Li Yike, Xiao Mingyang, Li Yaqian, Lv Lulu, Zhang Shanshan, Liu Yuhui, Zhang Juan
The Second Clinical Medical School, Zhengzhou University, Zhengzhou, China.
JMIR Med Inform. 2025 May 28;13:e72349. doi: 10.2196/72349.
Acute kidney injury (AKI) frequently occurs in critically ill patients with coronary heart disease (CHD), and its development markedly elevates mortality rates and prolongs hospitalization duration. Early AKI prediction is crucial for timely intervention and amelioration of patient outcomes.
This study aimed to develop and verify a clinical prediction model for the occurrence of AKI upon admission in the critically ill population with CHD through machine learning (ML).
Data from the MIMIC-IV (Medical Information Mart for Intensive Care IV) version 2.2 database were gathered and included information about critically ill individuals with CHD in the intensive care unit (ICU). The dataset was randomized into a training set (70%) and a testing set (30%). Least absolute shrinkage and selection operator (LASSO) regression was used for feature variable selection. ML models, including logistic regression (LR), decision tree (DT), naive Bayes (NB), random forest (RF), extreme gradient boosting (XGBoost), and support vector machine (SVM), were constructed using 13 variables in the training set. The 6 models were compared in the testing set to identify the best-performing model. Subsequently, the model was assessed using calibration curve analysis and decision curve analysis (DCA). External validation was conducted using data from the Second Affiliated Hospital of Zhengzhou University. Ultimately, the predictive model was interpreted via Shapley Additive Explanation (SHAP) values.
In total, 2711 patients with CHD admitted to the ICU were selected, with 1809 (66.7%) having AKI. XGBoost exhibited the best performance regarding discrimination (area under the receiver operating characteristic curve [AUROC]=0.765, 95% CI 0.731-0.800), accuracy (0.725), and sensitivity (0.759). External validation using a cohort of 226 patients confirmed the strong generalizability of the XGBoost model (AUROC=0.835, 95% CI 0.782-0.887). Feature importance analyses derived from SHAP values, DT, RF, and XGBoost consistently identified 5 key predictors associated with the development of AKI: mechanical ventilation, use of antiplatelet agents, age, N-terminal pro-B-type natriuretic peptide (NT-proBNP) levels, and acute physiology score III (APSIII).
ML models can serve as reliable tools for forecasting AKI in the critically ill population with CHD. The XGBoost model is highly accurate and may aid doctors in identifying high-risk individuals for early intervention to lower mortality.
急性肾损伤（AKI）在冠心病（CHD）重症患者中经常发生，其发展显著提高死亡率并延长住院时间。早期AKI预测对于及时干预和改善患者预后至关重要。
本研究旨在通过机器学习（ML）开发并验证冠心病重症患者入院时发生AKI的临床预测模型。
收集多重症监护医学信息集市（MIMIC-IV）2.2版数据库中的数据，包括重症监护病房（ICU）中冠心病重症患者的信息。将数据集随机分为训练集（70%）和测试集（30%）。采用最小绝对收缩和选择算子（LASSO）回归进行特征变量选择。使用训练集中的13个变量构建包括逻辑回归（LR）、决策树（DT）、朴素贝叶斯（NB）、随机森林（RF）、极端梯度提升（XGBoost）和支持向量机（SVM）在内的ML模型。在测试集中比较这6个模型，以确定表现最佳的模型。随后，使用校准曲线分析和决策曲线分析（DCA）对该模型进行评估。利用郑州大学第二附属医院的数据进行外部验证。最终，通过夏普利值相加解释（SHAP）对预测模型进行解释。
共纳入2711例入住ICU的冠心病患者，其中1809例（66.7%）发生AKI。XGBoost在区分度（受试者工作特征曲线下面积[AUROC]=0.765，95%CI 0.731 - 0.800）、准确性（0.725）和敏感性（0.759）方面表现最佳。使用226例患者队列进行的外部验证证实了XGBoost模型具有很强的通用性（AUROC=0.835，95%CI 0.782 - 0.887）。来自SHAP值、DT、RF和XGBoost的特征重要性分析一致确定了与AKI发生相关的5个关键预测因素：机械通气、抗血小板药物的使用、年龄、N末端B型脑钠肽原（NT-proBNP）水平和急性生理评分III（APSIII）。
ML模型可作为预测冠心病重症患者AKI的可靠工具。XGBoost模型高度准确，可能有助于医生识别高危个体以便早期干预，从而降低死亡率。
