Epilepsy prediction models for children and adolescents: a systematic review and meta-analysis.

Background: Epilepsy in children and adolescents harms cognitive development and quality of life, necessitating early risk identification to improve outcomes. Yet, current predictive models yielded inconsistent results, demanding a thorough evaluation of their accuracy and effectiveness to guide future research and inform evidence-based clinical strategies. This review aimed to integrate existing research findings on epilepsy prediction models for children and adolescents. Methods: China National Knowledge Infrastructure, Wanfang Database, SinoMed, China Science and Technology Journal Database, PubMed, Embase, CINAHL, and Web of Science were searched from inception to August 31, 2025. The Prediction Model Risk of Bias Assessment Tool was used to assess the risk of bias and applicability. The areas under the curve (AUC) with 95% confidence intervals were pooled using random-effects meta-analysis. The study was registered with PROSPERO (CRD42025637913). Findings: A total of 27 studies were included in this review. Sixteen studies were conducted in China. Twenty-five studies were at high risk of bias. The pooled AUC for 14 training models was 0.794 (95% CI: 0.747-0.840). For 17 validation models, the pooled AUC was 0.726 (95% CI: 0.659-0.792). Clinical features + EEG outperformed combinations with MRI in training (0.855 vs 0.725) and validation (0.743 vs 0.655). Non-machine learning models surpassed machine learning (training: 0.838 vs 0.717; validation: 0.778 vs 0.654), but the difference might not be statistically significant as the 95% CIs are overlapped in the validation; and external validation yielded higher AUC (0.807) than internal validation (0.634), though with extreme heterogeneity (I Interpretation: Current research showed uneven regional distribution. Models based on clinical features + EEG warrants further exploration. Predictor selection predominantly relies on univariate analysis, lacking standardized and scientific methodologies. Most studies carry a high risk of bias and rarely undergo validation, limiting their practical applicability. Validating existing models is crucial for identifying flaws and enhancing future research. Funding: Natural Science Foundation of Hunan Province (grant No. 2024JJ8254).