Symptom Clusters Using Edmonton Symptom Assessment System in Radiotherapy and Palliative Care Outpatient Clinic

Background/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). 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. Principal component analysis (PCA) and unsupervised K-means clustering (KMC) were used to identify SCs. Associations with ECOG performance status (PS), primary tumor site, 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 ± 15.28. PCA identified four SCs: SCPCA1 (tiredness, drowsiness, dyspnea, malaise), SCPCA2 (depression, anxiety), SCPCA3 (nausea, loss of appetite) and SCPCA4 (pain). KMC revealed three SCs: SCKMC1 (pain, tiredness, drowsiness, malaise), SCKMC2 (nausea, loss of appetite, dyspnea), SCKMC3 (depression, anxiety). Worse ECOG PS correlated significantly with physical SCs (p < 0.05). A trend linked SCKMC1 with greater PRT use. Psychological SCs (SCPCA2, SCKMC3) were significantly associated with a lower likelihood of receiving PRT. A trend toward shorter survival was observed among patients belonging to SCKMC2. Conclusions: SC analysis could improve clinical decision-making in the PRT setting. SC profiles reflect patient complexity and may guide personalized symptom management and treatment selection in advanced cancer.