The Application of Machine Learning in Predicting Post-Cardiac Surgery Acute Kidney Injury (CS-AKI) in Pediatric Patients: A systematic review

Sxe Chang Cheong^*

• The University of Sheffield, Sheffield, United Kingdom

Acute kidney injury (AKI) frequently complicates pediatric cardiac surgery with high incidence and outcomes. Conventional markers (KDIGO criteria) often fall short for pediatric patients undergoing cardiac surgery. Emerging machine learning models offer improved early detection and risk stratification. This review evaluates ML models' feasibility, performance, and generalizability in predicting pediatric AKI.This systematic review adheres to PRISMA-DTA guidelines. Search was conducted on PubMed and Medline (Ovid/Embase) on March 24, 2024, using PICOTS-based keywords. Titles, abstracts, and full texts were screened for eligibility. Data on study characteristics and best-performing ML models' AUROC, sensitivity, and specificity were extracted. PROBAST evaluated risk of bias and applicability comprehensively. A narrative synthesis approach was employed to summarize findings due to heterogeneity in study designs and outcome measures.Nine unique studies were identified and included, eight focused on post-cardiac surgery, and one on both PICU admissions and post-cardiac surgery patients. PROBAST demonstrated high risk of bias and low applicability amongst the studies, with notably limited external validation.While ML models predicting AKI in post-cardiac surgery pediatric patients show promising discriminatory ability with prediction lead times up to two days, outperforming traditional biomarkers and KDIGO criteria, findings must be interpreted cautiously. High risk of bias across studies, particularly lack of external validation, substantially limits evidence strength and clinical applicability. Variations in study design, patient populations, and outcome definitions complicate direct comparisons. Robust external validation through multicenter cohorts using standardized guidelines is essential before clinical implementation. Current evidence, though promising, is insufficient for widespread adoption without addressing these methodological limitations.