可解释机器学习模型预测心力衰竭合并急性肾损伤患者在重症监护室内的短期死亡率：一项回顾性队列研究

罗欣瑶; 万丁源; 王轲; 李育霈; 廖若西; 苏白海

doi:10.12182/20250160507

可解释机器学习模型预测心力衰竭合并急性肾损伤患者在重症监护室内的短期死亡率：一项回顾性队列研究

Predicting Intensive Care Unit Mortality in Patients With Heart Failure Combined With Acute Kidney Injury Using an Interpretable Machine Learning Model: A Retrospective Cohort Study

摘要

摘要:
目的开发一种可解释的机器学习模型，以提高心力衰竭（heart failure, HF）合并急性肾损伤（acute kidney injury, AKI）患者短期死亡率的早期预测准确性。

方法本回顾性队列研究利用了基于电子病历的公开大型数据库重症医学信息数据库MIMIC-Ⅳ（Medical Information Mart for Intensive Care Ⅳ, 版本2.0）。提取了患者入住ICU最初24 h的数据，并将其分为训练集（80%）和验证集（20%）。利用沙普利可加性解释（Shapley additive explanation, SHAP）方法解释极端梯度提升（XGBoost）模型的工作原理，并识别关键的预后因素。使用曲线下面积（area under the curve, AUC）指标评估XGBoost模型的预测能力，并与3种其他机器学习模型进行比较，其解释性通过SHAP方法得到增强。
结果研究包括8028名合并AKI的HF患者。XGBoost模型的表现优于其他模型，达到了0.93的AUC〔95%置信区间（confidence interval, CI） 0.78～0.94，准确度=0.89〕，而神经网络的表现最差（AUC=0.79，95%CI 0.77～0.82，准确度=0.82）。决策曲线分析显示XGBoost模型在9%至80%的阈值概率内具有更高的净收益。SHAP分析识别了前20个预测因素，年龄以及格拉斯哥昏迷评分被识别为重要的因素，SHAP平均值分别为1.29和1.24。

结论本研究开发的可解释模型提高了预测ICU内HF患者并发AKI的死亡风险的能力。这个模型不仅有助于制定有效的治疗计划，还能优化资源分配。

Abstract:
Objective Heart failure (HF) complicated by acute kidney injury (AKI) significantly impacts patient outcomes, and it is crucial to make early predictions of short-term mortality. This study is focused on developing an interpretable machine learning model to enhance early prediction accuracy in such clinical scenarios.
Methods This retrospective cohort study utilized data from the Medical Information Mart for Intensive Care Ⅳ (MIMIC-Ⅳ, version 2.0) database. Data from the first 24 hours after admission to the ICU were extracted and divided into a training set (70%) and a validation set (30%). We utilized the SHapley Additive exPlanation (SHAP) method to interpret the workings of an extreme gradient boosting (XGBoost) model and identify key prognostic factors. The XGBoost model's predictive ability was evaluated against three other machine learning models using the area under the curve (AUC) metric, and its interpretation was enhanced using the SHAP method.
Results The study included 8028 patients with HF complicated by AKI. The XGBoost model outperformed the other models, achieving an AUC of 0.93 (95% confidence interval CI: 0.78-0.94; accuracy = 0.89), while neural network model showed the worst performance (AUC = 0.79, 95% CI: 0.77-0.82; accuracy = 0.82). Decision curve analysis showed the superior net benefit of the XGBoost model within the 9% to 60% threshold probabilities. SHAP analysis was performed to identify the top 20 predictors, with age (mean SHAP value 1.29) and Glasgow Coma Scale score (mean SHAP value 1.24) emerging as significant factors.
Conclusions Our interpretable model offers an enhanced ability to predict mortality risk in HF patients with AKI in ICUs. This model can be used to assist in formulating effective treatment plans and optimizing resource allocation.

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