Symptom cluster profiles predict all-cause mortality among older adults with heart failure

Author:

Wang Zequan1,Walsh Stephen1,Jeon Sangchoon2,Conley Samantha3,Chyun Deborah1,Redeker Nancy1

Affiliation:

1. University of Connecticut

2. Yale University

3. Mayo Clinic

Abstract

Abstract

Background Heart failure (HF) has a high mortality risk in older adults. Individual symptoms as predictors of mortality in HF patients; however, symptoms often manifest in clusters, which may be more predictive of future risks than isolated symptoms. However, research on symptom clusters in older adults who have HF is limited. To explore the extent to which symptom cluster profiles predict all-cause mortality among older adults with HF, while adjusting for demographic and clinical factors. Methods A secondary study was conducted using the data from the Health and Retirement Study. We measured six symptoms (fatigue, shortness of breath, pain, swelling, depressive symptoms, and dizziness), and used latent class analysis to identify baseline symptom cluster profile. We performed survival analysis for time to death with Kaplan Meier survival analyses and Cox Proportional Hazard models. Results The sample included 684 participants (mean age = 74.9 (SD = 10.0) years) who demonstrated three symptom cluster profiles (high-burden, low-burden, and cardiopulmonary-depressive). The estimated median time-to-death was 71 (95% CI= [64, 79]) months. Participants in the high symptom burden and respiratory-depressive distress profiles had adjusted hazard ratios of 1.48 (95% CI = 1.15, 1.94) and 1.44 (95% CI = 1.14, 1.80) for time to death compared to those in the low burden profile. Conclusion Symptom profiles can assist in identifying older adults with HF who are at risk for earlier mortality. Further research is needed to determine whether alleviating these symptom clusters decreases the risk of mortality.

Publisher

Research Square Platform LLC

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