Zhu Xing-Yu, Jiang Zhi-Meng, Li Xiao-, Lv Zi-Wen, Tian Jian-Wei, Su Fei-Fei
Graduate School of Hebei North University, Zhangjiakou, Hebei, China.
Department of Cardiovascular Medicine, Chinese People's Liberation Army Air Force Medical Center, Beijing, China.
Front Physiol. 2025 Mar 26;16:1549138. doi: 10.3389/fphys.2025.1549138. eCollection 2025.
Sepsis is a leading cause of in-hospital mortality. However, its prevalence is increasing among the elderly population. Therefore, early identification and prediction of the risk of death in elderly patients with sepsis is crucial. The objective of this study was to create a machine learning model that can predict short-term mortality risk in elderly patients with severe sepsis in a clear and concise manner.
Data was collected from the MIMIC-IV (2.2). It was randomly divided into a training set and a validation set using a 7:3 ratio. Mortality predictors were determined through Recursive Feature Elimination (RFE). A prediction model for 28 days of ICU stay was built using six machine-learning algorithms. To create a comprehensive and nuanced model resolution, Shapley Additive Explanations (SHAP) and Local Interpretable Model-Agnostic Explanations (LIME) were used to systematically interpret the models at both a global and detailed level.
The study involved the analysis of 4,056 elderly patients with sepsis. A feature recursive elimination algorithm was utilized to select eight variables out of 49 for model development. Six machine learning models were assessed, and the Extreme Gradient Boosting (XGBoost) model was found to perform the best. The validation set achieved an AUC of 0.88 (95% CI: 0.86-0.90) and an accuracy of 0.84 (95% CI: 0.81-0.86) for this model. To examine the roles of the eight key variables in the model, SHAP analysis was employed. The global ranking order was made evident, and through the use of LIME analysis, the weights of each feature range in the prediction model were determined.
The study's machine learning prediction model is a dependable tool for forecasting the prognosis of elderly patients with severe sepsis.
脓毒症是住院死亡率的主要原因。然而，其在老年人群中的患病率正在上升。因此，早期识别和预测老年脓毒症患者的死亡风险至关重要。本研究的目的是创建一个机器学习模型，能够清晰简洁地预测老年重症脓毒症患者的短期死亡风险。
数据收集自MIMIC-IV（2.2）。使用7:3的比例将其随机分为训练集和验证集。通过递归特征消除（RFE）确定死亡率预测因子。使用六种机器学习算法构建了ICU住院28天的预测模型。为了创建一个全面且细致入微的模型分辨率，使用了夏普利值附加解释（SHAP）和局部可解释模型无关解释（LIME）在全局和详细层面系统地解释模型。
该研究涉及对4056例老年脓毒症患者的分析。利用特征递归消除算法从49个变量中选择8个变量用于模型开发。评估了六种机器学习模型，发现极端梯度提升（XGBoost）模型表现最佳。该模型在验证集中的AUC为0.88（95%CI：0.86 - 0.90），准确率为0.84（95%CI：0.81 - 0.86）。为了研究模型中八个关键变量的作用，采用了SHAP分析。明确了全局排名顺序，并通过LIME分析确定了预测模型中每个特征范围的权重。
该研究的机器学习预测模型是预测老年重症脓毒症患者预后的可靠工具。
