Abstract
Although there is extensive literature on symptoms experienced by patients after coronary artery bypass surgery (CABS), there is a paucity of data on symptom clusters and their impact on functional outcomes. The purpose of these descriptive, secondary analyses was to identify and describe cluster subgroups of 226 elderly CABS patients at discharge and to examine the cluster's impact on psychosocial functioning over time (baseline, 6 weeks, and 3 months) using the 36-item Medical Outcome Study Short Form subscales. Cluster analysis revealed a 3-cluster solution with low, low-moderate, and moderate symptom burden clusters. Significant differences were detected for Duke Activity Status Index (F(2,223) = 5.12, P = .007), Barnason Efficacy Expectation Scale (F(2,223) = 9.60, P < .0001), Hospital Anxiety and Depression Scale (F(2,219) = 15.14, P < .0001), and New York Heart Association classification (chi = 17.44, P = .008). Significant differences were noted on all variables between the low and moderate symptom burden clusters with differences between the low-moderate and moderate clusters for only the Barnason Efficacy Expectation Scale and the Hospital Anxiety and Depression Scale. Those in the moderate symptom burden cluster had more symptoms, anxiety, and depression along with lower self-efficacy and physiological functioning than those in the other 2 clusters. There was no interaction or simple main effects for the role-emotional or social subscales as measured by the 36-item Medical Outcome Study Short Form. There was no significant interaction for mental functioning by time and cluster (F(4,641) = .30, P = .88); however, there was a simple main effect for cluster (F(2,641) = 4.11, P = .02). Follow-up analysis indicated significant differences between the low and low-moderate clusters, indicating that those with low symptom burden had significantly better mental health functioning than those with moderate symptom burden. Findings provide a foundation of knowledge on symptom clusters in CABS populations and may be useful to clinicians to identify patients at risk for slowed or delayed recovery and for early intervention.