P078: Predicting the Health-Related Quality of Life of Hodgkin Lymphoma Survivors: Identification of Risk Factors

Abstract

Introduction: Cancer survivors are at risk of long-term impairment of their health-related quality of life (HRQoL), persisting for years after treatment. Despite the clinical importance, research is scarce and the etiology is complex. The aim of this study is to develop prediction models for different HRQoL domains of Hodgkin Lymphoma (HL) survivors. Therefore, we identify risk factors at the time of cancer diagnosis, as well as during and after therapy. Methods: Data of N=4981 patients from diagnosis to year five of survival of the fifth study generation (HD13-15) of the German Hodgkin Study Group was analyzed. Parametric (forward stepwise regression, lasso, and all-pairs lasso) and non-parametric (bagged and boosted regression trees) machine learning algorithms were investigated to identify the relevant risk factors out of a set of 61 potential demographic, physical and psychosocial predictors. HRQoL was measured with the EORTC QLQ-C30. Assessed HRQoL domains were overall HRQoL, cancer-related fatigue, employment, and financial status, and physical, emotional, cognitive, social, and role functioning. Results: The all-pairs lasso algorithm performed best at detecting the relevant predictors in comparison to the other methods tested. All-pairs lasso identified between six and 17 risk factors for each HRQoL domain. The risk factors included patients’ age, prior HRQoL levels, specific physical risk factors, and the cancer stage. Based on the variable selection we estimated regression models to predict each HRQoL domain. Cross-validation showed that each baseline prediction model explained around 30%–33% of the variability of the corresponding outcome. Conclusions: Our analysis reveals relevant risk factors of each HRQoL domain in HL survivors. The complex interplay between several demographic, physical and psychosocial predictors has some predictive power for the HRQoL levels after therapy. The possibility of predicting HRQoL at the time of diagnosis and during and after therapy contributes to a better understanding of HRQoL in HL survivors and informs the development of much-needed interventions.