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Identification of Symptom Clusters and Sentinel Symptoms During Radiotherapy in Lung Cancer Patients: A Longitudinal Study

Year: 2025

Type: article

Abstract: Purpose: This study longitudinally examined symptom clusters and sentinel symptoms in lung cancer patients across radiotherapy phases, providing a theoretical foundation for targeted symptom management. Methods: In this prospective longitudinal study, 244 patients were recruited via convenience sampling from a tertiary cancer hospital in Southwest China between January and December 2024. Data were collected using the General Demographic Questionnaire and MD Anderson Symptom Inventory (including the lung cancer module). Assessments occurred at four time points: T1 (one day before radiotherapy), T2 (after 10 sessions), T3 (after 20 sessions), and T4 (at end of radiotherapy). Exploratory factor analysis identified symptom clusters from symptoms with >20% incidence, while Apriori algorithm modeling analyzed intra-cluster associations to determine sentinel symptoms. Results: Five symptom clusters emerged: psychological (sadness, distress, sleep disturbance), respiratory (chest tightness, shortness of breath), lung cancer-specific (coughing, expectoration), radiotherapy side-effect (dry mouth, pain, decreased appetite), and perceptual (at T1: pain, numbness, at T2: dry mouth, pain, decreased appetite, numbness, constipation, at T3 and T4: forgetfulness, somnolence, fatigue, nausea, constipation). At T1, distress, shortness of breath, and coughing were sentinel symptoms for the psychological, respiratory, and lung cancer-specific clusters, respectively. Pain was the sentinel for the radiotherapy side-effect cluster at T3, and somnolence for the perceptual cluster at T4. Conclusion: Lung cancer patients exhibit multiple stable symptom clusters during radiotherapy, with sentinel symptoms varying by phase. These insights offer new perspectives on timed assessments and precision nursing interventions, while providing key theoretical and practical guidance for building early warning models and intelligent symptom management pathways.

Source: Cancer Management and Research

DOI: 10.2147/cmar.s536992