BACKGROUND:Poor health-related quality of life (QOL) is related to morbidity and mortality in coronary heart disease and ventricular arrhythmias as well as to mortality in patients with heart failure (HF) and atrial fibrillation (AF). This study examined the contributions of QOL to the prediction of 1-year hospitalization and mortality in patients with AF, independent of HF. METHODS: This study used the public use dataset from the NHLBI/NIH AFFIRM randomized clinical trial. Patients enrolled in the QOL substudy (N = 693) were randomly assigned to rate or rhythm control. QOL was assessed with the Medical Outcomes Study 36-item Short Form Health Survey (SF-36) and the Quality of Life Index-Cardiac Version (QLI-CV). Data were analyzed with logistic regression to predict 1-year hospitalization and Cox proportional hazards analysis to predict mortality. RESULTS: In the first year of participation in the study 37% (n = 256) were hospitalized; mortality was 14.3% (n = 93) with mean follow-up of 3.5 years. Patients' mean age was 69.8 ± 8.2 years, were largely male (62%), and white (93%). Patient histories included 70.8% hypertension, 38.2% coronary artery disease (CAD), and 23.7% HF. History of stroke, HF, rhythm control arm, lower SF-36 mental component scores (MCS), and lower SF-36 physical component scores (PCS) predicted hospitalization (P < 0.001). Diabetes, female gender, older age, CAD, hypertension, and lower PCS predicted mortality (P < 0.001). CONCLUSION:QOL adds meaningful information beyond traditional biomedical factors to the prediction of mortality and/or hospitalization of patients with AF. Interventions for improving QOL and helping patients adapt to AF treatments may decrease hospitalization and improve survival.
RCT Entities:
BACKGROUND: Poor health-related quality of life (QOL) is related to morbidity and mortality in coronary heart disease and ventricular arrhythmias as well as to mortality in patients with heart failure (HF) and atrial fibrillation (AF). This study examined the contributions of QOL to the prediction of 1-year hospitalization and mortality in patients with AF, independent of HF. METHODS: This study used the public use dataset from the NHLBI/NIH AFFIRM randomized clinical trial. Patients enrolled in the QOL substudy (N = 693) were randomly assigned to rate or rhythm control. QOL was assessed with the Medical Outcomes Study 36-item Short Form Health Survey (SF-36) and the Quality of Life Index-Cardiac Version (QLI-CV). Data were analyzed with logistic regression to predict 1-year hospitalization and Cox proportional hazards analysis to predict mortality. RESULTS: In the first year of participation in the study 37% (n = 256) were hospitalized; mortality was 14.3% (n = 93) with mean follow-up of 3.5 years. Patients' mean age was 69.8 ± 8.2 years, were largely male (62%), and white (93%). Patient histories included 70.8% hypertension, 38.2% coronary artery disease (CAD), and 23.7% HF. History of stroke, HF, rhythm control arm, lower SF-36 mental component scores (MCS), and lower SF-36 physical component scores (PCS) predicted hospitalization (P < 0.001). Diabetes, female gender, older age, CAD, hypertension, and lower PCS predicted mortality (P < 0.001). CONCLUSION: QOL adds meaningful information beyond traditional biomedical factors to the prediction of mortality and/or hospitalization of patients with AF. Interventions for improving QOL and helping patients adapt to AF treatments may decrease hospitalization and improve survival.
Keywords:
Mortality; atrial fibrillation; hospitalization; medical outcomes study (SF-36); quality of life; quality of life index-cardiac version (QLI-CV); survival
Authors: Leonard Emuren; Seth Welles; Marcia Polansky; Alison A Evans; Grace Macalino; Brian K Agan Journal: Health Qual Life Outcomes Date: 2018-05-30 Impact factor: 3.186
Authors: I M Blok; A C M J van Riel; M J Schuuring; M G Duffels; J C Vis; A P J van Dijk; E S Hoendermis; B J M Mulder; B J Bouma Journal: Neth Heart J Date: 2015-05 Impact factor: 2.380
Authors: S Lane Slabaugh; Mona Shah; Matthew Zack; Laura Happe; Tristan Cordier; Eric Havens; Evan Davidson; Michael Miao; Todd Prewitt; Haomiao Jia Journal: Popul Health Manag Date: 2016-03-31 Impact factor: 2.459