A longitudinal analysis of symptom clusters in cancer patients and their socio-demographic predictors — ASN Events

A longitudinal analysis of symptom clusters in cancer patients and their socio-demographic predictors (#263)

Amy Waller , Bejoy Thomas , Tak Fung , Linda E Carlson , Shannon L Groff , Barry D Bultz

Background: This study presents an exploratory longitudinal analysis of symptom clusters in a large sample of newly diagnosed patients attending a tertiary cancer centre.
Methods: Newly diagnosed patients completed the Distress Thermometer, Fatigue and Pain Thermometers, Canadian Problem Checklist, PSSCAN Part-C measuring anxiety and depression, and two nutrition items at baseline, 3, 6 and 12 months. A principal component analysis was carried out (controlling for the patient over time) on pain, fatigue, anxiety, depression, sleep, weight change and food intake items to identify clusters. A panel regression on each cluster explored associations with demographic and medical characteristics, and distress.
Results: 877 patients (73.3% of eligible population) provided baseline data with 505 retained at 12 months. Three clusters explained 71% of the variance. The Somatic cluster included pain, fatigue and sleep; the Psychological cluster was anxiety and depression; and the Nutrition cluster consisted of weight and food intake. Low income and treatment of radiation or chemotherapy predicted higher Somatic symptom burden. Younger age, being female, low income, and treatment with surgery predicted more Psychological symptomatology. Older age and treatment with surgery predicted higher Nutritional burden. Patients with higher Somatic, Psychological and Nutritional symptom burden reported higher distress.
Discussion: The presence of symptom clusters across the first year of diagnosis supports the need for routine and ongoing screening for the range of symptoms that may be experienced by patients. Further work is needed to develop interventions which better target individual symptoms that cluster, as well as the entire cluster itself.