Key result
Latent class analysis identified distinct clinical subtypes of nonischemic heart failure that predicted variable responses to bucindolol, such as a 48% mortality reduction (HR 0.52) in subtype A6.
Why the study?
Does latent class analysis identify subtypes of nonischemic HFrEF that predict mortality, LVEF improvement, and response to bucindolol?
Does latent class analysis identify subtypes of nonischemic HFrEF that predict mortality, LVEF improvement, and response to bucindolol?
Effect estimate: HR 0.82 (95% CI 0.65-1.04)
Absolute Event Rate: 23.5% vs 26.8%
p-value: p=0.10
High-dimensional phenotyping and latent class analysis can identify distinct subtypes of nonischemic HFrEF that predict prognosis and differential response to beta-blocker therapy.
Hypothesis-generating for subtype-specific bucindolol effects in nonischemic HFrEF; leaves open prospective validation before clinical use.
BACKGROUND: Heart failure patients with reduced ejection fraction (HFREF) are heterogenous, and our ability to identify patients likely to respond to therapy is limited. We present a method of identifying disease subtypes using high-dimensional clinical phenotyping and latent class analysis that may be useful in personalizing prognosis and treatment in HFREF. METHODS: A total of 1121 patients with nonischemic HFREF from the β-blocker Evaluation of Survival Trial were categorized according to 27 clinical features. Latent class analysis was used to generate two latent class models, LCM A and B, to identify HFREF subtypes. LCM A consisted of features associated with HF pathogenesis, whereas LCM B consisted of markers of HF progression and severity. The Seattle Heart Failure Model (SHFM) Score was also calculated for all patients. Mortality, improvement in left ventricular ejection fraction (LVEF) defined as an increase in LVEF ≥5% and a final LVEF of 35% after 12 months, and effect of bucindolol on both outcomes were compared across HFREF subtypes. Performance of models that included a combination of LCM subtypes and SHFM scores towards predicting mortality and LVEF response was estimated and subsequently validated using leave-one-out cross-validation and data from the Multicenter Oral Carvedilol Heart Failure Assessment Trial. RESULTS: A total of 6 subtypes were identified using LCM A and 5 subtypes using LCM B. Several subtypes resembled familiar clinical phenotypes. Prognosis, improvement in LVEF, and the effect of bucindolol treatment differed significantly between subtypes. Prediction improved with addition of both latent class models to SHFM for both 1-year mortality and LVEF response outcomes. CONCLUSIONS: The combination of high-dimensional phenotyping and latent class analysis identifies subtypes of HFREF with implications for prognosis and response to specific therapies that may provide insight into mechanisms of disease. These subtypes may facilitate development of personalized treatment plans.
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Kao et al. (2012) studied Nonischemic Heart Failure with Reduced Ejection Fraction (HFREF) (n=1,121). Bucindolol vs. Placebo was evaluated on Cumulative all-cause mortality (HR 0.82, 95% CI 0.65-1.04, p=0.10). Latent class analysis identified distinct clinical subtypes of nonischemic heart failure that predicted variable responses to bucindolol, such as a 48% mortality reduction (HR 0.52) in subtype A6.
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