Predictors of incident heart failure in patients after an acute coronary syndrome: The LIPID heart failure risk-prediction model
Introduction
Heart failure (HF) continues to be associated with a high burden of disease, with mortality and hospital discharge rates remaining unchanged over the past decade [1]. Identification of patients at high risk of developing HF has the potential to reduce its incidence and its clinical consequences. HF commonly develops in patients with known coronary heart disease (CHD). In patients with risk factors for CHD events or those who have had a myocardial infarction, various studies suggest that the incidence of HF is reduced with angiotensin-converting enzyme inhibitors [2]. Previous studies have developed risk-prediction models for incident HF with varying success [3], [4], [5], [6], [7]. Most of these models were developed before awareness of the potential prognostic role of novel biomarkers. Although some models have incorporated biomarkers [8], [9], [10], [11], [12], [13], the potential utility of a panel of biomarkers reflecting the range of pathophysiological processes involved in atherosclerosis and HF in patients with stable CHD has not been well established. We aimed to develop a risk-prediction model for incident HF that included clinical parameters and biomarkers from patients with stable CHD participating in the Long-Term Intervention with Pravastatin in Ischaemic Heart Disease (LIPID) study. We also investigated the association between pravastatin treatment and the development of HF.
Section snippets
Study population
The LIPID study enrolled 9014 patients aged 31–75 years with a history of acute myocardial infarction or hospitalization with unstable angina three to 36 months before recruitment into the study [14]. Patients were prospectively recruited from 87 centres throughout Australia and New Zealand between June 1990 and December 1992. Inclusion criteria included total cholesterol 4–7 mmol/L (155–271 mg/dL) and fasting triglyceride < 5 mmol/L (455 mg/dL). Exclusion criteria in the original study consisted of
Patient characteristics
Among the 7101 patients (84% male) with biomarkers available, the median age was 61 years (IQR 55–67 years) (Table 1). In patients who experienced a HF event, there was a higher proportion of cardiovascular comorbidities at baseline: more than one previous myocardial infarction, diabetes mellitus, hypertension, claudication, stroke, or transient ischemic event, and they had a higher body mass index (Table 1). There was a difference between groups in the rate of aspirin prescribing (86% of
Discussion
We identified nine clinical parameters and five biomarkers that were significantly associated with a future HF event in stable CHD patients (Fig. 1). The strongest predictors were: diabetes, BNP > 50.29 ng/L, LDL-cholesterol, and age (> 70 years). Coronary revascularization was associated with a lower risk of a HF event. These observations were irrespective of randomization to pravastatin or placebo. Importantly, we showed that the addition of multiple biomarkers improved the model discrimination,
Conclusion
The early identification of stable CHD patients at risk of developing HF enables the initiation of preventive therapy to slow its progression. The LIPID risk model highlights the importance of a multifactorial approach to risk reduction and provides guidance for clinicians needing to identify patients at high risk of developing HF who may require escalation of their clinical management to lower their risk.
Funding sources
Andrea Driscoll was supported by an Early Career Training Fellowship 546250 from the National Health and Medical Research Council of Australia and a Heart Foundation Future Leader fellowship 100472 from the National Heart Foundation of Australia. The LIPID study was previously supported by a grant from the Bristol-Myers Squibb Pharmaceutical Research Institute and conducted under the auspices of the National Heart Foundation of Australia. This biomarker research was also supported by project
Disclosures
AT has received research funding or honoraria for lectures or consulting from Amgen, AstraZeneca, Boehringer Ingelheim, Genzyme, Merck Sharpe and Dohme, Pfizer and Sanofi–Aventis/Regeneron; SB has received research funding from Boehringer Ingelheim,Bayer, Abbott Diagnostics, SIEMENS, Thermo Fisher and Roche Diagnostics and received honoraria for lectures or consulting from Boehringer Ingelheim, Bayer, Roche, AstraZeneca, SIEMENS, Thermo Fisher and Abbott Diagnostics; HW has received research
Acknowledgments
DiaDEXUS provided materials for the assay of Lp-PLA2 activity.
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