A Prediction Model for Progression of preserved ratio impaired spirometry (PRISm) Based on Longitudinal Pulmonary Function Trajectories

A Prediction Model for Progression of preserved ratio impaired spirometry (PRISm) Based on Longitudinal Pulmonary Function Trajectories | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article A Prediction Model for Progression of preserved ratio impaired spirometry (PRISm) Based on Longitudinal Pulmonary Function Trajectories Weijing Shen, Xinlan Li, Weiguo Xu This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8716601/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 9 You are reading this latest preprint version Abstract Background Preserved ratio impaired spirometry (PRISm) represents a significant clinical phenotype. However, its lung function evolution exhibits marked heterogeneity, leading to substantial variability in the risk of progression to chronic obstructive pulmonary disease (COPD). This study aimed to analyze the longitudinal trajectories of lung function in patients with PRISm, identify independent risk factors for lung function decline, and develop and validate a clinical model for individualized risk prediction. Methods A total of 100 patients were followed prospectively for 3 years. Logistic regression was employed to analyze factors influencing the longitudinal lung function trajectories and to establish a corresponding clinical risk prediction model. Results Sex, smoking status, a COPD Assessment Test (CAT) score ≥ 20, and metabolic syndrome were identified as influencing factors for lung function decline in patients with PRISm. The area under the ROC curve for the risk prediction model was 0.84. Decision curve analysis demonstrated that intervention based on the nomogram model yielded superior net benefit within a threshold probability range of 0.04 to 0.93. Conclusion The risk prediction model, constructed based on influencing factors for lung function decline in a PRISm population, can identify high-risk PRISm patients and provide a reference for preventing their progression to COPD. PRISm longitudinal lung function trajectories influencing factors prediction model Figures Figure 1 Figure 2 Introduction Preserved ratio impaired spirometry (PRISm) is a significant abnormal phenotype of lung function characterized by a preserved FEV1/FVC ratio (FEV1/FVC ≥ 0.7) alongside a reduced FEV1% below the lower limit of normal (< 80%) [ 1 ]. PRISm is now recognized as an independent risk factor for progression of COPD [ 2 ]. PRISm exhibits a heterogeneous trajectory in lung function. Longitudinal studies have shown that over a 5-year period, up to 50% of individuals with PRISm experience significant lung function decline, progressing to COPD with heightened symptom burden and exacerbation risk. Meanwhile, about 15% revert to normal spirometry, and the remainder remain stable over time [ 3 , 4 ]. Multiple studies have also associated the progression from PRISm to COPD with increased respiratory symptoms, cardiovascular diseases, and higher mortality [ 5 – 8 ]. Therefore, early identification of high-risk PRISm patients and timely clinical intervention may represent a promising strategy for COPD prevention. To enable early and stratified management, this study seeks to characterize the 3-year longitudinal lung function trajectories in patients with PRISm, identify independent risk factors closely associated with lung function decline, and ultimately develop and internally validate a personalized risk prediction model to provide evidence-based support for early intervention. Methods Participants Participants were recruited from adults aged 18 or older who were diagnosed with PRISm based on pulmonary function tests performed at Mianyang Central Hospital, Sichuan Province between January 2022 and December 2022. Eligible individuals meeting the study objectives and predefined criteria were subsequently invited to undergo follow-up spirometry and complete relevant questionnaires. Inclusion criteria: (1) age ≥ 18 years; (2) voluntary participation with the ability to cooperate fully to complete spirometry yielding reliable and reproducible results; (3) provision of complete and valid questionnaires without missing or unclear items. Exclusion criteria: (1) inability to perform or complete spirometry testing for any reason; (2) severe systemic disease with a life expectancy of < 1 year (e.g., advanced malignancy, end-stage heart or renal failure); (3) presence of severe chest wall deformity or history of lung resection surgery; (4) diagnosis of psychiatric or cognitive disorders; (5) incomplete or missing baseline registration information. Data Collection Data were collected through standardized questionnaires(see the questionnaire in the Supplementary Material) and spirometry. Information gathered included: Demographics: sex, age, body mass index (BMI), education level, place of residence, family history of respiratory diseases, and history of respiratory diseases in childhood. Lifestyle factors: occupational exposure to dust or biomass fuel, smoking habits (including pack-year history, current smoking status, years since quitting for former smokers), exposure to secondhand smoke, alcohol consumption, cooking habits, and routine physical exercise. Symptoms and comorbidities: COPD Assessment Test (CAT) score, modified Medical Research Council (mMRC) dyspnea score, Clinical COPD Questionnaire (CCQ) score, and physician-diagnosed conditions including asthma, pulmonary tuberculosis, obstructive sleep apnea, cardiovascular diseases, diabetes or impaired glucose tolerance, hyperlipidemia, metabolic syndrome, immune system disorders, and anxiety/depression. Spirometric parameters: Forced expiratory volume in one second (FEV1), percentage of predicted FEV1 (FEV1%), forced vital capacity (FVC), percentage of predicted FVC (FVC%), FEV1/FVC ratio, forced expiratory flow at 50% of FVC (FEF50), percentage of predicted FEF50 (FEF50%), forced expiratory flow at 75% of FVC (FEF75), percentage of predicted FEF75 (FEF75%), and forced expiratory flow between 25% and 75% of FVC (FEF25-75) along with its percentage of predicted value (FEF25-75%). Based on changes in FEV1% between baseline and follow-up spirometry, the 100 enrolled patients were categorized into groups with an upward trend, a downward trend, or a stable trend in lung function. Model Development and Validation We first performed univariate analysis on all collected variables. Variables found to be significantly associated with the outcome (P < 0.05) in this initial screening were then selected as candidates for inclusion in a multivariable regression model to develop the final risk prediction model. Subsequently, a nomogram was constructed based on the regression coefficients of the final model to provide a practical tool for visualizing individual risk. Finally, the model's performance was systematically evaluated. Discrimination (the model's ability to differentiate between outcomes) was assessed using the receiver operating characteristic curve(ROC). Calibration (the agreement between predicted probabilities and observed outcomes) was evaluated with a bias-corrected calibration curve. Clinical usefulness was examined through decision curve analysis (DCA). Statistical analysis Data processing and analysis were performed using Excel 2024, SPSS version 27.0, and R version 4.4.1. Normally distributed continuous variables are presented as mean ± standard deviation (x̅ ± s), and comparisons between groups were made using the independent samples t-test. Non-normally distributed continuous variables are expressed as median (interquartile range) [M (P25, P75)], and group comparisons were conducted using the Mann-Whitney U test. Categorical variables are presented as frequency (percentage) [n (%)], and group comparisons were performed using the Chi-square test or Fisher's exact test, as appropriate. To initially identify potential factors associated with lung function decline in PRISm patients, univariate analysis was first conducted. Variables with a significance level of P < 0.05 in the univariate analysis were subsequently entered into a multivariable logistic regression model. Variable selection for the final model was performed using the forward stepwise selection method (likelihood ratio). A two-sided P-value of less than 0.05 was considered statistically significant for all analyses. To address the potential overfitting due to the limited sample size, internal validation of the final prediction model was performed using bootstrap resampling (1000 repetitions). Results Baseline Characteristics Of the 100 patients, 52 (52.0%) were in the lung function improvement group, and the remaining 48 (48.0%) were in the decline group (Table 1 ). The comparison between the improvement and decline groups revealed statistically significant differences (P < 0.05 for all) in the following variables: sex, age, FEV1, FEV1% predicted, FVC, FEF75, FEF25–75, CAT score, mMRC score, education level, family history of respiratory diseases, smoking habits, secondhand smoke exposure, metabolic syndrome, cardiovascular diseases, and history of dust or biomass fuel exposure. Table 1 Baseline Characteristics of PRISm Patients Stratified by Lung Function Trajectory Variables Value or n (%) Statistical analysis Lung Function Improvement Group (n = 52) Lung Function Decline Group (n = 48) Test Statistic (Z/t/χ²) P-value Age (years) 52.43 ± 16.81 60.94 ± 12.67 -2.876 (2) 0.005 BMI(kg/m²) 23.80(21.03,26.58) 24.40(21.40,26.30) -0.435 (1) 0.663 FEV1(L) 1.83(1.59,2.20) 1.42(1.13,1.92) -3.191 (1) 0.001 FEV1% predicted(%) 73.42 ± 11.09 69.39 ± 8.54 -2.051 (2) 0.043 FVC(L) 2.27(1.95,2.77) 1.96(1.47,2.38) -2.891 (1) 0.004 FVC% predicted(%) 69.76 ± 13.34 72.46 ± 9.69 -1.143 (2) 0.256 FEV1/FVC ratio(%) 78.68(74.27,84.77) 78.81(72.19,87.59) -1.019 (1) 0.308 FEF50(L/s) 2.32(1.88,2.92) 1.80(1.15,2.57) -2.372 (1) 0.018 FEF50% predicted(%) 58.60(47.90,69.00) 47.00(34.00,85.00) -1.292 (1) 0.197 FEF75(L/s) 4.90(3.99,6.01) 4.21(3.07,5.50) -2.172 (1) 0.030 FEF75% predicted(%) 85.00(69.05,109.80) 82.2(65.00,100.00) -0.646 (1) 0.518 FEF25-75(L/s) 1.92 ± 0.88 1.50 ± 0.76 2.567 (2) 0.012 FEF25-75% predicted(%) 64.48 ± 29.06 76.23 ± 43.21 -1.611 (2) 0.110 Sex Male 24(45.30%) 36(76.60%) 10.177a (3) 0.001 Female 29(54.70%) 11(23.40%) Education Level Junior high school or below 16(30.20%) 22(46.80%) 4.304a (3) 0.116 Junior high school to technical secondary school 15(28.30%) 14(29.80%) College or above 22(41.50%) 11(23.40%) Residence Rural 16(30.20%) 16(34.00%) 0.170a (3) 0.680 Urban 37(69.80%) 31(66.00%) Family History of Respiratory Disease No 44(83.00%) 28(59.60%) 6.791a (3) 0.009 Yes 9(17.00%) 19(40.40%) Childhood Respiratory Disease History No 45(84.90%) 41(87.20%) 0.112a (3) 0.738 Yes 8(15.10%) 6(12.80%) Occupational Dust/Biomass Fuel Exposure No 48(90.60%) 30(63.80%) 10.377a (3) 0.001 Yes 5(9.40%) 17(36.20%) Smoking Habit No 46(86.8%) 27(57.40%) 10.884a (3) <0.001 Yes 7(13.2%) 20(42.60%) Smoking Pack-Years Never 46(86.80%) 27(57.40%) 24.172a (3) <0.001 20 3(5.70%) 5(10.60%) Current Smoking Status (Among Ever Smokers) Current Smoker 0(0.0%) 9(45.00%) 4.725a (3) 0.059 Former Smoker 7(100.00%) 11(55.00%) Years Since Quitting (Among Former Smokers) <5 years 1(14.30%) 1(9.10%) 0.117a (3) 1.000 ≥ 5 years 6(85.70%) 10(90.90%) Secondhand Smoke Exposure No 37(69.80%) 21(44.70%) 6.458a (3) 0.011 Yes 16(30.20%) 26(55.30%) Alcohol Consumption No 44(83.00%) 40(85.10%) 0.081a (3) 0.776 Yes 9(17.00%) 7(14.90%) Regular Cooking No 8(15.10%) 2(4.30%) 3.252a (3) 0.098 Yes 45(84.90%) 45(95.70%) Regular Exercise No 20(37.70%) 16(34.00%) 0.147a (3) 0.701 Yes 33(62.30%) 31(66.00%) CAT Score <20 29(54.70%) 7(14.90%) 17.146a (3) <0.001 ≥ 20 24(45.30%) 40(85.10%) mMRC Score <2 47(88.70%) 33(70.20%) 5.309a (3) 0.021 ≥ 2 6(11.03%) 14(29.80%) CCQ Score <20 43(81.10%) 42(89.40%) 1.323a (3) 0.250 ≥ 20 10(18.90%) 5(10.60%) Asthma No 47(88.70%) 35(74.50%) 3.408a (3) 0.065 Yes 6(11.30%) 12(25.50%) Pulmonary Tuberculosis No 48(90.60%) 45(95.70%) 1.026a (3) 0.442 Yes 5(9.40%) 2(4.30%) Obstructive Sleep Apnea No 51(96.20%) 45(95.70%) 0.015a (3) 1.000 Yes 2(3.80%) 2(4.30%) Cardiovascular Disease No 32(60.40%) 40(85.10%) 7.556a (3) 0.006 Yes 21(39.60%) 7(14.90%) Diabetes/Impaired Glucose Tolerance No 33(62.30%) 37(78.70%) 3.213a (3) 0.073 Yes 20(37.70%) 10(21.30%) Hyperlipidemia No 28(52.80%) 28(59.60%) 0.460a (3) 0.498 Yes 25(47.20%) 19(40.40%) Metabolic Syndrome No 36(67.90%) 15(31.90%) 12.925a (3) <0.001 Yes 17(32.10%) 32(68.10%) Immune System Disorder No 23(43.40%) 29(61.70%) 3.344a (3) 0.067 Yes 30(56.60%) 18(38.30%) Anxiety/Depression No 39(73.60%) 34(72.30%) 0.020a (3) 0.889 Yes 14(26.40%) 13(27.70%) Notes: Continuous variables are presented as Mean ± SD or Median (IQR). Categorical variables are presented as n (%). (1) Z-value from Mann-Whitney U test. (2) t-value from independent samples t-test. (3) χ² value from Pearson's chi-square test. Abbreviations: BMI, body mass index; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; FEF, forced expiratory flow; CAT, COPD Assessment Test; mMRC, modified Medical Research Council; CCQ, Clinical COPD Questionnaire. Multivariable Binary Logistic Regression Analysis Lung function trajectory (improvement = 0, decline = 1) was set as the dependent variable. Given the limited sample size of this study, to develop a robust predictive model and avoid overfitting, we followed general guidelines for predictor selection in model building. Based on the common recommendation of requiring 10–15 outcome events per candidate predictor in multivariable logistic regression (with 48 decline events in this study), and building upon univariate analysis (P < 0.05), we incorporated clinical relevance and prior literature to finalize the model. Consequently, four key categorical variables were selected for the final multivariable model: sex (female = 0, male = 1), smoking habit (no = 0, yes = 1), CAT score (< 20 = 0, ≥ 20 = 1), and metabolic syndrome (no = 0, yes = 1). Variables were selected using the forward stepwise (likelihood ratio) method to identify independent risk factors. The results are presented in Table 2 . Sex, smoking habit, CAT score, and metabolic syndrome were all identified as significant factors associated with lung function decline in the PRISm population (P 1). Male PRISm patients had a 6.32-fold higher risk of lung function decline compared to females (P = 0.002). Furthermore, compared to their counterparts, PRISm individuals with a smoking habit, a CAT score ≥ 20, or metabolic syndrome had a 4.505-fold (P = 0.029), 4.412-fold (P = 0.007), and 3.836-fold (P = 0.013) higher risk of lung function decline, respectively. Table 2 Results of the Binary Logistic Regression Analysis for Lung Function Trajectories in PRISm Patients Variable β (Coefficient) S.E Wald X² P-value Odds Ratio (95% CI) Sex (Male) 1.844 0.601 9.397 0.002 6.320 (1.944 ~ 20.54) Smoking Habit 1.505 0.688 4.782 0.029 4.505 (1.169 ~ 17.364) CAT Score (≥ 20) 1.484 0.550 7.289 0.007 4.412 (1.502 ~ 12.959) Metabolic Syndrome 1.345 0.541 6.179 0.013 3.836(1.329 ~ 11.075) Abbreviation: CI, confidence interval; S.E., standard error. Development of the Prediction Model Based on the independent risk factors identified by the binary logistic regression analysis, a nomogram was constructed to predict the risk of lung function decline in individuals with PRISm (Fig. 1 ). In this nomogram, sex contributed the highest weight to the total risk score, with male sex (coded as 1) assigned 100 points. This was followed by smoking habit and CAT score, which carried similar weights; a positive smoking history and a CAT score ≥ 20 were each assigned approximately 82 points. Metabolic syndrome had the smallest individual weight, contributing about 74 points when present. According to the nomogram, a total score of approximately 180 points corresponds to a 0.5 (or 50%) predicted probability of lung function decline. To use the nomogram, locate the patient's value for each predictor on the corresponding axis (e.g., Sex). Draw a vertical line upward to the ‘Points’ scale at the top to determine the score for that predictor. Sum the scores for all four predictors to obtain the Total Points. Finally, project the Total Points vertically downward to the bottom ‘Risk’ axis to read the individualized predicted probability of lung function decline. The nomogram is based on a multivariable logistic regression model and incorporates the following predictors: Sex (Male vs. Female), Smoking Habit (Yes vs. No), CAT Score (≥ 20 vs. <20), and Metabolic Syndrome (Yes vs. No). The Points scale assigns a weight to each predictor level. The aggregated Total Points correspond to the final Risk of lung function decline. Evaluation of the Prediction Model Figure 2A presents the receiver operating characteristic (ROC) curve of the nomogram for predicting lung function decline in PRISm patients. The area under the curve (AUC) was 0.84, indicating good discriminatory ability of the model. The model was further subjected to internal validation using the bootstrap resampling method. The internally validated concordance index (C-index) was 0.84 ( Figure 2B ). The calibration curve demonstrated close agreement with the ideal line, suggesting good model calibration. Decision curve analysis ( Figure 2C ) showed that, across a threshold probability range of 0.04 to 0.93, using this nomogram to guide clinical decisions provided a higher net benefit compared to the strategies of "intervention for all" or "intervention for none". Discussion Our study identified male sex as a risk factor for lung function decline in PRISm, which aligns with findings from previous research [ 11 , 12 ]. This association may be partly explained by behavioral and biological factors. Behaviorally, males generally have a higher cumulative smoking exposure. Biologically, their larger baseline airway diameter may mask early small airway dysfunction, delaying diagnosis. Furthermore, disease progression in males may also reflect a propensity for more pronounced systemic inflammation and an age-related decline in protective hormonal levels. [ 13 – 16 ]. Smoking is a well-established independent risk factor for COPD [ 17 , 18 ]. Our findings further indicate that a smoking habit significantly increases the risk of lung function decline in PRISm patients. Chronic smoking is known to cause injury, inflammation, and tissue remodeling, ultimately leading to airway dysfunction, impaired alveolar ventilation, and airflow limitation [ 19 ]. Consequently, PRISm patients who smoke, particularly long-term smokers, face a heightened risk of progressing to COPD. For these individuals, regular spirometry assessment and monitoring are crucial. This practice facilitates the timely detection of early COPD signs, thereby enabling prompt intervention. While other studies have reported that smoking cessation can significantly improve key lung function parameters such as FEV1% and FEV1/FVC [ 20 ], our analysis did not find a statistically significant association between smoking cessation and a reduced risk of lung function deterioration in PRISm patients. We postulate that this lack of association is likely due to the limited sample size of former smokers in our cohort. Additionally, our definition of "smoking cessation" did not account for its duration or the intensity of prior smoking, which may have led to potential exposure misclassification. Therefore, our findings do not negate the value of smoking cessation in the management of PRISm. Although quitting smoking may not fully reverse small airway damage caused by long-term tobacco use, it can effectively slow the progression of further structural injury and functional decline. Early cessation remains a crucial intervention for PRISm patients. Our study also found that PRISm individuals with a CAT score ≥ 20 were more prone to lung function deterioration. This is likely because respiratory symptoms (e.g., cough, dyspnea) serve as clinical manifestations of underlying persistent airway inflammation, mucus hypersecretion, or early-stage lung remodeling. These ongoing pathological processes can directly impair lung function, increase the risk of exacerbations, and thereby drive the progression toward fixed airflow limitation [ 21 ]. However, applying the GOLD symptom threshold (CAT ≥ 10), widely used for stable COPD assessment, showed only a borderline association trend (P = 0.058) in our cohort. This may be attributed to our relatively limited sample size, which provided insufficient statistical power to detect a potentially smaller effect size within this stratification. The utility of the CAT score is well-established in the management of chronic bronchitis and COPD, where it serves as a standard tool for guiding treatment decisions and evaluating prognosis [ 1 ]. In light of our results, integrating this validated tool into the long-term management strategy for PRISm patients appears both rational and necessary. Our study also identified a significant association between metabolic syndrome and lung function decline in PRISm patients. This link may be attributed to the chronic low-grade inflammatory state, disruption of the endocrine-immune axis, as well as insulin resistance and oxidative stress commonly present in metabolic syndrome. Current evidence-based research acknowledges metabolic syndrome as a key driver of poor prognosis in PRISm [ 22 – 25 ]. Nevertheless, further prospective studies specifically targeting the longitudinal transition of PRISm are warranted to clarify whether it constitutes an independent causal risk factor. It is noteworthy that univariate analysis indicated that older age, lower baseline levels of FEV1% and FEF25-75, a family history of respiratory diseases, and exposure to dust or biomass fuel were also associated with the outcome. These factors have been previously reported in the literature to be related to PRISm prognosis [ 11 , 21 , 26 , 27 ]. Although they were not included in the multivariable analysis in this study due to constraints on the number of variables in the final model, they may still serve as potential risk markers. They warrant focused attention and validation in future studies with larger sample sizes. Our study first conducted univariate analysis to preliminarily screen known risk factors for PRISm and potential predictors suggested by the literature. Subsequently, the significant variables identified from this screening were incorporated into a multivariable regression model for final model development. This analytical strategy was designed to optimize model performance while effectively controlling for bias introduced by potential confounding factors. However, this study has several limitations. Due to the relatively small sample size and limited number of outcome events, we explored a preliminary risk prediction model for PRISm progression. Consequently, not all variables showing significance in the univariate analysis could be included in the final model. Additionally, some data may be subject to recall bias. Future research should involve large-scale, prospective studies. Furthermore, this analysis did not incorporate routine biochemical markers or chest imaging characteristics, which will be important directions for subsequent investigations. In summary, the logistic regression-based prediction model for PRISm progression risk has demonstrated favorable predictive performance, accuracy, and practical utility. It serves as a practical tool, potentially assisting primary healthcare workers or individuals in estimating PRISm risk using the provided nomogram. This offers a scientific basis for the early identification and intervention of high-risk PRISm patients. Furthermore, the model can inform the development of targeted prevention and management strategies, thereby contributing more effectively to the protection of public health. Declarations Ethics approval and consent to participate This study was conducted in accordance with the principles of the Declaration of Helsinki.This study was reviewed and approved by the Biomedical Ethics Committee of Mianyang Central Hospital (Approval No.: S20240243-01). All participants provided written informed consent prior to enrollment, audio recordings, and abstraction of clinical data from the electronic medical record. Consent for publication As above, covered by Mianyang Central Hospital. Availability of data and materials The datasets used and analyzed during the current study are available from the corresponding author upon reasonable request. Competing interests The authors declare that they have no competing interests. Funding This work was supported by the China International Medical Exchange Foundation(Z-2017-24-2301). Authors' contributions Weiguo Xu contributed to the critical revision of the manuscript. The conception and design of the study, acquisition of funding, and project supervision were carried out by Xinlan Li. Weijing Shen was responsible for data collection, analysis, and drafting the initial manuscript. All authors read and approved the final version of the manuscript for publication. Acknowledgements The authors gratefully acknowledge the financial support from the China International Medical Exchange Foundation. We also extend our thanks to the Pulmonary Function Laboratory of Mianyang Central Hospital for their technical support throughout this study. References Global Initiative for Chronic Obstructive Lung Disease (GOLD). Global Strategy for the Diagnosis, Management, and Prevention of Chronic Obstructive Pulmonary Disease (2024 Report). Kanetake R, Takamatsu K, Park K et al. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8716601","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":589777497,"identity":"1607d284-0678-49e1-b0ff-5b98eaa28059","order_by":0,"name":"Weijing Shen","email":"","orcid":"","institution":"North Sichuan Medical University","correspondingAuthor":false,"prefix":"","firstName":"Weijing","middleName":"","lastName":"Shen","suffix":""},{"id":589777499,"identity":"61a5f325-87a6-4d69-b6d7-9e3e41ff0972","order_by":1,"name":"Xinlan Li","email":"","orcid":"","institution":"Mianyang Central Hospital","correspondingAuthor":false,"prefix":"","firstName":"Xinlan","middleName":"","lastName":"Li","suffix":""},{"id":589777502,"identity":"aa8e26aa-93bc-4091-8d08-db5f236e454e","order_by":2,"name":"Weiguo Xu","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA2UlEQVRIiWNgGAWjYHAC9g8fKmzs2NgbGx98IFILG+OMM2nJ/DyHmw1nEKuFmbftEOPMGelt0hzEqDe4kX7tAQ/bAWaDmw8bpBkY7OR0GwhokZyRU24gwXOHz+B2YoNxAUOysdkBAlr4pXMSJAwknjGDtCTPYDiQuI2QFjaQlgSDw4wbbh5sOMxDjBZ+6fRjEgcSDgO9z9jYTJQWyflvmA0bDoACObGZcYYBEX4xOHP84eO//0BRefz5jw8VdnIEtTAw8Bggm0BQOQiwPyBK2SgYBaNgFIxgAABFx0iP/qoPEAAAAABJRU5ErkJggg==","orcid":"","institution":"Mianyang Central Hospital","correspondingAuthor":true,"prefix":"","firstName":"Weiguo","middleName":"","lastName":"Xu","suffix":""}],"badges":[],"createdAt":"2026-01-28 05:24:02","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-8716601/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8716601/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102837994,"identity":"f4518fe8-b6fc-408f-b815-02e34971df01","added_by":"auto","created_at":"2026-02-17 11:27:00","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":191661,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eNomogram for Predicting the Risk of Lung Function Decline in Patients with PRISm.\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"floatimage1.png","url":"https://assets-eu.researchsquare.com/files/rs-8716601/v1/2985f3b73fe05dc47e895f30.png"},{"id":102837991,"identity":"35f7acc8-fd83-42b1-87ff-c240330ad1f2","added_by":"auto","created_at":"2026-02-17 11:26:59","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":82304,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) Receiver operating characteristic (ROC) curve.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(B) Calibration curve.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003e(C) Decision curve analysis (DCA).\u003c/strong\u003e\u003c/p\u003e","description":"","filename":"2.png","url":"https://assets-eu.researchsquare.com/files/rs-8716601/v1/f68e84e724379961b89f534f.png"},{"id":102963336,"identity":"e63c47b7-ddd1-433a-8757-1d0e475ea9f0","added_by":"auto","created_at":"2026-02-19 04:15:41","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":1320007,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8716601/v1/5ac52b58-5df8-428b-a22a-a5dc60ac2bdc.pdf"},{"id":102837993,"identity":"a26b6208-5575-4ca1-b874-91c55533f1a3","added_by":"auto","created_at":"2026-02-17 11:26:59","extension":"docx","order_by":0,"title":"","display":"","copyAsset":false,"role":"supplement","size":118155,"visible":true,"origin":"","legend":"","description":"","filename":"questionnaire.docx","url":"https://assets-eu.researchsquare.com/files/rs-8716601/v1/f132782e17848118ac7f093a.docx"}],"financialInterests":"No competing interests reported.","formattedTitle":"A Prediction Model for Progression of preserved ratio impaired spirometry (PRISm) Based on Longitudinal Pulmonary Function Trajectories","fulltext":[{"header":"Introduction","content":"\u003cp\u003ePreserved ratio impaired spirometry (PRISm) is a significant abnormal phenotype of lung function characterized by a preserved FEV1/FVC ratio (FEV1/FVC\u0026thinsp;\u0026ge;\u0026thinsp;0.7) alongside a reduced FEV1% below the lower limit of normal (\u0026lt;\u0026thinsp;80%) [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. PRISm is now recognized as an independent risk factor for progression of COPD [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e]. PRISm exhibits a heterogeneous trajectory in lung function. Longitudinal studies have shown that over a 5-year period, up to 50% of individuals with PRISm experience significant lung function decline, progressing to COPD with heightened symptom burden and exacerbation risk. Meanwhile, about 15% revert to normal spirometry, and the remainder remain stable over time [\u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e, \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Multiple studies have also associated the progression from PRISm to COPD with increased respiratory symptoms, cardiovascular diseases, and higher mortality [\u003cspan additionalcitationids=\"CR6 CR7\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. Therefore, early identification of high-risk PRISm patients and timely clinical intervention may represent a promising strategy for COPD prevention. To enable early and stratified management, this study seeks to characterize the 3-year longitudinal lung function trajectories in patients with PRISm, identify independent risk factors closely associated with lung function decline, and ultimately develop and internally validate a personalized risk prediction model to provide evidence-based support for early intervention.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eParticipants\u003c/h2\u003e \u003cp\u003eParticipants were recruited from adults aged 18 or older who were diagnosed with PRISm based on pulmonary function tests performed at Mianyang Central Hospital, Sichuan Province between January 2022 and December 2022. Eligible individuals meeting the study objectives and predefined criteria were subsequently invited to undergo follow-up spirometry and complete relevant questionnaires.\u003c/p\u003e \u003cp\u003eInclusion criteria: (1) age\u0026thinsp;\u0026ge;\u0026thinsp;18 years; (2) voluntary participation with the ability to cooperate fully to complete spirometry yielding reliable and reproducible results; (3) provision of complete and valid questionnaires without missing or unclear items.\u003c/p\u003e \u003cp\u003eExclusion criteria: (1) inability to perform or complete spirometry testing for any reason; (2) severe systemic disease with a life expectancy of \u0026lt;\u0026thinsp;1 year (e.g., advanced malignancy, end-stage heart or renal failure); (3) presence of severe chest wall deformity or history of lung resection surgery; (4) diagnosis of psychiatric or cognitive disorders; (5) incomplete or missing baseline registration information.\u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eData Collection\u003c/h3\u003e\n\u003cp\u003eData were collected through standardized questionnaires(see the questionnaire in the Supplementary Material) and spirometry. Information gathered included: Demographics: sex, age, body mass index (BMI), education level, place of residence, family history of respiratory diseases, and history of respiratory diseases in childhood. Lifestyle factors: occupational exposure to dust or biomass fuel, smoking habits (including pack-year history, current smoking status, years since quitting for former smokers), exposure to secondhand smoke, alcohol consumption, cooking habits, and routine physical exercise. Symptoms and comorbidities: COPD Assessment Test (CAT) score, modified Medical Research Council (mMRC) dyspnea score, Clinical COPD Questionnaire (CCQ) score, and physician-diagnosed conditions including asthma, pulmonary tuberculosis, obstructive sleep apnea, cardiovascular diseases, diabetes or impaired glucose tolerance, hyperlipidemia, metabolic syndrome, immune system disorders, and anxiety/depression. Spirometric parameters: Forced expiratory volume in one second (FEV1), percentage of predicted FEV1 (FEV1%), forced vital capacity (FVC), percentage of predicted FVC (FVC%), FEV1/FVC ratio, forced expiratory flow at 50% of FVC (FEF50), percentage of predicted FEF50 (FEF50%), forced expiratory flow at 75% of FVC (FEF75), percentage of predicted FEF75 (FEF75%), and forced expiratory flow between 25% and 75% of FVC (FEF25-75) along with its percentage of predicted value (FEF25-75%). Based on changes in FEV1% between baseline and follow-up spirometry, the 100 enrolled patients were categorized into groups with an upward trend, a downward trend, or a stable trend in lung function.\u003c/p\u003e\n\u003ch3\u003eModel Development and Validation\u003c/h3\u003e\n\u003cp\u003eWe first performed univariate analysis on all collected variables. Variables found to be significantly associated with the outcome (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05) in this initial screening were then selected as candidates for inclusion in a multivariable regression model to develop the final risk prediction model. Subsequently, a nomogram was constructed based on the regression coefficients of the final model to provide a practical tool for visualizing individual risk. Finally, the model's performance was systematically evaluated. Discrimination (the model's ability to differentiate between outcomes) was assessed using the receiver operating characteristic curve(ROC). Calibration (the agreement between predicted probabilities and observed outcomes) was evaluated with a bias-corrected calibration curve. Clinical usefulness was examined through decision curve analysis (DCA).\u003c/p\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cp\u003eData processing and analysis were performed using Excel 2024, SPSS version 27.0, and R version 4.4.1. Normally distributed continuous variables are presented as mean\u0026thinsp;\u0026plusmn;\u0026thinsp;standard deviation (x̅ \u0026plusmn; s), and comparisons between groups were made using the independent samples t-test. Non-normally distributed continuous variables are expressed as median (interquartile range) [M (P25, P75)], and group comparisons were conducted using the Mann-Whitney U test. Categorical variables are presented as frequency (percentage) [n (%)], and group comparisons were performed using the Chi-square test or Fisher's exact test, as appropriate. To initially identify potential factors associated with lung function decline in PRISm patients, univariate analysis was first conducted. Variables with a significance level of P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 in the univariate analysis were subsequently entered into a multivariable logistic regression model. Variable selection for the final model was performed using the forward stepwise selection method (likelihood ratio). A two-sided P-value of less than 0.05 was considered statistically significant for all analyses. To address the potential overfitting due to the limited sample size, internal validation of the final prediction model was performed using bootstrap resampling (1000 repetitions).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eBaseline Characteristics\u003c/h2\u003e \u003cp\u003eOf the 100 patients, 52 (52.0%) were in the lung function improvement group, and the remaining 48 (48.0%) were in the decline group (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). The comparison between the improvement and decline groups revealed statistically significant differences (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05 for all) in the following variables: sex, age, FEV1, FEV1% predicted, FVC, FEF75, FEF25\u0026ndash;75, CAT score, mMRC score, education level, family history of respiratory diseases, smoking habits, secondhand smoke exposure, metabolic syndrome, cardiovascular diseases, and history of dust or biomass fuel exposure.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eBaseline Characteristics of PRISm Patients Stratified by Lung Function Trajectory\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colspan=\"2\" morerows=\"1\" nameend=\"c2\" namest=\"c1\" rowspan=\"2\"\u003e \u003cp\u003eVariables\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c4\" namest=\"c3\"\u003e \u003cp\u003eValue or n (%)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colspan=\"2\" nameend=\"c6\" namest=\"c5\"\u003e \u003cp\u003eStatistical analysis\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLung Function Improvement Group (n\u0026thinsp;=\u0026thinsp;52)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eLung Function Decline Group (n\u0026thinsp;=\u0026thinsp;48)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eTest Statistic (Z/t/χ\u0026sup2;)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eAge (years)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e52.43\u0026thinsp;\u0026plusmn;\u0026thinsp;16.81\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e60.94\u0026thinsp;\u0026plusmn;\u0026thinsp;12.67\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.876\u003csup\u003e(2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.005\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eBMI(kg/m\u0026sup2;)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23.80(21.03,26.58)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e24.40(21.40,26.30)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.435\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.663\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEV1(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.83(1.59,2.20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.42(1.13,1.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-3.191\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEV1% predicted(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e73.42\u0026thinsp;\u0026plusmn;\u0026thinsp;11.09\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e69.39\u0026thinsp;\u0026plusmn;\u0026thinsp;8.54\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.051\u003csup\u003e(2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.043\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFVC(L)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.27(1.95,2.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.96(1.47,2.38)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.891\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.004\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFVC% predicted(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e69.76\u0026thinsp;\u0026plusmn;\u0026thinsp;13.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e72.46\u0026thinsp;\u0026plusmn;\u0026thinsp;9.69\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.143\u003csup\u003e(2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.256\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEV1/FVC ratio(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e78.68(74.27,84.77)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e78.81(72.19,87.59)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.019\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.308\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEF50(L/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2.32(1.88,2.92)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.80(1.15,2.57)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.372\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.018\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEF50% predicted(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e58.60(47.90,69.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e47.00(34.00,85.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.292\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.197\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEF75(L/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4.90(3.99,6.01)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.21(3.07,5.50)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-2.172\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.030\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEF75% predicted(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e85.00(69.05,109.80)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e82.2(65.00,100.00)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.646\u003csup\u003e(1)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.518\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEF25-75(L/s)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1.92\u0026thinsp;\u0026plusmn;\u0026thinsp;0.88\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.50\u0026thinsp;\u0026plusmn;\u0026thinsp;0.76\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e2.567\u003csup\u003e(2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.012\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"2\" nameend=\"c2\" namest=\"c1\"\u003e \u003cp\u003eFEF25-75% predicted(%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e64.48\u0026thinsp;\u0026plusmn;\u0026thinsp;29.06\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e76.23\u0026thinsp;\u0026plusmn;\u0026thinsp;43.21\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-1.611\u003csup\u003e(2)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.110\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSex\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(45.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e36(76.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.177a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFemale\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(54.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(23.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003eEducation Level\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school or below\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(30.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e22(46.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e4.304a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e \u003cp\u003e0.116\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eJunior high school to technical secondary school\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e15(28.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(29.80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCollege or above\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e22(41.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(23.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eResidence\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRural\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(30.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(34.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.170a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.680\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eUrban\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(69.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(66.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eFamily History of Respiratory Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(83.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(59.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.791a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(17.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(40.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eChildhood Respiratory Disease History\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(84.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e41(87.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.112a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.738\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(15.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6(12.80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eOccupational Dust/Biomass Fuel Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(90.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e30(63.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.377a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(9.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e17(36.20%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSmoking Habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(86.8%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(57.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e10.884a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(13.2%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e20(42.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003eSmoking Pack-Years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNever\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e46(86.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e27(57.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e24.172a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e4(7.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e10\u0026ndash;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(31.90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026gt;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e3(5.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(10.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCurrent Smoking Status (Among Ever Smokers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eCurrent Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0(0.0%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9(45.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e4.725a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.059\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFormer Smoker\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e7(100.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11(55.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eYears Since Quitting (Among Former Smokers)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1(14.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1(9.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.117a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;5 years\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(85.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(90.90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eSecondhand Smoke Exposure\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e37(69.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e21(44.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e6.458a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.011\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e16(30.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e26(55.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAlcohol Consumption\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e44(83.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(85.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.081a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.776\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e9(17.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(14.90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegular Cooking\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e8(15.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.252a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.098\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e45(84.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(95.70%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eRegular Exercise\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(37.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e16(34.00%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.147a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.701\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(62.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e31(66.00%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCAT Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e29(54.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(14.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e17.146a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e24(45.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(85.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003emMRC Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(88.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e33(70.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e5.309a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;2\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(11.03%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e14(29.80%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCCQ Score\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026lt;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e43(81.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e42(89.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.323a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.250\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026ge;\u0026thinsp;20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e10(18.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e5(10.60%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAsthma\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e47(88.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e35(74.50%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.408a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.065\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e6(11.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e12(25.50%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003ePulmonary Tuberculosis\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e48(90.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(95.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.026a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.442\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e5(9.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eObstructive Sleep Apnea\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e51(96.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e45(95.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.015a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e1.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e2(3.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2(4.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eCardiovascular Disease\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e32(60.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e40(85.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e7.556a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.006\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21(39.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7(14.90%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eDiabetes/Impaired Glucose Tolerance\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e33(62.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e37(78.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.213a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.073\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e20(37.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e10(21.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eHyperlipidemia\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e28(52.80%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e28(59.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.460a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.498\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e25(47.20%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e19(40.40%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e36(67.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e15(31.90%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e12.925a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e\u0026lt;0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e17(32.10%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e32(68.10%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eImmune System Disorder\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e23(43.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e29(61.70%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e3.344a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.067\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e30(56.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e18(38.30%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003eAnxiety/Depression\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eNo\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e39(73.60%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e34(72.30%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.020a\u003csup\u003e(3)\u003c/sup\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e \u003cp\u003e0.889\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e14(26.40%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e13(27.70%)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colspan=\"6\" nameend=\"c6\" namest=\"c1\"\u003e \u003cp\u003eNotes:\u003c/p\u003e \u003cp\u003eContinuous variables are presented as Mean\u0026thinsp;\u0026plusmn;\u0026thinsp;SD or Median (IQR). Categorical variables are presented as n (%).\u003c/p\u003e \u003cp\u003e\u003csup\u003e(1)\u003c/sup\u003e Z-value from Mann-Whitney U test.\u003c/p\u003e \u003cp\u003e\u003csup\u003e(2)\u003c/sup\u003e t-value from independent samples t-test.\u003c/p\u003e \u003cp\u003e\u003csup\u003e(3)\u003c/sup\u003e χ\u0026sup2; value from Pearson's chi-square test.\u003c/p\u003e \u003cp\u003eAbbreviations: BMI, body mass index; FEV1, forced expiratory volume in 1 second; FVC, forced vital capacity; FEF, forced expiratory flow; CAT, COPD Assessment Test; mMRC, modified Medical Research Council; CCQ, Clinical COPD Questionnaire.\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e\n\u003ch3\u003eMultivariable Binary Logistic Regression Analysis\u003c/h3\u003e\n\u003cp\u003eLung function trajectory (improvement\u0026thinsp;=\u0026thinsp;0, decline\u0026thinsp;=\u0026thinsp;1) was set as the dependent variable. Given the limited sample size of this study, to develop a robust predictive model and avoid overfitting, we followed general guidelines for predictor selection in model building. Based on the common recommendation of requiring 10\u0026ndash;15 outcome events per candidate predictor in multivariable logistic regression (with 48 decline events in this study), and building upon univariate analysis (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05), we incorporated clinical relevance and prior literature to finalize the model. Consequently, four key categorical variables were selected for the final multivariable model: sex (female\u0026thinsp;=\u0026thinsp;0, male\u0026thinsp;=\u0026thinsp;1), smoking habit (no\u0026thinsp;=\u0026thinsp;0, yes\u0026thinsp;=\u0026thinsp;1), CAT score (\u0026lt;\u0026thinsp;20\u0026thinsp;=\u0026thinsp;0, \u0026ge; 20\u0026thinsp;=\u0026thinsp;1), and metabolic syndrome (no\u0026thinsp;=\u0026thinsp;0, yes\u0026thinsp;=\u0026thinsp;1). Variables were selected using the forward stepwise (likelihood ratio) method to identify independent risk factors. The results are presented in Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e. Sex, smoking habit, CAT score, and metabolic syndrome were all identified as significant factors associated with lung function decline in the PRISm population (P\u0026thinsp;\u0026lt;\u0026thinsp;0.05, all OR\u0026thinsp;\u0026gt;\u0026thinsp;1). Male PRISm patients had a 6.32-fold higher risk of lung function decline compared to females (P\u0026thinsp;=\u0026thinsp;0.002). Furthermore, compared to their counterparts, PRISm individuals with a smoking habit, a CAT score\u0026thinsp;\u0026ge;\u0026thinsp;20, or metabolic syndrome had a 4.505-fold (P\u0026thinsp;=\u0026thinsp;0.029), 4.412-fold (P\u0026thinsp;=\u0026thinsp;0.007), and 3.836-fold (P\u0026thinsp;=\u0026thinsp;0.013) higher risk of lung function decline, respectively.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eResults of the Binary Logistic Regression Analysis for Lung Function Trajectories in PRISm Patients\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eβ (Coefficient)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eS.E\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald X\u0026sup2;\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eP-value\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eOdds Ratio (95% CI)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSex (Male)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.844\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.601\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e9.397\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.002\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e6.320 (1.944\u0026thinsp;~\u0026thinsp;20.54)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSmoking Habit\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.505\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.688\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.782\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.029\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.505 (1.169\u0026thinsp;~\u0026thinsp;17.364)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCAT Score (\u0026ge;\u0026thinsp;20)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.484\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.550\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.289\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.007\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e4.412 (1.502\u0026thinsp;~\u0026thinsp;12.959)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMetabolic Syndrome\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.345\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.541\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e6.179\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.013\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.836(1.329\u0026thinsp;~\u0026thinsp;11.075)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eAbbreviation: CI, confidence interval; S.E., standard error.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e\n\u003ch3\u003eDevelopment of the Prediction Model\u003c/h3\u003e\n\u003cp\u003eBased on the independent risk factors identified by the binary logistic regression analysis, a nomogram was constructed to predict the risk of lung function decline in individuals with PRISm (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). In this nomogram, sex contributed the highest weight to the total risk score, with male sex (coded as 1) assigned 100 points. This was followed by smoking habit and CAT score, which carried similar weights; a positive smoking history and a CAT score\u0026thinsp;\u0026ge;\u0026thinsp;20 were each assigned approximately 82 points. Metabolic syndrome had the smallest individual weight, contributing about 74 points when present. According to the nomogram, a total score of approximately 180 points corresponds to a 0.5 (or 50%) predicted probability of lung function decline.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eTo use the nomogram, locate the patient's value for each predictor on the corresponding axis (e.g., Sex). Draw a vertical line upward to the \u0026lsquo;Points\u0026rsquo; scale at the top to determine the score for that predictor. Sum the scores for all four predictors to obtain the Total Points. Finally, project the Total Points vertically downward to the bottom \u0026lsquo;Risk\u0026rsquo; axis to read the individualized predicted probability of lung function decline. The nomogram is based on a multivariable logistic regression model and incorporates the following predictors: Sex (Male vs. Female), Smoking Habit (Yes vs. No), CAT Score (\u0026ge;\u0026thinsp;20 vs. \u0026lt;20), and Metabolic Syndrome (Yes vs. No). The Points scale assigns a weight to each predictor level. The aggregated Total Points correspond to the final Risk of lung function decline.\u003c/p\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eEvaluation of the Prediction Model\u003c/h2\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003e\u003cstrong\u003eFigure 2A\u003c/strong\u003e presents the receiver operating characteristic (ROC) curve of the nomogram for predicting lung function decline in PRISm patients. The area under the curve (AUC) was 0.84, indicating good discriminatory ability of the model. The model was further subjected to internal validation using the bootstrap resampling method. The internally validated concordance index (C-index) was 0.84 (\u003cstrong\u003eFigure 2B\u003c/strong\u003e). The calibration curve demonstrated close agreement with the ideal line, suggesting good model calibration. Decision curve analysis (\u003cstrong\u003eFigure 2C\u003c/strong\u003e) showed that, across a threshold probability range of 0.04 to 0.93, using this nomogram to guide clinical decisions provided a higher net benefit compared to the strategies of \u0026quot;intervention for all\u0026quot; or \u0026quot;intervention for none\u0026quot;.\u0026nbsp;\u003c/p\u003e\u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eOur study identified male sex as a risk factor for lung function decline in PRISm, which aligns with findings from previous research [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e12\u003c/span\u003e]. This association may be partly explained by behavioral and biological factors. Behaviorally, males generally have a higher cumulative smoking exposure. Biologically, their larger baseline airway diameter may mask early small airway dysfunction, delaying diagnosis. Furthermore, disease progression in males may also reflect a propensity for more pronounced systemic inflammation and an age-related decline in protective hormonal levels. [\u003cspan additionalcitationids=\"CR14 CR15\" citationid=\"CR13\" class=\"CitationRef\"\u003e13\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e]. Smoking is a well-established independent risk factor for COPD [\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e17\u003c/span\u003e, \u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. Our findings further indicate that a smoking habit significantly increases the risk of lung function decline in PRISm patients. Chronic smoking is known to cause injury, inflammation, and tissue remodeling, ultimately leading to airway dysfunction, impaired alveolar ventilation, and airflow limitation [\u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Consequently, PRISm patients who smoke, particularly long-term smokers, face a heightened risk of progressing to COPD. For these individuals, regular spirometry assessment and monitoring are crucial. This practice facilitates the timely detection of early COPD signs, thereby enabling prompt intervention. While other studies have reported that smoking cessation can significantly improve key lung function parameters such as FEV1% and FEV1/FVC [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e], our analysis did not find a statistically significant association between smoking cessation and a reduced risk of lung function deterioration in PRISm patients. We postulate that this lack of association is likely due to the limited sample size of former smokers in our cohort. Additionally, our definition of \"smoking cessation\" did not account for its duration or the intensity of prior smoking, which may have led to potential exposure misclassification. Therefore, our findings do not negate the value of smoking cessation in the management of PRISm. Although quitting smoking may not fully reverse small airway damage caused by long-term tobacco use, it can effectively slow the progression of further structural injury and functional decline. Early cessation remains a crucial intervention for PRISm patients. Our study also found that PRISm individuals with a CAT score\u0026thinsp;\u0026ge;\u0026thinsp;20 were more prone to lung function deterioration. This is likely because respiratory symptoms (e.g., cough, dyspnea) serve as clinical manifestations of underlying persistent airway inflammation, mucus hypersecretion, or early-stage lung remodeling. These ongoing pathological processes can directly impair lung function, increase the risk of exacerbations, and thereby drive the progression toward fixed airflow limitation [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e]. However, applying the GOLD symptom threshold (CAT\u0026thinsp;\u0026ge;\u0026thinsp;10), widely used for stable COPD assessment, showed only a borderline association trend (P\u0026thinsp;=\u0026thinsp;0.058) in our cohort. This may be attributed to our relatively limited sample size, which provided insufficient statistical power to detect a potentially smaller effect size within this stratification. The utility of the CAT score is well-established in the management of chronic bronchitis and COPD, where it serves as a standard tool for guiding treatment decisions and evaluating prognosis [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. In light of our results, integrating this validated tool into the long-term management strategy for PRISm patients appears both rational and necessary. Our study also identified a significant association between metabolic syndrome and lung function decline in PRISm patients. This link may be attributed to the chronic low-grade inflammatory state, disruption of the endocrine-immune axis, as well as insulin resistance and oxidative stress commonly present in metabolic syndrome. Current evidence-based research acknowledges metabolic syndrome as a key driver of poor prognosis in PRISm [\u003cspan additionalcitationids=\"CR23 CR24\" citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e]. Nevertheless, further prospective studies specifically targeting the longitudinal transition of PRISm are warranted to clarify whether it constitutes an independent causal risk factor. It is noteworthy that univariate analysis indicated that older age, lower baseline levels of FEV1% and FEF25-75, a family history of respiratory diseases, and exposure to dust or biomass fuel were also associated with the outcome. These factors have been previously reported in the literature to be related to PRISm prognosis [\u003cspan citationid=\"CR11\" class=\"CitationRef\"\u003e11\u003c/span\u003e, \u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e, \u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Although they were not included in the multivariable analysis in this study due to constraints on the number of variables in the final model, they may still serve as potential risk markers. They warrant focused attention and validation in future studies with larger sample sizes.\u003c/p\u003e \u003cp\u003eOur study first conducted univariate analysis to preliminarily screen known risk factors for PRISm and potential predictors suggested by the literature. Subsequently, the significant variables identified from this screening were incorporated into a multivariable regression model for final model development. This analytical strategy was designed to optimize model performance while effectively controlling for bias introduced by potential confounding factors. However, this study has several limitations. Due to the relatively small sample size and limited number of outcome events, we explored a preliminary risk prediction model for PRISm progression. Consequently, not all variables showing significance in the univariate analysis could be included in the final model. Additionally, some data may be subject to recall bias. Future research should involve large-scale, prospective studies. Furthermore, this analysis did not incorporate routine biochemical markers or chest imaging characteristics, which will be important directions for subsequent investigations.\u003c/p\u003e \u003cp\u003eIn summary, the logistic regression-based prediction model for PRISm progression risk has demonstrated favorable predictive performance, accuracy, and practical utility. It serves as a practical tool, potentially assisting primary healthcare workers or individuals in estimating PRISm risk using the provided nomogram. This offers a scientific basis for the early identification and intervention of high-risk PRISm patients. Furthermore, the model can inform the development of targeted prevention and management strategies, thereby contributing more effectively to the protection of public health.\u003c/p\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eEthics approval and consent to participate\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis study was conducted in accordance with the principles of the Declaration of Helsinki.This study was reviewed and approved by the Biomedical Ethics Committee of Mianyang Central Hospital (Approval No.: S20240243-01). All participants provided written informed consent prior to enrollment, audio recordings, and abstraction of clinical data from the electronic medical record.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eConsent for publication\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eAs above, covered by Mianyang Central Hospital.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAvailability of data and materials\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe datasets used and analyzed during the current study are available from the corresponding author upon reasonable request.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors declare that they have no competing interests.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThis work was supported by the China International Medical Exchange Foundation(Z-2017-24-2301).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthors\u0026apos; contributions\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eWeiguo Xu contributed to the critical revision of the manuscript. The conception and design of the study, acquisition of funding, and project supervision were carried out by Xinlan Li. Weijing Shen was responsible for data collection, analysis, and drafting the initial manuscript. All authors read and approved the final version of the manuscript for publication.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eThe authors gratefully acknowledge the financial support from the China International Medical Exchange Foundation. We also extend our thanks to the Pulmonary Function Laboratory of Mianyang Central Hospital for their technical support throughout this study. \u003cstrong\u003e\u0026nbsp;\u003c/strong\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eGlobal Initiative for Chronic Obstructive Lung Disease (GOLD). 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Int J COPD. 2024;19:1315\u0026ndash;31.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNaznin R, Nazrina S, Hussain M, Naznen F. Influence of Cigarette Smoking on Forced Expiratory Volume in the First Second (FEV1) among Male Smokers. Community Based Med J. 2025;14(1):11\u0026ndash;5.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eShah R, Chiarella SE. (2021). Sex, Cells, and Asthma. Mayo Clinic Proceedings, 96(7), 1955\u0026ndash;1969.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eKharitonov MA, Salukhov VV, Kuandykova MV, Rudakov Y. Pituitary-gonadal system dysfunction in middle-aged men with an uncontrolled asthma. Bulletin of the Russian Military Medical Academy; 2021.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRan P, Zhou Y et al. Lung Function Decline and Airflow Limitation Risk in Preserved Ratio Impaired Spirometry (PRISm) Subtypes by Smoking Status. Chest. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHoffstedt B, Markstad T et al. PRISm is an important risk factor for development of COPD, also in non-smokers. Chest. 2025.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSingh D, Long G, Can\u0026ccedil;ado JED et al. Small airway disease in chronic obstructive pulmonary disease: insights and implications for the clinician[J]. Curr Opin Pulm Med 2020, 26 (2): 162\u0026ndash;8.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWang Z, Qiu Y, Ji X, Dong L. Effects of smoking cessation on individuals with COPD: a systematic review and meta-analysis. 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Preserved ratio impaired spirometry, chronic obstructive pulmonary disease, and development of frailty: a longitudinal study of older adults. J Gerontol Biol Sci Med Sci. 2024;glae139. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003e10.1093/gerona/glae139\u003c/span\u003e\u003cspan address=\"10.1093/gerona/glae139\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHan X, Pan Z, et al. Air pollution and dynamic transitions of preserved ratio impaired spirometry in the UK Biobank: a multi-state Markov model analysis. Commun Med. 2025;5:123.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"PRISm, longitudinal lung function trajectories, influencing factors, prediction model","lastPublishedDoi":"10.21203/rs.3.rs-8716601/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8716601/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003ePreserved ratio impaired spirometry (PRISm) represents a significant clinical phenotype. However, its lung function evolution exhibits marked heterogeneity, leading to substantial variability in the risk of progression to chronic obstructive pulmonary disease (COPD). This study aimed to analyze the longitudinal trajectories of lung function in patients with PRISm, identify independent risk factors for lung function decline, and develop and validate a clinical model for individualized risk prediction.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003e A total of 100 patients were followed prospectively for 3 years. Logistic regression was employed to analyze factors influencing the longitudinal lung function trajectories and to establish a corresponding clinical risk prediction model.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eSex, smoking status, a COPD Assessment Test (CAT) score\u0026thinsp;\u0026ge;\u0026thinsp;20, and metabolic syndrome were identified as influencing factors for lung function decline in patients with PRISm. The area under the ROC curve for the risk prediction model was 0.84. Decision curve analysis demonstrated that intervention based on the nomogram model yielded superior net benefit within a threshold probability range of 0.04 to 0.93.\u003c/p\u003e\u003ch2\u003eConclusion\u003c/h2\u003e \u003cp\u003eThe risk prediction model, constructed based on influencing factors for lung function decline in a PRISm population, can identify high-risk PRISm patients and provide a reference for preventing their progression to COPD.\u003c/p\u003e","manuscriptTitle":"A Prediction Model for Progression of preserved ratio impaired spirometry (PRISm) Based on Longitudinal Pulmonary Function Trajectories","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-17 11:26:55","doi":"10.21203/rs.3.rs-8716601/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"reviewerAgreed","content":"249084454395706582556758681557007860230","date":"2026-05-18T14:47:27+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2026-03-26T13:48:56+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"189913223268793716965753972289440908645","date":"2026-03-18T12:25:45+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"111990719715217362014000891712158796016","date":"2026-03-04T08:23:34+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2026-02-11T11:19:43+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2026-02-11T11:17:53+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2026-02-03T10:59:38+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2026-02-03T07:37:44+00:00","index":"","fulltext":""},{"type":"submitted","content":"BMC Pulmonary Medicine","date":"2026-02-03T07:21:25+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"bmc-pulmonary-medicine","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"pulm","sideBox":"Learn more about [BMC Pulmonary Medicine](http://bmcpulmmed.biomedcentral.com/)","snPcode":"","submissionUrl":"https://www.editorialmanager.com/pulm/default.aspx","title":"BMC Pulmonary Medicine","twitterHandle":"BMC_series","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"em","reportingPortfolio":"BMC Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"04bdc351-ceec-4488-82ad-6ed375f53442","owner":[],"postedDate":"February 17th, 2026","published":true,"recentEditorialEvents":[{"type":"reviewerAgreed","content":"249084454395706582556758681557007860230","date":"2026-05-18T14:47:27+00:00","index":140,"fulltext":""}],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2026-02-17T11:26:55+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-17 11:26:55","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8716601","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8716601","identity":"rs-8716601","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}