Symptom Clusters by Edmonton Symptom Assessment System in Radiotherapy and Palliative Care Clinic

Background and Objectives: Effective palliative care relies on accurate identification and management of symptoms, especially in patients referred for palliative radiotherapy (PRT). This study aimed to identify symptom clusters (SCs)-defined as ≥2 interrelated symptoms-in patients evaluated at a multidisciplinary Radiotherapy and Palliative Care (RaP) outpatient clinic, using the Edmonton Symptom Assessment System (ESAS). Materials and Methods: We retrospectively analyzed data from patients referred to the RaP clinic between February 2017 and April 2020. Demographic and clinical characteristics, including ESAS scores at first visit, were collected. SCs were identified with principal component analysis (PCA) and unsupervised k-means clustering (KMC), determining the number of SCs based on the maximum gap statistic and interpretability. Associations with ECOG performance status (PS), primary tumor and metastases site, and PRT administration were analyzed. Exploratory survival analyses were performed. Results: Among 215 patients (median age = 71 years; 53% male), the mean total ESAS score was 24.03 (SD = 15.28). PCA identified four SCs: SC_PCA1 (tiredness, drowsiness, dyspnea, malaise), SC_PCA2 (depression, anxiety), SC_PCA3 (nausea, loss of appetite) and SC_PCA4 (pain). KMC revealed three SCs: SC_KMC1 (pain, tiredness, drowsiness, malaise), SC_KMC2 (nausea, loss of appetite, dyspnea), and SC_KMC3 (depression, anxiety). Worse ECOG PS correlated with physical SCs (p < 0.05). Psychological SCs were associated with lower likelihood of receiving PRT (OR_PCA2 = 0.26, CI: 0.07-0.80, OR_kmc3 = 0.19, CI: 0.02-0.85, p < 0.05), but when associated with pain/systemic clusters correlated with greater PRT use. A trend toward shorter survival was seen in SC_KMC2. Conclusions: SC analysis could improve personalized symptom management and clinical decision-making in the PRT setting.