Transforming hypoglycemia prediction in adult type 1 diabetes: a systematic review and meta-analysis for precision care

Abstract Type 1 diabetes mellitus (T1DM) patients require lifelong insulin therapy; however, iatrogenic hypoglycemia remains a major clinical challenge, with high incidence in adults. This study evaluated the performance, methodological rigor, and clinical utility of hypoglycemia risk prediction models for adult T1DM patients to inform evidence-based risk management strategies. Following Cochrane framework and PRISMA guidelines, 18 studies were identified. Data extraction and bias assessment were conducted using the PROBAST tool. The mean area under the curve (AUC) across individual models was 0.85. Meta-analysis of AUC values revealed a pooled AUC of 0.88 (95 % CI: 0.88–0.89), indicating moderate-to-good predictive accuracy. Substantial heterogeneity was observed (I 2 = 99.82 %, P < 0.001), mainly due to differences in prediction time windows, data sources, and validation strategies. Most studies (88.9 %) showed high or unclear risk of bias, and clinical applicability was limited, with only one study meeting criteria for low bias and high applicability. While existing models show moderate predictive performance, significant methodological limitations exist. Future research should focus on optimizing study design, conducting multi-center investigations, developing interpretable AI, standardizing validation protocols, and integrating these models into clinical practice to improve hypoglycemia management.