Utilization of interpretable machine learning model to forecast the risk of major adverse kidney events in elderly patients in critical care

Abstract

Major adverse kidney events within 30 d (MAKE30) implicates poor outcomes for elderly patients in the intensive care unit (ICU). This study aimed to predict the occurrence of MAKE30 in elderly ICU patients using machine learning. The study cohort comprised 2366 elderly ICU patients admitted to the Second Xiangya Hospital of Central South University between January 2020 and December 2021. Variables including demographic information, laboratory values, physiological parameters, and medical interventions were used to construct an extreme gradient boosting (XGBoost) -based prediction model. Out of the 2366 patients, 1656 were used for model derivation and 710 for testing. The incidence of MAKE30 was 13.8% in the derivation cohort and 13.2% in the test cohort. The average area under the receiver operating characteristic curve of the XGBoost model was 0.930 (95% CI: 0.912–0.946) in the training set and 0.851 (95% CI: 0.810–0.890) in the test set. The top 8 predictors of MAKE30 tentatively identified by the Shapley additive explanations method were Acute Physiology and Chronic Health Evaluation II score, serum creatinine, blood urea nitrogen, Simplified Acute Physiology Score II score, Sequential Organ Failure Assessment score, aspartate aminotransferase, arterial blood bicarbonate, and albumin. The XGBoost model accurately predicted the occurrence of MAKE30 in elderly ICU patients, and the findings of this study provide valuable information to clinicians for making informed clinical decisions.

Keywords:

• Critical illness
• major adverse kidney events
• the elderly
• XGBoost
• machine learning

Disclosure statement

No potential conflict of interest was reported by the author(s).

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.