Re-analysis of symptom clusters in advanced cancer patients attending a palliative outpatient radiotherapy clinic.

BACKGROUND: Cancer patients often present with several concurrent symptoms. There is evidence to suggest that related symptoms can cluster together in stable groups. The present study sought to identify symptom clusters in advanced cancer patients using the Edmonton Symptom Assessment System (ESAS) in a palliative outpatient radiotherapy clinic. METHODS: Principal component analysis (PCA), exploratory factor analysis (EFA), and hierarchical cluster analysis (HCA) were used to identify symptom clusters among the 9 ESAS items using ESAS scores from each patient's first visit. RESULTS: PCA identified three symptom clusters (cluster 1: depression, anxiety; cluster 2: nausea, dyspnea, loss of appetite; cluster 3: pain, well-being, tiredness, drowsiness). EFA identified two clusters (cluster 1: tiredness, drowsiness, loss of appetite, well-being, pain, nausea, dyspnea; cluster 2: depression, anxiety). HCA identified three symptom clusters (cluster 1: depression, anxiety, pain, well-being; cluster 2: tiredness, drowsiness, dyspnea; cluster 3: nausea, loss of appetite). CONCLUSIONS: Symptom clusters were identified using three analytical methods. The following items were always in the same cluster: depression and anxiety; nausea and appetite loss; well-being and pain; tiredness and drowsiness. Further research in symptom clusters is necessary to advance our understanding of the complex symptom interactions in advanced cancer patients and to determine the most clinically relevant symptom clusters.

Predictors of dyspnea in patients with advanced cancer.

BACKGROUND: More than 70% of patients with advanced cancer experience dyspnea. Dyspnea is predictive of shorter survival and interferes with quality of life (QOL). The present study aimed to identify predictors of the presence and severity of dyspnea in advanced cancer patients. METHODS: A prospective database collected from patients attending a palliative radiotherapy clinic was analyzed for patient demographics, Edmonton Symptom Assessment System (ESAS) scores, Patient-Reported Functional Status (PRFS), history of smoking and respiratory conditions, pulse oximetry readings, and primary cancer site. Using the ESAS shortness of breath item, dyspnea was classified as mild [1-3], moderate [4-6] or severe [7-10]. Logistic regression analysis and generalized estimating equations (GEEs) were used to identify predictors of the severity of dyspnea and presence of moderate/severe dyspnea (ESAS ≥4) at patients' first visit and over time, respectively. RESULTS: A total of 252 patients with dyspnea data were included (median age 71.3 years, 61.5% male, 44.4% had dyspnea) in a demographic analysis. Multivariable analysis showed liver metastases (P=0.01, OR =2.04), a history of respiratory conditions (P=0.03, OR =2.09) and PRFS ≥3 (P=0.03, OR =1.75) were predictive of the severity of dyspnea at the first visit. Analyzed over time, liver metastases (P=0.02, OR =1.80), lymph node metastases (P=0.02, OR =1.79), a history of respiratory conditions (P=0.006, OR =2.50) and pulse oximetry <90 (P=0.003, OR =3.32) were predictive of greater severity of dyspnea symptoms. Patients with multiple radiation treatments in the thorax region were less likely to have severe dyspnea symptoms over time (P=0.01, OR =0.32). Lung metastases (P=0.04, OR =2.03), a history of respiratory conditions (P=0.01, OR =2.60) and PRFS ≥3 (P=0.009, OR =2.30) were predictive of moderate/severe dyspnea at the first visit. Over time, lymph node metastases (P=0.003, OR =2.51), a history of respiratory conditions (P=0.04, OR =2.37) and pulse oximetry <90 (P=0.0004, OR =5.15) were predictive of moderate/severe dyspnea. CONCLUSIONS: Liver, lung and lymph node metastases, a history of respiratory conditions, pulse oximetry <90 and PRFS ≥3 were predictive of the severity of dyspnea and moderate/severe dyspnea. Physicians should be aware of predictive factors that could lead to dyspnea to promote early intervention for improved patient care and the creation of screening tools for clinical practice.

An update in symptom clusters using the Edmonton Symptom Assessment System in a palliative radiotherapy clinic.

PURPOSE: To identify symptom clusters in advanced cancer patients attending a palliative radiotherapy clinic using the Edmonton Symptom Assessment System (ESAS). METHODS: Principal component analysis (PCA), exploratory factor analysis (EFA), and hierarchical cluster analysis (HCA) were used to identify symptom clusters among the nine ESAS items using scores from each patient's first visit. RESULTS: ESAS scores from 182 patients were analyzed. The PCA identified three symptom clusters (cluster 1: depression-anxiety-well-being, cluster 2: pain-tiredness-drowsiness, cluster 3: nausea-dyspnea-loss of appetite). The EFA identified two clusters (cluster 1: tiredness-drowsiness-loss of appetite-well-being-pain-nausea-dyspnea, cluster 2: depression-anxiety). The HCA identified three clusters similar to the PCA with an exception of the loss of appetite item being classified under cluster 1 rather than 3. Two to three symptom clusters were identified using three analytical methods, with similar patterns reported in the literature. Particular groups of items co-occurred consistently across all three analyses: depression and anxiety; nausea and dyspnea; as well as pain, tiredness, and drowsiness. CONCLUSION: Three similar symptom clusters were identified in our patient population using the PCA and HCA; whereas, the EFA produced two clusters: one physical and one psychological cluster. Given the implications of symptom clusters in the management of quality of life, clinicians should be aware of these clusters to aid in the palliative treatment of patients.