Why the study?
Does a risk prediction model based on routinely available clinical measurements reliably estimate short-term incident heart failure risk?
Does a risk prediction model based on routinely available clinical measurements reliably estimate short-term incident heart failure risk?
A subject-level meta-analysis developed a risk prediction model using simple clinical measurements to reliably estimate short-term incident heart failure risk.
May support short-term HF risk estimation with routine data in research settings; leaves open external validation before clinical adoption.
BACKGROUND: To address the need for personalized prevention, we conducted a subject-level meta-analysis within the framework of the Heart "OMics" in AGEing (HOMAGE) study to develop a risk prediction model for heart failure (HF) based on routinely available clinical measurements. METHODS AND RESULTS: =0.54). A web-based calculator was developed to allow easy calculations of the HF risk. CONCLUSIONS: Simple measurements allow reliable estimation of the short-term HF risk in populations and patients. The risk model may aid in risk stratification and future HF prevention strategies.
No takes yet. Share an insight, caveat, or question.
Jacobs et al. (2017) studied this question.
Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context: