Racial, ethnic, and socioeconomic disparities in treatment outcomes in patients (pts) with diffuse large B-cell lymphoma (DLBCL): A U.S. real-world study using a de-identified electronic health record (EHR)-derived database.
Bibliographic record
Abstract
e18514 Background: DLBCL, an aggressive disease, is the most common subtype of non-Hodgkin lymphoma. Few studies have addressed socioeconomic and racial/ethnic disparities in treatment patterns and health outcomes for pts with DLBCL. We present a retrospective cohort study, leveraging real-world data from a nationwide database, to investigate these disparities. Methods: Pts with DLBCL treated with first-line (1L) therapy within 90 days of diagnosis were selected from the nationwide Flatiron Health EHR-derived de-identified database from January 2011 to May 2020. During the study, the de-identified data originated from approximately 280 US cancer clinics (̃800 sites of care). Pts’ baseline characteristics, treatment patterns, overall survival (OS), time to next therapy or death to any cause (TTNTD) were compared between race groups (non-Hispanic White [W], non-Hispanic African American [AA], Hispanic or Latino [H], non-Hispanic Asian [A]) and socioeconomic groups (Medicaid without Commercial [Medicaid] vs Commercial without Medicaid [Commercial]). Baseline characteristics were compared using Fisher’s exact, chi-squared or t-tests. Time to event endpoints were compared using Cox models adjusting baseline characteristics. Results: In total, 4,648 pts with DLBCL (82% W, 7% AA, 8% H, 3% A) were included. Compared with other race groups, W pts were older (mean age: 67 vs 60, 62, 62 [W vs AA, H, A]), had a higher proportion of pts with Eastern Cooperative Oncology Group score ≥2 (8% vs 5%, 4%, 4%), and fewer pts with Medicaid insurance (1.7% vs 5%, 6%, 3%). Across race groups, 1L treatments received were similar; 82% had R-CHOP. There were no significant differences in OS (P = 0.278; HR [AA, H, A vs W]: 0.87, 0.85, 0.84) and TTNTD (P = 0.158; HR: 0.89, 0.88, 1.19). There were statistically significant differences in time from diagnosis to treatment (P < 0.0001; HR: 0.83, 0.79, 1.12), although the magnitude of the median differences were relatively small (22, 24, 25, 19 days [W, AA, H, A]). In pts aged < 65, commercially insured pts had less advanced disease (Group Stage IV: 28% vs 59%), better OS (HR [95% CI]: 0.50 [0.31–0.81], P = 0.005) and later TTNTD (HR: 0.70 [0.48–1.03], P = 0.067) compared with Medicaid insured pts. In pts aged ≥65, commercially insured pts had similar disease stage, OS (HR: 1.09 [0.65–1.84], P = 0.756) and TTNTD (HR: 0.94 [0.61–1.44], P = 0.763) compared with Medicaid insured pts. Insurance was not a significant factor for time from diagnosis to treatment for pts aged < 65 (HR: 1.05 [0.80–1.37], P = 0.727) and ≥65 (HR: 1.05 [0.78–1.42], P = 0.742). Conclusions: In this analysis of over 4,500 pts with DLBCL treated in the real-world, access to commercial insurance was associated with health outcomes in pts under 65 years of age, possibly due to earlier diagnosis; race was not a significant factor.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".