Li Lu, Meng Jiaqi, Chen Jiquan
Department of Pulmonary and Critical Care Medicine, Third Affiliated Hospital of Naval Medical University, Shanghai, 200438, People's Republic of China.
Int J Chron Obstruct Pulmon Dis. 2024 Dec 5;19:2639-2650. doi: 10.2147/COPD.S487178. eCollection 2024.
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.
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.
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.
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.
慢性肺部疾病（CLDs）是全球主要的健康问题，其特征是肺功能逐渐下降，严重影响生活质量。识别和预测CLDs的主要危险因素至关重要。本研究旨在建立一个预测模型，以帮助医疗保健提供者早期识别高危患者，并及时进行干预和选择治疗方案。
本研究使用了2011年、2013年和2015年收集的中国健康与养老追踪调查（CHARLS）的问卷数据。以CLDs为基线样本建立了潜在类别增长模型（LCGM）。该模型根据作为目标变量的呼气峰值流速下降程度（ΔPEF）对患者进行分层。自变量包括年龄、性别、吸烟状况、体重指数、教育水平和合并症。使用Python开发了随机森林模型，并通过SHAP方法直观显示特征的重要性。使用受试者工作特征（ROC）曲线分析、校准曲线分析和决策曲线分析评估模型的预测性能。
经过筛选，共有553例CLDs患者纳入研究。随机森林模型确定握力、年龄、教育水平、性别和哮喘是肺功能下降的前五大危险因素。具体而言，该模型表现出强大的预测性能，ROC曲线下面积（AUC）值为0.77，证实了其准确性和临床适用性。校准曲线和决策曲线均进一步证实了该模型在识别肺功能下降风险增加患者方面的可靠性。
本研究开发的预测模型是临床医生的宝贵工具，可用于针对性地进行早期干预和优化治疗策略，以提高CLDs管理中的护理质量和患者预后。
