Machine learning-based model for predicting all-cause mortality in severe pneumonia

作者：Weichao Zhao, Xuyan Li, Lianjun Gao, Zhuang Ai, Yaping Lu, Jiachen Li, Dong Wang, Xin‐Lou Li, Nan Song, Xuan Huang, Zhaohui Tong · 发表于：BMJ Open Respiratory Research · 年份：2025 · DOI：10.1136/bmjresp-2023-001983 · 被引用次数：14 · 研究领域：Sepsis Diagnosis and Treatment、Nosocomial Infections in ICU、Pneumonia and Respiratory Infections

BACKGROUND: Severe pneumonia has a poor prognosis and high mortality. Current severity scores such as Acute Physiology and Chronic Health Evaluation (APACHE-II) and Sequential Organ Failure Assessment (SOFA), have limited ability to help clinicians in classification and management decisions. The goal of this study was to analyse the clinical characteristics of severe pneumonia and develop a machine learning-based mortality-prediction model for patients with severe pneumonia. METHODS: Consecutive patients with severe pneumonia between 2013 and 2022 admitted to Beijing Chaoyang Hospital affiliated with Capital Medical University were included. In-hospital all-cause mortality was the outcome of this study. We performed a retrospective analysis of the cohort, stratifying patients into survival and non-survival groups, using mainstream machine learning algorithms (light gradient boosting machine, support vector classifier and random forest). We aimed to construct a mortality-prediction model for patients with severe pneumonia based on their accessible clinical and laboratory data. The discriminative ability was evaluated using the area under the receiver operating characteristic curve (AUC). The calibration curve was used to assess the fit goodness of the model, and decision curve analysis was performed to quantify clinical utility. By means of logistic regression, independent risk factors for death in severe pneumonia were figured out to provide an important basis for clinical de...