Multivariable prediction models for incident heart failure demonstrated excellent discrimination, including the PCP-HF white women model (c-statistic 0.852; 95% CI 0.804-0.895) and ARIC score (0.802).
Meta-Analysis
Do multivariable prediction models accurately estimate the risk of incident heart failure in the general population?
Prediction models for incident heart failure show excellent discrimination, but their clinical utility remains uncertain due to high risk of bias and lack of clinical impact studies.
Abstract Aims Multivariable prediction models can be used to estimate risk of incident heart failure (HF) in the general population. A systematic review and meta-analysis was performed to determine the performance of models. Methods and results From inception to 3 November 2022 MEDLINE and EMBASE databases were searched for studies of multivariable models derived, validated and/or augmented for HF prediction in community-based cohorts. Discrimination measures for models with c-statistic data from ≥3 cohorts were pooled by Bayesian meta-analysis, with heterogeneity assessed through a 95% prediction interval (PI). Risk of bias was assessed using PROBAST. We included 36 studies with 59 prediction models. In meta-analysis, the Atherosclerosis Risk in Communities (ARIC) risk score (summary c-statistic 0.802, 95% confidence interval CI 0.707–0.883), GRaph-based Attention Model (GRAM; 0.791, 95% CI 0.677–0.885), Pooled Cohort equations to Prevent Heart Failure (PCP-HF) white men model (0.820, 95% CI 0.792–0.843), PCP-HF white women model (0.852, 95% CI 0.804–0.895), and REverse Time AttentIoN model (RETAIN; 0.839, 95% CI 0.748–0.916) had a statistically significant 95% PI and excellent discrimination performance. The ARIC risk score and PCP-HF models had significant summary discrimination among cohorts with a uniform prediction window. 77% of model results were at high risk of bias, certainty of evidence was low, and no model had a clinical impact study. Conclusions Prediction models for estimating risk of incident HF in the community demonstrate excellent discrimination performance. Their usefulness remains uncertain due to high risk of bias, low certainty of evidence, and absence of clinical effectiveness research.
Nadarajah et al. (Wed,) conducted a meta-analysis in Incident heart failure. Multivariable prediction models was evaluated on Discrimination performance (c-statistic). Multivariable prediction models for incident heart failure demonstrated excellent discrimination, including the PCP-HF white women model (c-statistic 0.852; 95% CI 0.804-0.895) and ARIC score (0.802).