Prediction models for heart failure in type 2 diabetes show acceptable discrimination but lack prospective testingNew Prediction Models Help Identify Heart Failure Risk in Diabetes

AIMS: Heart failure (HF) is a common sequela of type 2 diabetes (diabetes), and models have been developed for its prediction. We aimed to synthesize the available evidence by performing a systematic review and meta-analysis of models predicting incident HF and HF hospitalization (HFH) in individuals with diabetes. METHODS: We searched MEDLINE and EMBASE for multivariable models predicting HF or HFH in patients with diabetes from inception to 2 December 2025. Discrimination metrics from models validated in ≥3 cohorts were pooled using Bayesian meta-analysis. Heterogeneity was assessed using 95% prediction intervals (PI), and risk of bias with PROBAST. RESULTS: In total, 65 studies describing 56 HF and 44 HFH prediction models were included. Following exclusion of studies at high risk of bias, two HF models (RECODe (0.711, 95% CI 0.651-0.767) and DMRS (0.758, 95% CI 0.684-0.827)) and two HFH models (WATCH-DM 2022 (r) (0.705, 95% CI, 0.624-0.795), WATCH-DM 2022(i) (0.718, 95% CI 0.587-0.850)) had acceptable prediction performance, while one HFH model had good performance (DM-CURE (0.837, 95% CI 0.757-0.913). Of these, none reported a prediction horizon of less than ten years. For all models, regardless of risk of bias, none were prospectively tested, and only three underwent clinical utility analysis. CONCLUSION: While some HF and HFH models in diabetes show acceptable/good long-term discriminative performance, the scarcity of utility analyses and prospective testing limits translation to clinical settings. Prospective evaluation of these models is required to establish clinical utility.