Patients’ voices matter: Translating pretreatment patient-reported outcomes into high and low health-related quality of life provides a new clinical aspect on prognosis and survival in patients with previously untreated diffuse large B‐cell lymphoma.

Abstract

e23165 Background: Patient-reported health-related quality of life (HRQoL) measures collected prior to treatment initiation have been shown to be prognostic of disease outcomes and survival in patients with previously untreated diffuse large B-cell lymphoma (DLBCL), including progression-free survival (PFS) and overall survival (OS), However, these HRQoL measures are usually expressed as numbers from a scale (e.g. from 0 to 100), which limit their ease of clinical utility compared to binary scales (i.e. high vs. low). Using HRQoL measures collected from the phase 3 study GOYA, we aim to convert these measures into binary scales by identifying optimal cutoffs and demonstrate their clinical utility in differentiating patient risks in DLBCL. Methods: HRQoL data from the physical functioning (PF2), global health status (QL2), and fatigue (FA) scales from the EORTC QLQ-C30 questionnaire and the lymphoma subscale (LYMS) from the FACT-LYM questionnaire were used in this analysis. The optimal cutoff points were identified using statistical metrics that evaluate the prognostic value and goodness-of-fit for each assessed cutoff point. The top performing binary HRQoL scale was further assessed in its clinical utility as an addition to the Internal Prognostic Index (IPI), a standard risk classification tool in DLBCL based on 5 binary measures of patient baseline characteristics, to evaluate whether adding the HRQoL scale would potentially improve the risk classification. Results: As shown in the table, while all binary scales can clearly differentiate patient overall survival (demonstrated by the hazard ratios), PF2 numerically exhibited the best performance, and was further assessed as an addition to IPI. After adding PF2 to the base OS model containing only IPI, we observed improvements in model performance metrics, including the concordance index and the goodness-of-fit. In addition, among patients classified as high risk by IPI (n = 185, 3-yr OS: 65% [95% CI 68-72%]), those with low HRQoL (n = 102, 3-yr OS: 56% [95% CI 47-67%]) had a substantially worse OS, suggesting that further refinement of patient-risk defined by IPI may be feasible with HRQoL measures. Conclusions: Our work highlights the potential of the binary HRQoL scales to provide additional prognostic value to IPI, and suggests that patient reported outcomes may be useful in further refining patient risk classification in addition to IPI in untreated DLBCL. [Table: see text]