Breaking new ground: machine learning enhances survival forecasts in hypercapnic respiratory failure

作者：Zhongxiang Liu, Bingqing Zuo, Jianyang Lin, Zhixiao Sun, H. Hu, Yuan Yin, Shuanying Yang · 发表于：Frontiers in Medicine · 年份：2025 · DOI：10.3389/fmed.2025.1497651 · 被引用次数：4 · 研究领域：Sepsis Diagnosis and Treatment、Respiratory Support and Mechanisms、Nosocomial Infections in ICU

Background: The prognostic prediction of patients with hypercapnic respiratory failure holds significant clinical value. The objective of this study was to develop and validate a predictive model for predicting survival in patients with hypercapnic respiratory failure. Methods: The study enrolled a total of 697 patients with hypercapnic respiratory failure, including 565 patients from the First People's Hospital of Yancheng in the modeling group and 132 patients from the People's Hospital of Jiangsu Province in the external validation group. The three selected models were random survival forest (RSF), DeepSurv, a deep learning-based survival prediction algorithm, and Cox Proportional Risk (CoxPH). The model's predictive performance was evaluated using the C-index and Brier score. Receiver operating characteristic curve (ROC), area under ROC curve (AUC), and decision curve analysis (DCA) were employed to assess the accuracy of predicting the prognosis for survival at 6, 12, 18, and 24 months. Results: The RSF model (c-index: 0.792) demonstrated superior predictive ability for the prognosis of patients with hypercapnic respiratory failure compared to both the traditional CoxPH model (c-index: 0.699) and DeepSurv model (c-index: 0.618), which was further validated on external datasets. The Brier Score of the RSF model demonstrated superior performance, consistently measuring below 0.25 at the 6-month, 12-month, 18-month, and 24-month intervals. The ROC curve confirmed the superi...