Does Quality of Life (QOL) Predict Morbidity or Mortality in Patients with Atrial Fibrillation (AF)?
Thomas, Sue A.
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Purpose: To examine the contributions of quality of life to one year hospitalization and mortality in patients with AF. Methods: This study used the public access data from the Atrial Fibrillation Follow up Investigation of Rhythm Management (AFFIRM) randomized clinical trial conducted by the NIH, National Heart, Lung, and Blood Institute. Patients enrolled in the QOL substudy (N= 693) were randomly assigned to rate control or rhythm control groups. QOL was measured with the Medical Outcomes Study Short Form-36 health survey (SF-36) and the Quality of Life Index-Cardiac Version (QLI-CV). Data were analyzed with descriptive statistics, logistic regression to predict one year hospitalization or death and Cox proportional hazards analysis to predict mortality. Results: In the first year 62.2% (n=427) were hospitalized or died; overall, mortality was 14.3% (n=93) with mean follow-up of 3.5 years. Patients had a mean age of 69.8 +/- 8.2 years, were largely male (62%), and white (93%). At baseline 70.8% of the patients had a history of hypertension, 38.2 % coronary artery disease (CAD), and 23.7% heart failure (HF). Clinical, demographic, and QOL variables predicted morbidity and mortality. A history of HF, stroke, rhythm control arm, lower mental component score (MCS) and physical component scores (PCS) predicted morbidity (< .001). QOL predicted morbidity beyond clinical and demographic variables. Patients with a history of CAD, hypertension, older age, lower PCS, and women with diabetes had a higher risk of mortality (p <.001). QOL predicted mortality beyond clinical and demographic variables. Conclusion: This study supported the bio-psycho-social model implied contributions of QOL variables to morbidity and mortality in patients treated for AF. Interventions for improving QOL and helping patients adapt to treatments for AF may decrease morbidity and improve survival. Psychosocial variables add meaningful information beyond traditional biomedical factors to the prediction of mortality and/or morbidity of patients with AF.