Machine learning model predicts acute kidney injury in pediatric patients after cardiac surgery: a systematic review and meta-analysis

BACKGROUND: Acute kidney injury (AKI) is a common complication following pediatric cardiac surgery, frequently leading to poor outcomes and even death in severe cases. Early prevention remains the primary intervention strategy. Studies have developed prediction models to identify at-risk children at an early stage. This study systematically evaluate existing AKI prediction models to support their clinical utility and future refinement. METHODS: PubMed, Embase, Web of Science, Cochrane Library, China National Knowledge Infrastructure, Wanfang and SinoMed were searched from inception to 31 December, 2024. The search of references from included studies, as well as the manual search, extended until November 30, 2025. Literature searching, screening, and data extraction were done by two authors. Quality evaluation according to prediction model risk of bias assessment tool (PROBAST). Area under the receiver operating characteristic curve (AUROC) was pooled using a random-effects model to summarize the overall performance of existing models, exploring sources of heterogeneity of performance through subgroup analysis and meta-regression. Sensitivity analysis and Egger's method were used to analyze the stability of the included studies and to identify publication bias. This study was registered with PROSPERO (CRD42024593112) and reported following the Transparent Reporting of Multivariable Prediction Models for Individual Prognosis or Diagnosis: Checklist for Systematic Reviews and Meta-Analysis (TRIPOD-SRMA). RESULTS: A total of 2189 studies were screened which represented the total number of studies retrieved from the database search, the search of references from included studies, and the manual search. Nineteen studies were included in this review. Included studies differed in study design, AKI definition, predictor screening, model development and validation and model performance. The overall pooled AUROC was 0.850 (95% CI, 0.810-0.890), but all studies were evaluated as high risk of bias using the PROBAST. Heterogeneity in model performance was high, and study design and development methods were identified as possible sources of heterogeneity in pooled AUROC. Included studies were stable and free of publication bias. CONCLUSIONS: This systematic review suggested that machine learning models for predicting postoperative AKI in pediatric cardiac surgery indicated good discriminative ability. However, the high risk of bias across all included studies and the significant heterogeneity in model performance indicated that the reported performance may be overestimated. The high heterogeneity observed highlights the substantial variability in model performance, which is likely driven by differences in study design and development methods. The clinical utility of these models was currently limited due to the lack of external validation in most studies and the methodological limitations identified. Future research must incorporate rigorous study design, transparent reporting based on the TRIPOD guidelines, and external validation to develop prediction models with clinical utility.