Construction and evaluation of a mortality prediction model for patients with acute kidney injury undergoing continuous renal replacement therapy based on machine learning algorithms

To construct and evaluate a predictive model for in-hospital mortality among critically ill patients with acute kidney injury (AKI) undergoing continuous renal replacement therapy (CRRT), based on nine machine learning (ML) algorithm.
The study retrospectively included patients with AKI who underwent CRRT during their initial hospitalization in the United States using the medical information mart for intensive care (MIMIC) database IV (version 2.0), as well as in the intensive care unit (ICU) of Huzhou Central Hospital. Patients from the MIMIC database were used as the training cohort to construct the models (from 2008 to 2019, n = 1068). Patients from Huzhou Central Hospital were utilized as the external validation cohort to evaluate the models (from June 2019 to December 2022, n = 327). In the training cohort, least absolute shrinkage and selection operator (LASSO) regression with cross-validation was employed to select features for constructing the model and subsequently established nine ML predictive models. The performance of these nine models on the external validation cohort dataset was comprehensively evaluated based on the area under the receiver operating characteristic curve (AUROC) and the optimal model was selected. A static nomogram and a web-based dynamic nomogram were presented, with a comprehensive evaluation from the perspectives of discrimination (AUROC), calibration (calibration curve) and clinical practicability (DCA curves).
Finally, 1395 eligible patients were enrolled, including 1068 patients in the training cohort and 327 patients in the external validation cohort. In the training cohort, LASSO regression with cross-validation was employed to select features and nine models were individually constructed. Compared to the other eight models, the Lasso regularized logistic regression (Lasso-LR) model exhibited the highest AUROC (0.756) and the optimal calibration curve. The DCA curve suggested a certain clinical utility in predicting in-hospital mortality among critically ill patients with AKI undergoing CRRT. Consequently, the Lasso-LR model was the optimal model and it was visualized as a common nomogram (static nomogram) and a web-based dynamic nomogram (https://chsyh2006.shinyapps.io/dynnomapp/). Discrimination, calibration and DCA curves were employed to assess the performance of the nomogram. The AUROC for the training and external validation cohorts in the nomogram model was 0.771 (95%CI: 0.743, 0.799) and 0.756 (95%CI: 0.702, 0.809), respectively. The calibration slope and Brier score for the training cohort were 1.000 and 0.195, while for the external validation cohort, they were 0.849 and 0.197, respectively. The DCA indicated that the model had a certain clinical application value.
Our study selected the optimal model and visualized it as a static and dynamic nomogram integrating clinical predictors, so that clinicians can personalized predict the in-hospital outcome of critically ill patients with AKI undergoing CRRT upon ICU admission.

本研究旨在基于九种机器学习（Machine Learning, ML）算法，构建并评估针对接受连续性肾脏替代治疗（Continuous Renal Replacement Therapy, CRRT）的急性肾损伤（Acute Kidney Injury, AKI）重症患者的院内死亡预测模型。
本研究依托美国重症监护医学信息库IV（Medical Information Mart for Intensive Care IV, MIMIC-IV，版本2.0），以及湖州市中心医院重症监护室（Intensive Care Unit, ICU）的临床数据，回顾性纳入首次住院期间接受CRRT治疗的AKI患者。其中，MIMIC数据库来源患者作为训练队列（2008年至2019年，n=1068）用于构建模型；湖州市中心医院来源患者作为外部验证队列（2019年6月至2022年12月，n=327）用于模型性能评估。在训练队列中，本研究采用带交叉验证的最小绝对收缩和选择算子（Least Absolute Shrinkage and Selection Operator, LASSO）回归进行特征筛选，随后构建了九种机器学习预测模型。基于受试者工作特征曲线下面积（Area Under the Receiver Operating Characteristic Curve, AUROC），全面评估这九种模型在外部验证队列数据集上的性能，并筛选出最优模型。此外，本研究构建了静态列线图与基于网页的动态列线图，并从区分度（AUROC）、校准度（校准曲线）与临床实用性（决策曲线分析，Decision Curve Analysis, DCA曲线）三个维度进行综合评价。
最终共有1395例符合纳入标准的患者入组，其中训练队列1068例，外部验证队列327例。在训练队列中，本研究通过带交叉验证的LASSO回归筛选特征并构建九种预测模型。相较于其余八种模型，LASSO正则化逻辑回归（Lasso regularized Logistic Regression, Lasso-LR）模型展现出最高的AUROC（0.756）与最优校准曲线。决策曲线分析结果显示，该模型在预测接受CRRT治疗的AKI重症患者院内死亡风险方面具备一定临床应用价值。综上，Lasso-LR模型为最优预测模型，本研究将其可视化为通用列线图（静态列线图）与基于网页的动态列线图（https://chsyh2006.shinyapps.io/dynnomapp/）。本研究采用区分度、校准度与决策曲线分析评估该列线图的性能：训练队列与外部验证队列中该列线图模型的AUROC分别为0.771（95%CI：0.743, 0.799）与0.756（95%CI：0.702, 0.809）；训练队列的校准斜率与Brier评分分别为1.000与0.195，外部验证队列则分别为0.849与0.197。决策曲线分析结果表明，该模型具备一定的临床应用价值。
本研究筛选出最优预测模型，并将其可视化为整合临床预测因子的静态与动态列线图，以便临床医师在重症患者入住ICU时，个体化预测接受CRRT治疗的AKI重症患者的院内结局。