A novel symptom cluster analysis among ambulatory HIV/AIDS patients in Uganda

Abstract

Symptom clusters are gaining importance given HIV/AIDS patients experience multiple, concurrent symptoms. This study aimed to: determine clusters of patients with similar symptom combinations; describe symptom combinations distinguishing the clusters; and evaluate the clusters regarding patient socio-demographic, disease and treatment characteristics, quality of life (QOL) and functional performance. This was a cross-sectional study of 302 adult HIV/AIDS outpatients consecutively recruited at two teaching and referral hospitals in Uganda. Socio-demographic and seven-day period symptom prevalence and distress data were self-reported using the Memorial Symptom Assessment Schedule. QOL was assessed using the Medical Outcome Scale and functional performance using the Karnofsky Performance Scale. Symptom clusters were established using hierarchical cluster analysis with squared Euclidean distances using Ward’s clustering methods based on symptom occurrence. Analysis of variance compared clusters on mean QOL and functional performance scores. Patient subgroups were categorised based on symptom occurrence rates. Five symptom occurrence clusters were identified: Cluster 1 (n = 107), high–low for sensory discomfort and eating difficulties symptoms; Cluster 2 (n = 47), high–low for psycho-gastrointestinal symptoms; Cluster 3 (n = 71), high for pain and sensory disturbance symptoms; Cluster 4 (n = 35), all high for general HIV/AIDS symptoms; and Cluster 5 (n = 48), all low for mood-cognitive symptoms. The all high occurrence cluster was associated with worst functional status, poorest QOL scores and highest symptom-associated distress. Use of antiretroviral therapy was associated with all high symptom occurrence rate (Fisher’s exact = 4, P < 0.001). CD4 count group below 200 was associated with the all high occurrence rate symptom cluster (Fisher’s exact = 41, P < 0.001). Symptom clusters have a differential, affect HIV/AIDS patients’ self-reported outcomes, with the subgroup experiencing high-symptom occurrence rates having a higher risk of poorer outcomes. Identification of symptom clusters could provide insights into commonly co-occurring symptoms that should be jointly targeted for management in patients with multiple complaints.