Abstract 2035: Listen to the patients: Assessing the prognostic value of pre-treatment health-related quality of life in 1L DLBCL patients
Bibliographic record
Abstract
Abstract Background: Identifying patients with high-risk of progression or death is important in developing novel treatment strategies in diffuse large B-cell lymphoma (DLBCL). The International Prognostic Index (IPI) is a commonly used score to classify the prognostic risk of previously untreated (1L) DLBCL patients. Although perceived as important in understanding the health status of patients, patient-reported health-related quality-of-life (HRQoL) measures have not been studied extensively as prognostic factors in DLBCL. In this study, we explored the prognostic value of pretreatment HRQoL in progression free survival (PFS) and overall survival (OS) in 1L DLBCL patients, using data from the phase III GOYA study (NCT01287741, Obinutuzumab-CHOP vs Rituximab-CHOP). Method: Four preselected pretreatment HRQoL subscale scores (lymphoma specific [LYMS], physical functioning [PF2], role functioning [RF2], and fatigue [FA]) were derived from two HRQoL questionnaires (EORTC-QLQ C30 and FACT-Lym) in the GOYA study. Each subscale was dichotomized to indicate low or high HRQoL based on their respective median scores (table). Sensitivity analyses were similarly evaluated. The prognostic value of each HRQoL subscale was evaluated using Cox proportional hazard models, adjusted for the five components of IPI. Table. Summary of prognostic value for the four pre-treatment HRQoL subscales Summary 3-year OS estimate2 (95% CI) Cox proportional hazard model Subscale(n1) Questionnaire Median (min-max) Low HRQoL High HRQoL OS HR3 (95% CI) PFS HR3(95% CI) Lymphoma specific(n = 1246) FACT-LYM 47 (7-60) 0.78 (0.74, 0.81) 0.85 (0.82, 0.87) 0.7 (0.51, 0.95) 0.81(0.63, 1.03) Physical functioning(n = 1254) EORTC-QLQ C30 87(0-100) 0.77 (0.74, 0.80) 0.86 (0.83, 0.89) 0.6 (0.43, 0.85) 0.72(0.56, 0.93) Role functioning(n = 1256) EORTC-QLQ C30 83(0-100) 0.78 (0.75, 0.81) 0.84 (0.81, 0.88) 0.72(0.52, 0.99) 0.84(0.65, 1.07) Fatigue(n = 1256) EORTC-QLQ C30 33(0-100) 0.74(0.70, 0.78) 0.84 (0.82, 0.87) 0.68 (0.5, 0.92) 0.94(0.73, 1.2) 1Only patients with valid respective pretreatment HRQoL scores were included in each analysis 2OS estimated using the Kaplan-Meier method 3HR = hazard ratio representing high HRQoL vs. low HRQoL, adjusted for the five components of IPI: age(≤60 vs >60), ECOG PS(0-1 vs 2-3), lactate dehydrogenase level (LDH)(≤1 normal vs >1 normal), Ann Arbor stage (Stage I or II vs III or IV disease), and extranodal sites(≤1 vs >1 extranodal site) Results: All four HRQoL subscales contributed independent prognostic value to patient outcome (table). Results show that high HRQoL is associated with better survival outcome (higher 3-year OS estimate and lower risk [HR]) compared with low HRQoL. Among the four subscales, PF2 had the highest estimated contribution to prognosis (OS: HR = 0.59, 95% CI: [0.42, 0.84], PFS: HR = 0.71, 95% CI [0.55, 0.93]). Conclusion: Our findings demonstrate the potential of patient-reported HRQoL measures in providing prognostic value in addition to IPI, which may contribute to improved risk stratification and inform treatment decisions for DLBCL patients. Citation Format: Huang Huang, Asim Datye, Ming Fan, Andrea Knapp, Rama Balakrishnan, Sandhya Balasubramanian, Julia Chae, Emma Roth, Tina Nielsen, Joseph N. Paulson, Peter Trask. Listen to the patients: Assessing the prognostic value of pre-treatment health-related quality of life in 1L DLBCL patients [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2035.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".