (303)) and the institutional review boards of participating hospitals. Informed consent was collected during hospitalization according to the approval of the local ethics committee.

[0087] This application is in four tertiary or secondary hospitals, including the Second Hospital of Shanxi Medical University, Gujiao Central Hospital, Pianguan County People's Hospital, and Jincheng General Hospital. The clinical medical records and follow-up data of patients who were diagnosed with acute exacerbation of chronic obstructive pulmonary disease (COPD) from April 2021 to May 2022 and met the inclusion and exclusion criteria of this application were collected. The specific patient conditions are shown in Table 3. A total of 135 patient medical records were included in this study, including 114 male patients, accounting for 84.44%, and 21 female patients, accounting for 15.56%; the average age was 67.91±9.35 years old, and the main diagnosis was acute exacerbation of 72 patients, accounting for 53.3%.

[0088] A total of 104 patients (86 males and 18 females) were screened out for the cost-utility subgroup; the youngest patient was 40 years old and the oldest was 89 years old, with an average age of 66.81 years old (see Table 1 below).

[0089] Table 1 Basic information of COPD patients

[0090]

[0091]

[0092] Note: Data are expressed as number (%) or mean ± standard deviation

[0093] Differences between the overall study population and cost-effectiveness subgroups were analyzed based on multiple dimensions, including age, gender, acute onset, smoking history, history of cardiovascular disease, PaO2, and BMI. The results showed that the cost-effectiveness subgroups were representative of the overall study population and applicable to the entire study population to a certain extent.

[0094] Example 2: Data Design and Preprocessing

[0095] The data in this application covers the following aspects, including HIS, LIS, PACS, RIS systems, mMRC, CAT, SGRQ scales, etc.:

[0096] 1. Prepare a COPD baseline questionnaire, including a baseline questionnaire and a follow-up questionnaire:

[0097] The contents of the baseline questionnaire included the patient's basic information, laboratory test results, treatment measures, medical expenses, SGRQ questionnaire, CAT questionnaire, PEACE questionnaire, and mMRC questionnaire;

[0098] The contents of the follow-up questionnaire included the patient's basic information, CAT questionnaire, SGRQ questionnaire, adverse outcomes and other follow-up information.

[0099] The basic information in the questionnaire includes the following:

[0100] Basic information: gender, age, occupation, medical insurance, marital status, route of admission, length of hospital stay, smoking index (number of cigarettes smoked per day × number of years of smoking), personal history (including alcohol consumption history, family history, allergy history, surgical history, etc.), whether the current illness was treated;

[0101] Laboratory results: red blood cell count (WBC), white blood cell count (BRC), hemoglobin concentration (HGB), hematocrit (HCT), platelet count (PLT), absolute lymphocyte count (LYMPH#), absolute neutrophil count (NEUT#), absolute eosinophil count (EO#), lymphocyte percentage (LYMPH%), neutrophil percentage (NEUT%), eosinophil percentage (EO%), alanine aminotransferase (ALT), aspartate aminotransferase (AST), ALT / AST ratio, albumin (ALB), albumin (ALB), total bilirubin (TBIL), direct bilirubin (DBIL), indirect bilirubin (IBIL), urea (UREA), creatinine (CREA), potassium (K), sodium (Na), chloride (Cl), calcium (Ca), urine specific gravity (SG), C-reactive protein (CRP), erythrocyte sedimentation rate (ESR), D-dimer, etc.

[0102] Treatment measures: blood pressure monitoring, blood oxygen monitoring, short-acting bronchodilators, glucocorticoids, antibiotics (including whether used in combination), theophylline drugs, expectorants or antioxidants (including ambroxol, eucalyptol, bromhexine, N-acetylcysteine, carbocysteine, fudosteine, erdosteine), nebulization therapy, etc.

[0103] Medical expenses, including: total fee, service fee, diagnosis fee, drug fee and triple fee (service fee + diagnosis fee + drug fee)

[0104] Adverse outcomes include acute exacerbation, outpatient visits, rehospitalization, and death.

[0105] 2. Variable assignment and preprocessing for prognostic analysis

[0106] The variable assignments used in the prognostic analysis and cost-utility analysis are shown in Tables 2 and 3, respectively:

[0107] Table 2 Prognostic analysis variable assignment table

[0108]

[0109]

[0110] 3. Variable assignment and related definitions for cost-utility analysis

[0111] Table 3 Cost-utility analysis variable assignment table

[0112]

[0113]

[0114] In this application, hospitalization expenses are divided into the following sections: total expenses, service fees (general medical service fees + general treatment operation fees + nursing fees + other comprehensive medical service fees), diagnostic fees (pathology diagnosis fees + laboratory diagnosis fees + imaging diagnosis fees + clinical diagnosis project fees), and medication fees (Western medicine fees + antimicrobial drug fees + traditional Chinese medicine fees + Chinese herbal medicine fees). The difference between the baseline and three-month SGRQ values ​​was defined as the utility indicator for quantifying hospitalization treatment.

[0115] This application deleted the deceased cases and the cases without recorded hospitalization expenses from the entire study population, selected the cases with three-month follow-up records to form the cost-utility subgroup, and divided the subgroup into COPD acute exacerbation group and non-COPD acute exacerbation group according to the main discharge diagnosis.

[0116] Example 3: Prognostic Assessment Analysis

[0117] This paper uses the quantitative data of the research objects to or median(* +, ,* ., ) for description, and count data were described using the constituent ratio n (%). The statistical analysis content of this application is mainly divided into two parts: patient prognosis analysis, analysis of factors affecting quality of life, and cost-utility analysis. All statistical analyses in this application were performed using R 4.1, SAS 9.4, and SPSS25.

[0118] 1. Patient prognosis analysis

[0119] In the prognostic analysis section, the main focus is on the prognostic analysis of adverse outcomes and factors affecting quality of life in COPD patients:

[0120] a. For factors that affect adverse outcomes in COPD patients, use α in =0.05 and α out =0.10, and a Cox proportional hazard model was constructed for univariate and multivariate analysis. In the sensitivity analysis: (1) the adverse outcome was adjusted to acute exacerbation, rehospitalization, or death, and the same predictive indicators and analysis methods as in the above modeling were used to model whether the prognostic factors changed after the adverse outcome adjustment; (2) the stepwise screening strategy was changed to α in =0.05 and α out =0.15 (defined as new strategy 1), α in =0.10 and α out =0.15 (defined as new strategy 2), and the impact of screening strategy on influencing factors was studied.

[0121] b. A generalized estimation model (GEE) was constructed to analyze factors affecting the quality of life of COPD patients. During the variable screening process, the model with the smallest QIC and QICC was selected as the optimal model.

[0122] Experimental results:

[0123] 1. Analysis of factors affecting the prognosis of COPD patients

[0124] Table 4 Screening of factors affecting prognosis in COPD patients

[0125]

[0126]

[0127] Using α in =0.05 and α out A stepwise screening strategy with an RR of 0.10 was used, and the resulting variables are shown in Table 4. Furthermore, demographic variables such as age, sex, BMI, and smoking index were incorporated into the Cox proportional hazards model for univariate and multivariate analyses, with the results shown in Table 5.

[0128] Univariate analysis revealed that age, length of hospital stay, treatment for the current episode, ambroxol, and NAC had a statistically significant impact on patient prognosis. However, multivariate analysis revealed that smoking index, length of hospital stay, and urine specific gravity had a statistically significant impact on patient prognosis. For each unit increase in smoking index, the probability of adverse outcomes (acute exacerbation, medical consultation, rehospitalization, and death) increased by 1.008; for each day increase in hospital stay, the probability of adverse outcomes increased by 0.235; and for each unit increase in urine specific gravity, the probability of adverse outcomes increased by 67.148. However, factors such as age, treatment for the current episode, ambroxol, and NAC were not statistically significant in the multivariate analysis, possibly due to the influence of demographic variables.

[0129] Table 5 Factors affecting the prognosis of COPD patients

[0130]

[0131]

[0132] Note: *Item: The multivariate analysis included hospital grade as a covariate and all variables in the univariate model were included in the analysis; **Item: The normal category in BMI was used as the reference standard.

[0133] Therefore, smoking index, length of hospital stay, and urine specific gravity are risk factors for adverse outcomes (acute exacerbation, medical consultation, rehospitalization, and death).

[0134] 2. Sensitivity analysis

[0135] To assess the sensitivity or stability of the model, this section evaluated the model and screened out key prognostic factors by adjusting the types of adverse outcomes and screening strategies.

[0136] 1) Adjusted adverse outcomes

[0137] Adverse patient outcomes were adjusted to acute exacerbation, rehospitalization, and death (considering that outpatients may have planned outpatient follow-up visits, which would have a certain impact on the analysis results, outpatient visits were deleted). The same modeling method as above was used, and the variable screening results are shown in Table 6.

[0138] Table 6 Screening of prognostic factors in COPD patients after adjusting for adverse outcomes

[0139]

[0140] The results of univariate and multivariate analyses after adding demographic characteristic variables are shown in Table 7.

[0141] Table 7 Factors influencing the prognosis of COPD patients after adjusting for adverse outcomes

[0142]

[0143] Note: *Item: The multivariate analysis included hospital grade as a covariate and all variables in the univariate model for analysis; **Item: The normal category in BMI was used as the reference level.

[0144] Univariate analysis showed that factors influencing adverse outcomes included length of stay, mMRC score, SGRQ total score, and NAC use, all of which were statistically significant. Multivariate analysis revealed that length of stay, SGRQ total score, and NAC were all statistically significant factors influencing adverse outcomes. For every additional day of hospitalization, the probability of an adverse outcome increased by 0.256 times; for every unit increase in SGRQ total score, the probability of an adverse outcome increased by 1.271 times; and the probability of an adverse outcome in patients using NAC was 0.258 times higher than in those not using NAC.

[0145] It can be seen that the length of hospital stay and SGRQ total score are risk factors for adverse outcomes (acute exacerbation, rehospitalization and death) after adjustment, while NAC is a protective factor.

[0146] 2) Impact of different screening strategies on influencing factors

[0147] a. Screening of factors affecting original adverse outcomes under various strategies

[0148] Change the stepwise screening strategy to α in =0.05 and α out =0.15, corresponding to the new strategy 1, α in =0.10 and α out =0.15, corresponding to new strategy 2. The results of variable screening are shown in Tables 8 and 9 respectively.

[0149] Table 8 Screening of factors affecting original adverse outcomes under the new strategy 1

[0150]

[0151] Table 9 Screening of factors affecting original adverse outcomes under the new strategy 2

[0152]

[0153]

[0154] The results in the table above show that compared with the original single-factor screening strategy (Table 4), new strategy 1 remained unchanged. New strategy 2 removed ambroxol from the screened variables and added C-reactive protein, inhaled budesonide suspension, and ALT. This suggests that factors such as duration of hospitalization, treatment for the current episode, hematocrit, and urine specific gravity significantly impact adverse patient outcomes. The impact of ambroxol, C-reactive protein, inhaled budesonide suspension, and ALT also played a role in this study.

[0155] b. Screening of factors influencing adverse outcomes after adjustment under various strategies

[0156] On the basis of the adjusted adverse outcomes, variables were screened using the aforementioned new strategies 1 and 2. The specific results are shown in Tables 10 and 11.

[0157] Table 10 Screening of factors influencing adverse outcomes after adjustment under the new strategy 1

[0158]

[0159] Table 11 Screening of factors influencing adverse outcomes after adjustment under the new strategy 2

[0160]

[0161]

[0162] The results in the table above show that compared with the adjusted adverse outcome results under the original screening strategy (Table 6), New Strategy 1 remained unchanged. New Strategy 2 added ALB, absolute lymphocyte count, urine specific gravity, terbutaline hydrochloride inhalation solution, hematocrit, ALT, and calcium ion to the original screening variables. This suggests that duration of hospitalization, SGRQ total score, and NAC use significantly impacted adverse outcomes. The impact of ALB, absolute lymphocyte count, urine specific gravity, terbutaline hydrochloride inhalation solution, hematocrit, ALT, and calcium ion also played a role in this study.

[0163] Conclusion: From the above results, it can be seen that the performance of the model in this application is relatively stable. Based on different strategies, it can derive consistent major prognostic influencing factors, and can also derive other minor influencing factors.

[0164] These major prognostic factors include: when the adverse outcomes are acute exacerbation, outpatient visit, rehospitalization and death, the basic prognostic factors are smoking index, length of hospitalization and urine specific gravity; when the adverse outcomes are acute exacerbation, rehospitalization and death, the major prognostic factors are length of hospitalization, SGRQ total score and NAC.

[0165] 3. Analysis of factors affecting the quality of life of COPD patients

[0166] The SGRQ questionnaire contains sub-scores in three aspects: patient symptoms, behavior, and impact, which are combined into the total SGRQ score. In order to make the analysis results more clinically interpretable, this application combines the SGRQ total score with its corresponding MCID to construct the new indicator UVDSGRQ as the dependent variable for analysis. Specifically, for the SGRQ total score, in order to effectively reduce its degree of dispersion and reduce the impact of its large variance on the overall model, while making the model parameters more clinically meaningful, this application constructs the unit value of difference (UVD) of the SGRQ total score based on the minimum clinically important difference (MCID):

[0167]

[0168] In the above formula, SGRQ i is the total SGRQ score at follow-up time i, MCID SGRQ is the MCID of the total SGRQ score. According to previous studies, the MCID of the total SGRQ score is usually set at 4 points. SGRQ The analysis was conducted as a dependent variable in the model, which means that when other variables remain unchanged, the total SGRQ score increases (or decreases) by a certain number of MCID units due to a certain factor change. SGRQ When the absolute value of the change is greater than 1, it often indicates that the change in the SGRQ total score may have clinical significance.

[0169] The specific steps are as follows:

[0170] 1) The dependent variable is set as the unit change value of the SGRQ total score (UVD SGRQ );

[0171] 2) Independent variables include indicators of the patient's basic condition, test results, treatment measures, etc.;

[0172] 3) Construct a generalized estimation model (GEE) to analyze the factors affecting quality of life;

[0173] 4) In the process of variable screening, the model with smaller QIC and QICC was selected as the optimal model.

[0174] Generalized estimating equations were used to analyze factors influencing quality of life in COPD patients. The link function of this model was the gamma function, and the corresponding QIC and QICC were 845.71 and 846.69, respectively. The results are shown in Table 12.

[0175] Table 12 Factors affecting the quality of life of COPD patients

[0176]

[0177] Note: *Item: The normal category is used as the reference level in BMI; **Item: The value is too small and the current number of digits cannot be displayed. The original value is approximately 0.0000494; ***Item: The value is too small and the current number of digits cannot be displayed. The original value is approximately 0.0000107.

[0178] As can be seen from the table, age, smoking index, mMRC score and follow-up time are factors that affect the quality of life of COPD patients, and they are statistically significant. SGRQ The value of the smoking index will decrease by 0.0067, and the corresponding SGRQ total score will decrease by 0.0268 points; for every 1 unit increase in the smoking index, the UVD SGRQ The value of the mMRC score will increase by 0.0001, and the corresponding SGRQ total score will increase by 0.0004 points; for every increase of 1 level in the mMRC score, the UVD SGRQ The value of UVD will decrease by 0.0148, and the corresponding SGRQ total score will decrease by 0.0592 points; for every additional day of follow-up, the SGRQ The value of the SGRQ score will increase by 0.0039, and the corresponding SGRQ score will increase by 0.0156 points. Therefore, factors such as age, smoking index, mMRC score and follow-up duration are the main factors affecting the quality of life of COPD patients.

[0179] Example 4: Cost-Effectiveness Analysis

[0180] The cost-utility analysis is mainly divided into three parts: according to whether short-acting bronchodilators and glucocorticoids are used in combination, the patients are divided into combined medication group and non-combination medication group, and the cost-utility difference analysis is conducted; patients with acute exacerbation of COPD are divided into antibiotic use group and non-antibiotic use group, and the cost-utility difference analysis is conducted between the groups; analysis of the influencing factors of cost-utility in patients with acute exacerbation of COPD.

[0181] a. To determine whether there is a difference in cost-effectiveness between the combined medication group and the non-combination medication group, let the total cost of a hospitalized patient be C1, the drug cost be C2, the diagnostic fee be C3, the service fee be C4, and the triple fee (service fee + diagnostic fee + drug cost) be C5. Let the difference in the patient's SGRQ between baseline and three months be U. Calculate Y1, Y2, Y3, Y4, and Y5 using the formula Yi = 100 U / C. i Two independent sample t-tests were performed with the combination of short-acting bronchodilators and glucocorticoids as the dependent variable and the combination of short-acting bronchodilators and glucocorticoids as the independent variable.

[0182] b. Is there a difference in cost-utility between the antibiotic group and the non-antibiotic group in patients with acute exacerbation of COPD? i The settings are the same as above, with Y i Two independent sample t-tests were performed with the use of antibiotics as the dependent variable and whether or not antibiotics were used as the independent variable.

[0183] c. Factors that affect the cost-effectiveness of COPD patients with acute exacerbation i The settings are the same as above, with Y i A regression equation was established with age, BMI, dyspnea and exercise capacity as the dependent variable and general linear regression and multiple linear regression methods were used to analyze the possible influencing factors of cost-effectiveness.

[0184] 1. Cost structure of direct economic burden

[0185] Among the 104 inpatients, the lowest hospitalization cost was 4,306.57 yuan, the highest was 30,906.72 yuan, and the median cost was 10,074.56 yuan. The direct economic burden included the following five items: medication costs, which accounted for the largest proportion, followed by diagnostic fees, service fees, other fees, and surgical and treatment costs. See Table 13 for details.

[0186] Table 13 Composition and ranking of direct economic burden items of 104 inpatients

[0187]

[0188]

[0189] 2. Analysis of factors affecting cost-effectiveness in AECOPD patients is shown in Table 14.

[0190] Table 14 Analysis of factors affecting total cost-effectiveness

[0191]

[0192] Table 14 shows that dyspnea had a statistically significant effect on the cost-utility of total expenses, as determined by a univariate analysis of the cost-utility of total expenses. For every one-level increase in dyspnea, the SGRQ increased by 0.371 per 100 yuan of total expenses. A multivariate analysis of the cost-utility of total expenses revealed that dyspnea was a significant factor influencing the cost-utility of total expenses. For every one-level increase in dyspnea, the SGRQ increased by 0.348 per 100 yuan of total expenses. After eliminating the influence of other factors, only dyspnea maintained a statistically significant effect on the cost-utility of total expenses.

[0193] Table 15 Analysis of factors affecting the cost-effectiveness of service fees

[0194]

[0195] As shown in Table 15, a univariate analysis of the cost-effectiveness of service fees revealed a statistically significant effect of dyspnea on the cost-effectiveness of service fees. For every one-level increase in dyspnea, the corresponding SGRQ increase for every 100 yuan in service fees was 0.406. A multivariate analysis of the cost-effectiveness of service fees revealed that dyspnea was a significant factor influencing the cost-effectiveness of service fees. For every one-level increase in dyspnea, the corresponding SGRQ increase for every 100 yuan in service fees was 0.413. After eliminating the influence of other factors, only dyspnea remained statistically significant in its effect on the cost-effectiveness of service fees.

[0196] Table 16 Analysis of factors affecting the cost-effectiveness of diagnostic fees

[0197]

[0198] A univariate analysis of the cost-effectiveness of diagnostic fees revealed that age and dyspnea had statistically significant effects on the cost-effectiveness of diagnostic fees. For every 1-year increase in age, the SGRQ for every 100 yuan spent on diagnostic fees increased by 0.232. For every one-level increase in dyspnea level, the SGRQ for every 100 yuan spent on diagnostic fees increased by 0.294. A multivariate analysis of the cost-effectiveness of diagnostic fees revealed that dyspnea was a significant factor influencing the cost-effectiveness of diagnostic fees. For every one-level increase in dyspnea level, the SGRQ for every 100 yuan spent on diagnostic fees increased by 0.266. After eliminating the influence of other factors, only dyspnea remained statistically significant, as shown in Table 16.

[0199] Table 17 Analysis of factors affecting drug cost-effectiveness

[0200]

[0201]

[0202] Table 17 shows that dyspnea had a statistically significant effect on the cost-effectiveness of medications, with a one-level increase in dyspnea corresponding to a 100-yuan increase in medication costs. A multivariate analysis of the cost-effectiveness of medications revealed that dyspnea was a significant factor influencing the cost-effectiveness of medications, with a one-level increase in dyspnea corresponding to a 100-yuan increase in medication costs. After eliminating other factors, only dyspnea maintained a statistically significant effect on the cost-effectiveness of medications.

[0203] Table 18 Analysis of factors affecting the cost-effectiveness of triple fee (service fee + diagnosis fee + drug fee)

[0204]

[0205]

[0206] A univariate analysis of the cost-effectiveness of the triple-check fee showed that dyspnea had a statistically significant impact on the cost-effectiveness of the triple-check fee. For every one-level increase in dyspnea, the corresponding SGRQ increase for each 100 yuan triple-check fee was 0.342. A multivariate analysis of the cost-effectiveness of the triple-check fee showed that dyspnea was an influencing factor in the cost-effectiveness of the triple-check fee. For every one-level increase in dyspnea, the corresponding SGRQ increase for each 100 yuan triple-check fee was 0.307. After eliminating the confounding of other factors, only dyspnea remained statistically significant in its impact on the cost-effectiveness of the triple-check fee, as shown in Table 18.

[0207] Therefore, dyspnea is a positively correlated factor affecting the cost-effectiveness of five types of expenses: total cost, service fee, diagnosis fee, drug fee, and triple fee.

[0208] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present application, rather than to limit them. Although the present application has been described in detail with reference to the aforementioned embodiments, those skilled in the art should understand that they can still modify the technical solutions described in the aforementioned embodiments, or make equivalent replacements for some or all of the technical features therein. These modifications or replacements do not deviate the essence of the corresponding technical solutions from the scope of the technical solutions of the embodiments of the present application.

Claims

1. A model for evaluating the prognosis of chronic obstructive pulmonary disease (COPD), characterized in that: The model includes the following modules: 1) Data collection module: used to collect clinical medical records and follow-up data of patients with acute exacerbation of COPD; 2) Data design module: used to design baseline data questionnaire; 3) Prognostic analysis module: including a) variable assignment and preprocessing for baseline questionnaire data; and b) prognostic analysis of adverse outcomes and factors affecting quality of life in COPD patients; In the data collection module 1), the follow-up data are the follow-up data at 3, 6, 9, 12, 15, 18, 21, and 24 months after discharge; In the 2) data design module, the baseline data questionnaire includes a baseline questionnaire and a follow-up questionnaire: The baseline questionnaire included the patient's basic information, test results, treatment measures, medical expenses, SGRQ questionnaire, CAT questionnaire, PEACE questionnaire and mMRC questionnaire; The follow-up questionnaire included basic information of the patient, CAT questionnaire, SGRQ questionnaire, and adverse outcomes; The adverse outcomes described were acute exacerbation, outpatient visit, rehospitalization, or death within one year after discharge; In the prognostic analysis module 3), the pre-processing includes: analyzing the factors affecting the quality of life, constructing the change unit value UVD of the SGRQ total score based on the minimum clinical significance change value MCID, SGRQ : Among them, SGRQ i is the total SGRQ score at follow-up time i, MCID SGRQ is the MCID of the total SGRQ score; its meaning is: when other variables remain unchanged, the MCID of the total SGRQ score increases or decreases by a certain number of units due to a change in a certain factor; when UVD is in the model SGRQ When the absolute value of the change is greater than 1, it indicates that the change in the total SGRQ score may have clinical significance; In the 3) prognostic analysis module, the prognostic analysis of adverse outcomes in COPD patients includes the following steps: Statistical description: The quantitative data of the research objects are analyzed using or median(P 25 ,P 75 ) were used to describe the enumeration data, and the composition ratio n (%) was used to describe the enumeration data; Prognostic analysis: Adverse outcomes were defined as acute exacerbation, outpatient visit, rehospitalization, and death. A stepwise screening strategy was used with an inclusion test level of 0.05 and an exclusion test level of 0.10 to screen out variables including length of hospital stay, treatment for the current episode, hematocrit, urine specific gravity, and ambroxol. Age, sex, BMI, and smoking index were then included. Univariate and multivariate analyses were performed using the Cox proportional hazard model to identify prognostic factors. The prognostic analysis of adverse outcomes in COPD patients further includes: 1) After adjusting adverse outcomes to include acute exacerbation, rehospitalization, and death, a Cox proportional hazard model was constructed based on the same method as above to conduct univariate and multivariate analyses to confirm whether prognostic factors changed after adjustment for adverse outcomes; 2) Adjust the screening strategy. The stepwise screening strategy was changed to include the test level at 0.05 and exclude the test level at 0.15, and to include the test level at 0.10 and exclude the test level at 0.15, respectively. The effects of different screening strategies on the original adverse outcomes and the adjusted adverse outcome influencing factors were analyzed. When the adverse outcomes were acute exacerbation, outpatient visit, rehospitalization, and death, the basic prognostic factors were smoking index, length of hospital stay, and urine specific gravity; when the adverse outcomes were acute exacerbation, rehospitalization, and death, the main prognostic factors were length of hospital stay, SGRQ total score, and NAC; In the 3) prognostic analysis module, the analysis of factors affecting quality of life includes the following steps: 1) The dependent variable is set as the unit change value UVD of the SGRQ total score SGRQ ; 2) Independent variables include patient basic information, test results, and treatment measures; 3) Constructing the generalized estimation model (GEE) to analyze the factors affecting quality of life; 4) During the variable screening process, the model with the smallest QIC and QICC was selected as the optimal model; the factors affecting quality of life were determined based on the model; The factors affecting quality of life are age, smoking index, mMRC score and follow-up duration.

2. The model according to claim 1, characterized in that In the 1) data collection module, the acute exacerbation of COPD patients is a patient whose respiratory symptoms are acutely worsened, resulting in the need for additional treatment; the patient has at least two or more symptoms of cough, increased sputum volume, purulent sputum, dyspnea or wheezing, including exacerbation of existing symptoms and new symptoms, and lasts for at least 48 hours, exceeds daily variation, and requires additional treatment.

3. The model according to claim 1, characterized in that In the 3) prognostic analysis module, the variable assignments of the baseline data questionnaire are as follows:

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