Phase III study of the European Organisation for Research and Treatment of Cancer Quality of Life cancer survivorship core questionnaire.

PURPOSE: The purpose of this study is to develop a European Organisation for Research and Treatment of Cancer Quality of Life Group (EORTC QLG) questionnaire that captures the full range of physical, mental, and social health-related quality of life (HRQOL) issues relevant to disease-free cancer survivors. In this phase III study, we pretested the provisional core questionnaire (QLQ-SURV111) and aimed to identify essential and optional scales. METHODS: We pretested the QLQ-SURV111 in 492 cancer survivors from 17 countries with one of 11 cancer diagnoses. We applied the EORTC QLG decision rules and employed factor analysis and item response theory (IRT) analysis to assess and, where necessary, modify the hypothesized questionnaire scales. We calculated correlations between the survivorship scales and the QLQ-C30 summary score and carried out a Delphi survey among healthcare professionals, patient representatives, and cancer researchers to distinguish between essential and optional scales. RESULTS: Fifty-four percent of the sample was male, mean age was 60 years, and, on average, time since completion of treatment was 3.8 years. Eleven items were excluded, resulting in the QLQ-SURV100, with 12 functional and 9 symptom scales, a symptom checklist, 4 single items, and 10 conditional items. The essential survivorship scales consist of 73 items. CONCLUSIONS: The QLQ-SURV100 has been developed to assess comprehensively the HRQOL of disease-free cancer survivors. It includes essential and optional scales and will be validated further in an international phase IV study. IMPLICATIONS FOR CANCER SURVIVORS: The availability of this questionnaire will facilitate a standardized and robust assessment of the HRQOL of disease-free cancer survivors.

Symptom clusters among cancer survivors: what can machine learning techniques tell us?

PURPOSE: Knowledge regarding symptom clusters may inform targeted interventions. The current study investigated symptom clusters among cancer survivors, using machine learning techniques on a large data set. METHODS: Data consisted of self-reports of cancer survivors who used a fully automated online application 'Oncokompas' that supports them in their self-management. This is done by 1) monitoring their symptoms through patient reported outcome measures (PROMs); and 2) providing a personalized overview of supportive care options tailored to their scores, aiming to reduce symptom burden and improve health-related quality of life. In the present study, data on 26 generic symptoms (physical and psychosocial) were used. Results of the PROM of each symptom are presented to the user as a no well-being risk, moderate well-being risk, or high well-being risk score. Data of 1032 cancer survivors were analysed using Hierarchical Density-Based Spatial Clustering of Applications with Noise (HDBSCAN) on high risk scores and moderate-to-high risk scores separately. RESULTS: When analyzing the high risk scores, seven clusters were extracted: one main cluster which contained most frequently occurring physical and psychosocial symptoms, and six subclusters with different combinations of these symptoms. When analyzing moderate-to-high risk scores, three clusters were extracted: two main clusters were identified, which separated physical symptoms (and their consequences) and psycho-social symptoms, and one subcluster with only body weight issues. CONCLUSION: There appears to be an inherent difference on the co-occurrence of symptoms dependent on symptom severity. Among survivors with high risk scores, the data showed a clustering of more connections between physical and psycho-social symptoms in separate subclusters. Among survivors with moderate-to-high risk scores, we observed less connections in the clustering between physical and psycho-social symptoms.