Background

Prediction of cardiac surgery-associated acute kidney injury (CS-AKI) in pediatric patients is crucial to improve outcomes and guide clinical decision-making. This study aimed to develop a supervised machine learning (ML) model for predicting moderate to severe CS-AKI at postoperative day 2 (POD2).

Methods

This retrospective cohort study analyzed data from 402 pediatric patients who underwent cardiac surgery at a university-affiliated children’s hospital, who were separated into an 80%-20% train-test split. The ML model utilized demographic, preoperative, intraoperative, and POD0 clinical and laboratory data to predict moderate to severe AKI categorized by Kidney Disease: Improving Global Outcomes (KDIGO) stage 2 or 3 at POD2. Input feature importance was assessed by SHapley Additive exPlanations (SHAP) values. Model performance was evaluated using accuracy, area under the receiver operating curve (AUROC), precision, recall, area under the precision-recall curve (AUPRC), F1-score, and Brier score.

Results

Overall, 13.7% of children in the test set experienced moderate to severe AKI. The ML model achieved promising performance, with accuracy of 0.91 (95% CI: 0.82–1.00), AUROC of 0.88 (95% CI: 0.72–1.00), precision of 0.92 (95% CI: 0.70–1.00), recall of 0.63 (95% CI: 0.32–0.96), AUPRC of 0.81 (95% CI: 0.61–1.00), F1-score of 0.73 (95% CI: 0.46–0.99), and Brier score loss of 0.09 (95% CI: 0.00–0.17). The top ten most important features assessed by SHAP analyses in this model were preoperative serum creatinine, surgery duration, POD0 serum pH, POD0 lactate, cardiopulmonary bypass duration, POD0 vasoactive inotropic score, sex, POD0 hematocrit, preoperative weight, and POD0 serum creatinine.

Conclusions

A supervised ML model utilizing demographic, preoperative, intraoperative, and immediate postoperative clinical and laboratory data showed promising performance in predicting moderate to severe CS-AKI at POD2 in pediatric patients.

Graphical abstract

中文翻译：

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使用机器学习预测小儿心脏手术相关的急性肾损伤

背景

预测儿科患者心脏手术相关的急性肾损伤 (CS-AKI) 对于改善预后和指导临床决策至关重要。本研究旨在开发一种监督机器学习 (ML) 模型，用于预测术后第 2 天 (POD2) 中度至重度 CS-AKI。

方法

这项回顾性队列研究分析了 402 名在大学附属儿童医院接受心脏手术的儿科患者的数据，这些患者被分为 80%-20% 的训练测试组。ML 模型利用人口统计、术前、术中以及 POD0 临床和实验室数据来预测按肾脏疾病分类的中度至重度 AKI：改善全球结果 (KDIGO) 在 POD2 的 2 期或 3 期。输入特征重要性通过 SHapley Additive exPlanations (SHAP) 值进行评估。使用准确度、受试者工作曲线下面积 (AUROC)、精确度、召回率、精确度-召回率曲线下面积 (AUPRC)、F1 分数和 Brier 分数来评估模型性能。

结果

总体而言，测试集中 13.7% 的儿童经历了中度至重度 AKI。ML 模型取得了可喜的性能，准确率为 0.91（95% CI：0.82–1.00），AUROC 为 0.88（95% CI：0.72–1.00），精确度为 0.92（95% CI：0.70–1.00），召回率为 0.63 (95% CI: 0.32–0.96)，AUPRC 为 0.81 (95% CI: 0.61–1.00)，F1 分数为 0.73 (95% CI: 0.46–0.99)，Brier 分数损失为 0.09 (95% CI: 0.00) –0.17)。在该模型中，SHAP 分析评估的最重要的 10 个特征是术前血清肌酐、手术持续时间、POD0 血清 pH 值、POD0 乳酸、体外循环持续时间、POD0 血管活性正性肌力评分、性别、POD0 血细胞比容、术前体重和 POD0 血清肌酐。

结论

利用人口统计学、术前、术中和术后即刻临床和实验室数据的监督 ML 模型在预测儿科患者 POD2 中度至重度 CS-AKI 方面表现出良好的性能。

图形概要