Machine learning prediction of moderate-to-severe acute kidney injury after ICU admission and cardiac surgery with urine trace elements.

Abstract

Background: Acute kidney injury (AKI) is common and linked to poor outcomes, but early detection remains challenging. Previous research identified urinary trace elements (TE) as early AKI biomarkers in intensive care unit (ICU) or cardiac surgery patients. We aimed to explore whether urinary TE enhance machine learning (ML) models for AKI prediction.

Methods: We constructed ML models using the ICU cohort. We filtered the variables and optimized hyperparameters before predicting Kidney Disease: Improving Global Outcomes stage 2-3 AKI using eight ML classifiers: light gradient boosting machine (LightGBM), random forest (RF), ML logistic regression, support vector machine, multilayer perceptron, eXtreme gradient boosting (XGBoost), Gaussian Naive Bayes and k-nearest neighbors. External validation was performed in the cardiac surgery cohort.

Results: Among 149 ICU patients (median age 56.0 [interquartile range (IQR): 43.5-67.0], 63.1% male), 25 developed stage 2-3 AKI; among 144 cardiac surgery patients (median age 70.0 [IQR: 62.0-76.0], 72.9% male), 12 developed stage 2-3 AKI. Each ML in the internal validation had area under the curve (AUC) above .7, with XGBoost having the highest (.813); LightGBM had the second highest AUC (.799), highest G-mean (.567) and F1-score (.545). In external validation, RF had the highest AUC (.740), XGBoost had the highest G-mean (.289) and F1-score (.286). Age, strontium and boron were consistently ranked among the top five most important features in LightGBM, RF and XGBoost.

Conclusion: ML models primarily based on urinary TE can identify AKI risk in both clinical groups (ICU and cardiac surgery), with LightGBM, RF and XGBoost serving as high-performance models for early prediction of stage 2-3 AKI.