a personalized best characterization of latent clinical classes of nonischemic heart failure that predict outcomes and response to bucindolol个性化的最佳表征潜在的临床类的非缺血型心脏病bucindolol预测结果和反应.pdfVIP

A Personalized BEST: Characterization of Latent Clinical Classes of Nonischemic Heart Failure That Predict Outcomes and Response to Bucindolol 1 2 1 1 3 David P. Kao *, Brandie D. Wagner , Alastair D. Robertson , Michael R. Bristow , Brian D. Lowes 1 Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, 2 Biostatistics and Informatics, Colorado School of Public Health, Aurora, CO, 3 Division of Cardiology, University of Nebraska Medical Center, Omaha, NE Abstract 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 b-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