Decision Tree Analyses for Prediction of QoL over a One-Year Period in Breast Cancer Patients: An Added Value of Patient-Reported Outcomes

SIMPLE SUMMARY: Despite the fact that self-rated health is an established and independent predictor for future health outcomes, patient-reported outcomes (PROs) are rarely utilized in clinical decisions. Such an approach can be a disadvantage for patients who might benefit from the early detection of subtle signs of deterioration in the quality of life. The present study explored the determinants of various quality-of-life trajectories during the first year after breast cancer treatment. We recognized three distinct trajectories of global quality of life: ‘high’, ‘U-shape’ and ‘low’. Our results indicate that taking PROs into account allows for a more accurate prediction of a given quality-of-life trajectory than considering only medical and sociodemographic characteristics. Concentrating on the patient’s perspective in the clinical interview is recommended, especially for patients with permanent or fluctuating lower quality of life. ABSTRACT: Despite the current shift in medicine towards patient-centered care, clinicians rarely utilize patient-reported outcomes (PROs) in everyday practice. We examined the predictors of quality- of-life (QoL) trajectories in breast cancer (BC) patients during the first year after primary treatment. A total of 185 BC patients referred for postoperative radiotherapy (RT) filled in the EORTC QLQ-C30 Questionnaire assessing global QoL, functioning and cancer-related symptoms before starting RT; directly after RT; and 3, 6 and 12 months after RT. We used decision tree analyses to examine which baseline factors best allowed for predicting the one-year trajectory of the global QoL after BC treatment. We tested two models: ‘basic’, including medical and sociodemographic characteristics, and ‘enriched’, additionally including PROs. We recognized three distinct trajectories of global QoL: ‘high’, ‘U-shape’ and ‘low’. Of the two compared models, the ‘enriched’ model allowed for a more accurate prediction of a given QoL trajectory, with all indicators of model validation being better. In this model, baseline global QoL and functioning measures were the key discriminators of QoL trajectory. Taking PROs into account increases the accuracy of the prediction model. Collecting this information in the clinical interview is recommended, especially for patients with lower QoL.