ArticleBMC medical informatics and decision making2025
Development and external validation of a machine learning-based predictive model for acute kidney injury in hospitalized children with idiopathic nephrotic syndrome.
Article in BMC medical informatics and decision making, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Artificial intelligence in pediatric nephrology: current applications and emerging frameworks for evidence generation.Pediatric nephrology (Berlin, Germany) · 2026Review
- Development and external validation of an interpretable early prediction model for acute kidney injury using TabPFN and routine admission data: a retrospective cohort study.BMC medical informatics and decision making · 2026Article
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14 authors.
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Abstract
backgroundAcute kidney injury (AKI), a critical complication of childhood idiopathic nephrotic syndrome (INS), markedly increases the risk of chronic kidney disease (CKD) and mortality. This study developed an interpretable machine learning (ML) model for early AKI prediction in pediatric INS to enable proactive interventions and mitigate adverse outcomes.
methodsA total of 3,390 patients and 356 hospitalized pediatric patients with INS were included in the derivation and external cohorts, respectively, from four hospitals across China. Logistic regression, Random Forest, K-nearest neighbors, Naïve Bayes, and Support Vector machines were integrated into a stacking ensemble model and optimized for class imbalance using SMOTE-Tomek. Model performance was assessed using the area under the curve (AUC), area under the precision-recall curve, sensitivity, specificity, and balanced accuracy. SHapley Additive Explanations (SHAP) analysis elucidated the importance of features, and a Random Forest model was developed to predict CKD progression in patients with AKI.
resultsOf the 3,390 patients with INS, 12.9% developed AKI. The stacking model outperformed the individual algorithms, achieving an AUC of 0.888 internally and 0.822 externally, which could be well explained by the SHAP algorithm. It was then deployed as a web-based calculator for real-time risk assessment. Key predictors included exposure to nephrotoxic antibiotics, exposure to cyclophosphamide, respiratory tract infection, urine pH, and admission times. In the AKI cohort, alanine transaminase, aspartate transaminase, and serum phosphate levels were primarily associated with the development of CKD.
conclusionsThis study presents an interpretable ML model for early AKI prediction in pediatric Chinese patients with INS, which could serve as a practical, efficient, and economical tool for preventing AKI by identifying modifiable risk factors to reduce AKI incidence and mitigate CKD progression.
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