Physicians' Academy for Cardiovascular Education

New risk prediction models according to heart failure type

Ho JE et al., Circ Heart Fail. 2016

Predicting Heart Failure With Preserved and Reduced Ejection Fraction
The International Collaboration on Heart Failure Subtypes

 
Ho JE, Enserro D, Brouwers FP, et al.
Circ Heart Fail. 2016;9:e003116
 

Background

The predicted prevalence of heart failure (HF) approaches 20% of the general population worldwide, and the projected relevant medical costs are expect to double within the next 20 years [1]. Therefore, it is argued that primary prevention of HF should actively focus on high-risk patients [2], which in turn highlights the importance of the accuracy of HF risk prediction models.
 
Some HF risk prediction models do not take into account HF subtypes, and most of them lack external validation [3]. The differentiation between HF subtypes, namely HF with preserved ejection fraction (HFpEF) and HF with reduced ejection fraction (HFrEF), in HF risk prediction models is important, since different phenotypes, distinct causes, and individual therapeutic approaches have been reported for each type [4,5]. Hence, it is hypothesised that there are also different risk predictors for HFpEF and HFrEF [6,7].
 
In this study, specific risk profiles by HF subtype were evaluated, in 4 longitudinal community-based cohorts with 22,142 participants, based on which, separate risk prediction models for HFpEF and HFrEF were developed and validated. The Framingham Heart Study (FHS) [8,9], Cardiovascular Health Study (CHS) [10] and Prevention of Renal and Vascular End-stage Disease (PREVEND) [11] were used for derivation and internal validation and the Multi-Ethnic Study of Atherosclerosis (MESA) [12] was used for external validation.
 

Main results

The final HFpEF-specific risk model included age, sex, systolic blood pressure (SBP), body mass index (BMI), antihypertensive treatment, and previous myocardial infarct (MI).
The relative risk of HFpEF increased by:
  • 90% per 10 years of age (HR: 1.90; 95% CI: 1.74–2.07)
  • 14% per 20 mm Hg SBP (HR: 1.14; 95% CI: 1.05–1.24)
  • 28% per 4 kg/m2 BMI (HR: 1.28; 95% CI: 1.21–1.37)
  • 42% if taking antihypertensive treatment (HR: 1.42; 95% CI: 1.18–1.71)
  • 48% with previous MI (HR: 1.48; 95% CI: 1.12–1.96)
The HFrEF-specific multivariable risk model included age, sex, SBP, BMI, smoking status, antihypertensive treatment, left ventricle hypertrophy, left bundle branch block, diabetes mellitus, and previous MI.
The relative risk of HFrEF increased by:
  • 66% per 10 years of age (HR: 1.66; 95% CI: 1.52–1.80)
  • 84% for men (HR: 1.84; 95% CI: 1.55–2.19)
  • 20% per 20 mm Hg SBP (HR: 1.20; 95% CI: 1.10–1.30)
  • 19% per 4 kg/m2 BMI (HR: 1.19; 95% CI: 1.11–1.28)
  • 41% in current smokers (HR: 1.41; 95% CI: 1.14–1.75)
  • 35% if taking antihypertensive treatment (HR: 1.35; 95% CI: 1.13–1.63)
  • 112% in presence of ECG LV hypertrophy (HR: 2.12; 95% CI: 1.55–2.90)
  • 217% in presence of left bundle branch block (HR: 3.17; 95% CI: 2.11–4.78)
  • 83% with diabetes mellitus (HR: 1.83; 95% CI: 1.48–2.26)
  • 160% with previous MI (HR: 2.60; 95% CI: 2.08–3.25)
Internal performance metrics and validation of HF subtype–specific risk models:
  • c-statistic for the HFpEF model in the derivation sample: 0.80; 95% CI: 0.78–0.82
  • c-statistic for the HFpEF model in the validation sample: 0.79; 95% CI: 0.77–0.82
  • c-statistic for the HFrEF model in the derivation sample: 0.82; 95% CI: 0.80–0.84
  • c-statistic for the HFrEF model in the validation sample: 0.80; 95% CI: 0.78–0.83
External validation of HF subtype–specific risk models using the MESA cohort:
  • c-statistic of HFpEF model: 0.76; 95% CI: 0.71–0.80
  • c-statistic of HFrEF model: 0.76; 95% CI: 0.71–0.80
Differential effects of predictors on HFpEF versus HFrEF:
  • men had a higher risk than women for HFrEF, but not for HFpEF (P for comparison <0.0001)
  • left bundle branch block and previous MI increased the risk more strongly for HFrEF than for HFpEF (P for comparison ≤0.0008 for both)
  • age seemed to have a greater risk associated with HFpEF than HFrEF
  • smoking status and LV hypertrophy were more strongly associated with HFrEF than HFpEF (P for comparison ≤0.02 for all)

Conclusion

Data from 4 cohorts were used to create and validate separate risk prediction models for HFpEF and HFrEF. Many risk factors were in common, however, it was possible to identify specific risk factors for each HF type. These findings may be useful to identify individuals at risk for either HFrEF or HFpEF, and support the selection of appropriate prevention and treatment strategies.
 
Find this article online at Circ Heart Fail
 

References

1. McMurray JJ, Petrie MC, Murdoch DR, et al. Clinical epidemiology of heart failure: public and private health burden. Eur Heart J.1998;19(suppl P):P9–16.
2. Schocken DD, Benjamin EJ, Fonarow GC, et al; American Heart Association Council on Epidemiology and Prevention; American Heart Association Council on Clinical Cardiology; American Heart Association Council on Cardiovascular Nursing; American Heart Association Council on High Blood Pressure Research; Quality of Care and Outcomes Research Interdisciplinary Working Group; Functional Genomics and Translational Biology Interdisciplinary Working Group. Prevention of heart failure: a scientific statement from the American Heart Association Councils on Epidemiology and Prevention, Clinical Cardiology, Cardiovascular Nursing, and High Blood Pressure Research; Quality of Care and Outcomes Research Interdisciplinary Working Group; and Functional Genomics and Translational Biology Interdisciplinary Working Group. Circulation. 2008;117:2544–2565.
3. Echouffo-Tcheugui JB, Greene SJ, Papadimitriou L, et al. Population risk prediction models for incident heart failure: a systematic review. Circ Heart Fail. 2015;8:438–447.
4. McMurray JJ, Adamopoulos S, Anker SD, et al; ESC Committee for Practice Guidelines. ESC guidelines for the diagnosis and treatment of acute and chronic heart failure 2012: The Task Force for the Diagnosis and Treatment of Acute and Chronic Heart Failure 2012 of the European Society of Cardiology. Developed in collaboration with the Heart Failure Association (HFA) of the ESC. Eur Heart J. 2012;33:1787–1847.
5. Borlaug BA, Redfield MM. Diastolic and systolic heart failure are distinct phenotypes within the heart failure spectrum. Circulation. 2011;123:2006–13; discussion 2014.
6. Ho JE, Gona P, Pencina MJ, et al. Discriminating clinical features of heart failure with preserved vs. reduced ejection fraction in the community. Eur Heart J. 2012;33:1734–1741.
7. Ho JE, Lyass A, Lee DS, et al. Predictors of new-onset heart failure: differences in preserved versus reduced ejection fraction. Circ Heart Fail. 2013;6:279–286.
8. Dawber TR, Kannel WB, Lyell LP. An approach to longitudinal studies in a community: the Framingham Study. Ann NY Acad Sci.1963;107:539–556.
9. Kannel WB, Feinleib M, McNamara PM, et al. An investigation of coronary heart disease in families. The Framingham offspring study. Am J Epidemiol. 1979;110:281–290.
10. Psaty BM, Kuller LH, Bild D, et al. Methods of assessing prevalent cardiovascular disease in the Cardiovascular Health Study. Ann Epidemiol.1995;5:270–277.
11. Diercks GF, Janssen WM, van Boven AJ, et al. Rationale, design, and baseline characteristics of a trial of prevention of cardiovascular and renal disease with fosinopril and pravastatin in nonhypertensive, nonhypercholesterolemic subjects with microalbuminuria (the Prevention of REnal and Vascular ENdstage Disease Intervention Trial [PREVEND IT]). Am J Cardiol.2000;86:635–638.
12. Bild DE, Bluemke DA, Burke GL, et al. Multi-ethnic study of atherosclerosis: objectives and design. Am J Epidemiol. 2002;156:871–881.