Impact of Comorbidity Clusters on Quality of Life in Chronic Heart Failure

Leyi Zhang,1 Yanwen Wang,2 Na Yuan,3 Yujia Zhang,1 Jingjing Yan,3 Lei Zhang,4 Wenxiao Xiong,5 Sanqiao Ma,1 Yanbo Zhang,3,6 Jing Tian5,6 1Department of Health Statistics, School of Medical Sciences, Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China; 2First Clinical Medical College, Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China; 3Department of Health Statistics, School of Public Health, Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China; 4Department of Cardiology, Shanxi Bethune Hospital, Taiyuan, Shanxi Province, People’s Republic of China; 5Department of Cardiology, First Hospital of Shanxi Medical University, Taiyuan, Shanxi Province, People’s Republic of China; 6Shanxi Provincial Key Laboratory of Biomedical Data and Statistics, Taiyuan, Shanxi Province, People’s Republic of ChinaCorrespondence: Jing Tian, Department of Cardiology, The First Hospital of Shanxi Medical University, 85 South Jiefang Road, Taiyuan, Shanxi Province, 030001, People’s Republic of China, Tel +86 351 4639114, Email [email protected] Yanbo Zhang, Department of Health Statistics, School of Public Health, Shanxi Medical University, 56 South XinJian Road, Taiyuan, Shanxi Province, 030001, People’s Republic of China, Tel +86 3543985051, Email [email protected]: Grouping comorbidities can better reflect the intrinsic relationships among complications. We aimed to explore the relationships between comorbidity groups and dynamic changes in health-related quality of life (HRQoL) in patients with chronic heart failure (CHF).Patients and Methods: A total of 1533 patients with CHF were included. Demographic and clinical variables were recorded during hospitalization. HRQoL was assessed using the Kansas City Cardiomyopathy Questionnaire (KCCQ) at baseline and at 3 and 12 months. Latent class analysis was used to identify comorbidity groups among patients with CHF. This method identifies latent subgroups by analyzing patterns in their comorbidities. Repeated-measures analysis of variance (ANOVA) was used to assess the relationships between comorbidity groups and dynamic changes in KCCQ scores.Results: Three comorbidity clusters were identified: the young/valvular heart disease (VHD) group, the elderly/coronary heart disease (CHD) group, and the high-comorbidity burden group. The young group had a relatively lower comorbidity burden and the best HRQoL, followed by the elderly/CHD group, while the high-comorbidity burden group showed the worst HRQoL. The overall summary scores, clinical summary scores, social limitations, physical limitations, and self-efficacy were significantly highest in the high-comorbidity burden group. In addition, at 3 months the symptom scores had decreased in all three groups. Repeated-measures ANOVA showed that the comorbidity groups were significantly associated with dynamic changes in HRQoL through time and group effects.Conclusion: There were significant differences in the baseline characteristics and dynamic changes of HRQoL among different comorbidity clusters. Patients in the high-comorbidity burden group performed the worst in terms of quality of life, but the improvement was the most significant. Keywords: chronic heart failure, comorbidities, latent class analysis, health-related quality of life, Kansas City Cardiomyopathy Questionnaire