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Published on: June 10, 2025
Pre-diagnostic predictors of mortality in patients with heart failure: The multi-ethnic study of atherosclerosis
Mahsima Shabani1, Mohammad R Ostovaneh1,2, Xiaoyang Ma3
1Division of Cardiology, Department of Medicine, Johns Hopkins University, Baltimore, MD, United States.
Insights
Cumulative exposure to risk factors before heart failure (HF) diagnosis, including cholesterol and glucose, improves prediction of cardiovascular mortality. This cumulative exposure offers greater accuracy than latest measures for HF mortality prediction.
Area of Science:
- Cardiology
- Public Health
- Epidemiology
Background:
- Cardiovascular (CV) mortality prediction in heart failure (HF) often relies on factors measured after diagnosis.
- The impact of long-term exposure to these predictors before HF diagnosis remains unclear.
Purpose of the Study:
- To identify predictors of CV mortality in HF patients.
- To assess the role of cumulative exposure to risk factors prior to HF development.
Main Methods:
- Utilized data from the Multi-Ethnic Study of Atherosclerosis (MESA) cohort with incident HF.
- Employed stepwise Akaike Information Criterion to select CV mortality predictors from clinical, biochemical, and imaging markers.
- Estimated cumulative exposure to predictors using B-spline-corrected curves and evaluated prognostic performance with competing risk regression.
Main Results:
- The study included 375 participants with new HF events (mean age 74).
- Key predictors of CV mortality included age, cardiac arrest, myocardial infarction, diabetes, QRS duration, HDL, cumulative cholesterol and glucose exposure, NT-proBNP, left ventricular mass, and statin use.
- Cumulative prior exposure yielded a higher AUC (0.79) compared to latest exposure (0.72).
Conclusions:
- Age, diabetes, prior lipid profile, NT-proBNP, LV mass, and QRS duration predict CV mortality in HF patients.
- Cumulative exposure to cholesterol and glucose significantly improves HF mortality prediction accuracy compared to latest measures.
Background:
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.
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