title: Risk assessment for incident heart failure in individuals with atrial fibrillation.
abstract: BACKGROUND
  Atrial fibrillation (AF) is a strong risk factor for heart failure (HF); HF onset in patients with AF is associated with increased morbidity and mortality. Risk factors that predict HF in individuals with AF in the community are not well established.
  
  
  RESULTS
  We examined clinical variables related to the 10-year incidence of HF in 725 individuals (mean 73.3 years, 45% women) with documented AF in the Framingham Heart Study. Event rates for incident HF (n = 161, 48% in women) were comparable in women (4.30 per 100 person-years) and men (3.34 per 100 person-years). Age, body mass index, ECG LV hypertrophy, diabetes, significant murmur, and history of myocardial infarction were positively associated with incident HF in multivariable models (C-statistic 0.71; 95% confidence interval 0.67-0.75). We developed a risk algorithm for estimating absolute risk of HF in AF patients with good model fit and calibration (adjusted calibration χ2 statistic 7.29; P(χ2) = 0.61). Applying the algorithm, 47.6% of HF events occurred in the top tertile in men compared with 13.1% in the bottom tertile, and 58.4% in women in the upper tertile compared with 18.2% in the lowest category. For HF type, women had a non-significantly higher incidence of HF with preserved EF compared with men.
  
  
  CONCLUSIONS
  We describe advancing age, LV hypertrophy, body mass index, diabetes, significant heart murmur, and history of myocardial infarction as clinical predictors of incident HF in individuals with AF. A risk algorithm may help identify individuals with AF at high risk of developing HF.
authors.affiliation: NHLBI's and Boston University's Framingham Heart Study, Framingham, MA, USA. schnabelr@gmx.de
authors.forename: Renate B
authors.initials: RB
authors.lastname: Schnabel
authors.forename: Michiel
authors.initials: M
authors.lastname: Rienstra
authors.forename: Lisa M
authors.initials: LM
authors.lastname: Sullivan
authors.forename: Jenny X
authors.initials: JX
authors.lastname: Sun
authors.forename: Carlee B
authors.initials: CB
authors.lastname: Moser
authors.forename: Daniel
authors.initials: D
authors.lastname: Levy
authors.forename: Michael J
authors.initials: MJ
authors.lastname: Pencina
authors.forename: João D
authors.initials: JD
authors.lastname: Fontes
authors.forename: Jared W
authors.initials: JW
authors.lastname: Magnani
authors.forename: David D
authors.initials: DD
authors.lastname: McManus
authors.forename: Steven A
authors.initials: SA
authors.lastname: Lubitz
authors.forename: Thomas M
authors.initials: TM
authors.lastname: Tadros
authors.forename: Thomas J
authors.initials: TJ
authors.lastname: Wang
authors.forename: Patrick T
authors.initials: PT
authors.lastname: Ellinor
authors.forename: Ramachandran S
authors.initials: RS
authors.lastname: Vasan
authors.forename: Emelia J
authors.initials: EJ
authors.lastname: Benjamin
mesh_terms: D000328:Adult; D000367:Age Factors; D000368:Aged; D000369:Aged, 80 and over; D000465:Algorithms; D001281:Atrial Fibrillation / Q000453:epidemiology*; D015992:Body Mass Index; D015331:Cohort Studies; D003920:Diabetes Mellitus / Q000453:epidemiology; D005260:Female; D006333:Heart Failure / Q000453:epidemiology*; D006337:Heart Murmurs / Q000453:epidemiology; D006801:Humans; D017379:Hypertrophy, Left Ventricular / Q000453:epidemiology; D015994:Incidence; D008297:Male; D008875:Middle Aged; D015999:Multivariate Analysis; D009203:Myocardial Infarction / Q000453:epidemiology; D050177:Overweight / Q000453:epidemiology; D016016:Proportional Hazards Models; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors
keywords: Atrial fibrillation; Epidemiology; Heart failure; Risk score