Lu Li,* Jiaqi Meng,* Jiquan Chen

Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, People’s Republic of China

*These authors contributed equally to this work

Correspondence: Jiquan Chen, Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, People’s Republic of China, Tel +86-13065135302, Email chenjq9932@163.com

Objective: Chronic lung diseases (CLDs) are a major global health concern, characterized by a progressive decline in pulmonary function that severely impacts quality of life. It is essential to identify and predict the primary risk factors for CLDs. This study aims to establish a predictive model to assist healthcare providers in the early identification of high-risk patients and timely interventions and treatment options.
Methods: This study utilized questionnaire data from the China Health and Retirement Longitudinal Study (CHARLS) collected in 2011, 2013, and 2015. A latent class growth model (LCGM) was established using CLDs as the baseline sample. This model stratified the patients based on the extent of the decline in Δpeak expiratory flow (ΔPEF), which served as the target variable. Independent variables included age, gender, smoking status, body mass index, education level, and comorbidities. A random forest model was developed using Python, and the importance of the feature was visualized through the SHAP method. The predictive performance of the model was evaluated using receiver operating characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis.
Results: After screening, a total of 553 patients with CLDs were included in the study. The random forest model pinpointed grip strength, age, education level, gender, and asthma as the top five risk factors for pulmonary function decline. Specifically, the model demonstrated robust predictive performance with an area under the ROC curve (AUC) value of 0.77, affirming its accuracy and clinical applicability. Both calibration and decision curves further substantiated the reliability of the model in identifying patients at increased risk for pulmonary function decline.
Conclusion: The predictive model developed in this study serves as a valuable tool for clinicians to target early interventions and optimize treatment strategies to enhance the quality of care and patient outcomes in the management of CLDs.

Keywords: chronic lung diseases, pulmonary function decline, latent class growth modeling, random forest model, health services, machine learning in healthcare