Ren Bingkui, Zhang Yuping, Chen Siying, Dai Jinglong, Chong Junci, Zhong Yifei, Deng Mengkai, Jiang Shaobo, Chang Zhigang
Department of Critical Care Medicine, Beijing Hospital, National Center of Gerontology, Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, 100730, P.R. China.
Graduate School of Peking Union Medical College, Beijing, P.R. China.
BMC Med Inform Decis Mak. 2025 Jul 15;25(1):263. doi: 10.1186/s12911-025-03101-9.
Hemorrhage is a prevalent and critical condition in the intensive care unit (ICU), characterized by high incidence, elevated mortality rates, and substantial therapeutic challenges. Accurate prediction of mortality in patients with hemorrhage is essential for developing personalized prevention and treatment strategies. Nevertheless, the implementation of effective predictive models in clinical practice remains limited, primarily due to the lack of robust and interpretable tools.
This study aimed to develop an interpretable model for predicting mortality risk in critically ill patients with hemorrhage admitted to ICUs. The SHapley Additive exPlanations (SHAP) method was applied to interpret the eXtreme Gradient Boosting (XGBoost)model, identifying key prognostic factors in this population.
In this retrospective cohort study, we derived data from the eICU Collaborative Research Database (eICU-CRD) to develop and evaluate a predictive model. Clinical data from the first 24 h of ICU admission were extracted, and the dataset was randomly split into training (80%) and validation (20%) sets. Model performance was compared to four other machine learning algorithms using the area under the curve (AUC). SHAP was utilized to interpret the XGBoost model. External validation was subsequently performed using data from the Chinese REFRAIN cohort, which focuses on hemorrhage and coagulopathy in critically ill patients..
The study protocol was retrospectively registered in the Chinese Clinical Trial Registry (ChiCTR) on December 17, 2024 (Registration number ChiCTR2400094140).
A total of 10,306 eligible patients with hemorrhage were included. The observed in-hospital mortality rate was 11.5%.Among the five models compared, XGBoost demonstrated the highest predictive performance (AUC = 0.81), whereas logistic regression (LR) showed the lowest generalizability(AUC = 0.726). Decision curve analysis revealed that the XGBoost model provided a greater net benefit than other models at threshold probabilities of 10-30%. SHAP analysis identified the top 15 predictors of mortality, with bilirubin level ranked as the most influential variable. External validation using the REFRAIN cohort confirmed the robustness of model(AUC = 0.776).
The interpretable predictive model improves mortality risk stratification in ICU patients with hemorrhage, supporting clinicians in optimizing treatment plans and resource allocation. Enhanced model transparency through SHAP explanations may facilitate clinical adoption by improving trust in model reliability.
出血是重症监护病房（ICU）中常见且危急的病症，具有高发病率、高死亡率以及重大治疗挑战的特点。准确预测出血患者的死亡率对于制定个性化的预防和治疗策略至关重要。然而，有效的预测模型在临床实践中的应用仍然有限，主要原因是缺乏强大且可解释的工具。
本研究旨在开发一种可解释的模型，用于预测入住ICU的重症出血患者的死亡风险。采用SHapley加性解释（SHAP）方法来解释极端梯度提升（XGBoost）模型，识别该人群中的关键预后因素。
在这项回顾性队列研究中，我们从eICU协作研究数据库（eICU-CRD）中获取数据来开发和评估一个预测模型。提取ICU入院后前24小时的临床数据，并将数据集随机分为训练集（80%）和验证集（20%）。使用曲线下面积（AUC）将模型性能与其他四种机器学习算法进行比较。利用SHAP来解释XGBoost模型。随后使用来自中国REFRAIN队列的数据进行外部验证，该队列专注于重症患者的出血和凝血障碍。
该研究方案于2024年12月17日在中国临床试验注册中心（ChiCTR）进行回顾性注册（注册号ChiCTR2400094140）。
共纳入10306例符合条件的出血患者。观察到的院内死亡率为11.5%。在比较的五个模型中，XGBoost表现出最高的预测性能（AUC = 0.81），而逻辑回归（LR）的泛化能力最低（AUC = 0.726）。决策曲线分析表明，在阈值概率为10%-30%时，XGBoost模型比其他模型提供了更大的净效益。SHAP分析确定了死亡率的前15个预测因素，胆红素水平被列为最有影响力的变量。使用REFRAIN队列进行的外部验证证实了模型的稳健性（AUC = 0.776）。
可解释的预测模型改善了ICU出血患者的死亡风险分层，有助于临床医生优化治疗方案和资源分配。通过SHAP解释提高模型透明度可能会通过增强对模型可靠性的信任来促进临床应用。
