In:
Frontiers in Cardiovascular Medicine, Frontiers Media SA, Vol. 9 ( 2022-12-21)
Abstract:
There are multiple predictive factors for cardiovascular (CV) mortality measured at, or after heart failure (HF) diagnosis. However, the predictive role of long-term exposure to these predictors prior to HF diagnosis is unknown. Objectives We aim to identify predictive factors of CV mortality in participants with HF, using cumulative exposure to risk factors before HF development. Methods Participants of Multi-Ethnic Study of Atherosclerosis (MESA) with incident HF were included. We used stepwise Akaike Information Criterion to select CV mortality predictors among clinical, biochemical, and imaging markers collected prior to HF. Using the AUC of B-spline-corrected curves, we estimated cumulative exposure to predictive factors from baseline to the last exam before HF. The prognostic performance for CV mortality after HF was evaluated using competing risk regression with non-CV mortality as the competing risk. Results Overall, 375 participants had new HF events (42.9% female, mean age: 74). Over an average follow-up of 4.7 years, there was no difference in the hazard of CV death for HF with reduced versus preserved ejection fraction (HR = 1.27, p = 0.23). The selected predictors of CV mortality in models with the least prediction error were age, cardiac arrest, myocardial infarction, and diabetes, QRS duration, HDL, cumulative exposure to total cholesterol and glucose, NT-proBNP, left ventricular mass, and statin use. The AUC of the models were 0.72 when including the latest exposure to predictive factors and 0.79 when including cumulative prior exposure to predictive factors ( p = 0.20). Conclusion In HF patients, besides age and diagnosed diabetes or CVD, prior lipid profile, NT-proBNP, LV mass, and QRS duration available at the diagnosis time strongly predict CV mortality. Implementing cumulative exposure to cholesterol and glucose, instead of latest measures, improves predictive accuracy for HF mortality, though not reaching statistical significance.
Type of Medium:
Online Resource
ISSN:
2297-055X
DOI:
10.3389/fcvm.2022.1024031
DOI:
10.3389/fcvm.2022.1024031.s001
Language:
Unknown
Publisher:
Frontiers Media SA
Publication Date:
2022
detail.hit.zdb_id:
2781496-8
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