title: A personalized BEST: characterization of latent clinical classes of nonischemic heart failure that predict outcomes and response to bucindolol.
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 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.
authors.affiliation: Division of Cardiology, University of Colorado School of Medicine, Aurora, CO, USA. David.Kao@ucdenver.edu
authors.forename: David P
authors.initials: DP
authors.lastname: Kao
authors.forename: Brandie D
authors.initials: BD
authors.lastname: Wagner
authors.forename: Alastair D
authors.initials: AD
authors.lastname: Robertson
authors.forename: Michael R
authors.initials: MR
authors.lastname: Bristow
authors.forename: Brian D
authors.initials: BD
authors.lastname: Lowes
mesh_terms: D000319:Adrenergic beta-Antagonists / Q000627:therapeutic use*; D000328:Adult; D019540:Area Under Curve; D001499:Bayes Theorem; D004311:Double-Blind Method; D005260:Female; D006333:Heart Failure / Q000188:drug therapy / Q000401:mortality / Q000503:physiopathology*; D006801:Humans; D053208:Kaplan-Meier Estimate; D016015:Logistic Models; D008297:Male; D008875:Middle Aged; D008954:Models, Biological; D015999:Multivariate Analysis; D011412:Propanolamines / Q000627:therapeutic use*; D016032:Randomized Controlled Trials as Topic; D012189:Retrospective Studies; D013318:Stroke Volume; D016896:Treatment Outcome; D016277:Ventricular Function, Left / Q000187:drug effects
chemical_list: D000319:Adrenergic beta-Antagonists; D011412:Propanolamines; C024307:bucindolol