Outcomes and Predictors of Survival for Patients with Relapsed or Refractory Diffuse Large B Cell Lymphoma: A Large Population-Based Analysis
Bibliographic record
Abstract
Background Despite therapeutic advances for treatment of diffuse large B cell lymphoma (DLBCL), 20-30% of patients (pts) experience relapse or refractory disease (R/R) after initial therapy (1L). With ongoing development of novel therapies, better understanding of outcomes for pts with R/R disease are needed to establish benchmarks for future studies. We conducted a population-based analysis of outcomes in pts with R/R DLBCL treated with curative or palliative approaches, and identified factors associated with overall survival (OS). Methods All patients with DLBCL ≥ 18 years (y) in Ontario, Canada, who received rituximab-based treatment as 1L between January 2012 - December 2022 were identified using linked administrative databases (follow-up to August 2023). Pts with R/R disease were defined as initiation of second line therapy (2L) with curative intent (salvage chemotherapy [SC] with or without autologous stem cell transplant [ASCT]), palliative systemic therapy or palliative radiation (> 3 months after 1L). The primary outcome was OS. Analyses were stratified by 2L intent (curative vs. palliative), age, and time to next treatment (TTNT) (surrogate for time to relapse). TTNT was defined as the time from end of 1L to initiation of 2L, categorized as ≤3, 3-6, 6-12, 12-24, and ≥ 24 months (mos). Univariate and multivariable Cox regression analyses were used to identify factors associated with OS. Results We identified 8,675 pts diagnosed with DLBCL;1,675 (19%) met definition for R/R disease (median age 67 y, 60-69 y n=474 [28%], 70-79 y [27%], ≥ 80 y [14%]; 40% female; 56% stage IV with known stage). Median LDH was 265 U/L (IQR 195-438). The 2-y and 5-y OS rates from start of 2L were 33% and 26%, respectively. For 2L therapy, 976 pts (58%) had curative intent SC (including 440 pts [26%] who underwent ASCT), 338 pts (20%) had palliative intent systemic therapy (146 oral, 17 investigational, 175 multiagent), and 361 (22%) had palliative intent radiotherapy. The 2-y and 5-y OS rates for curative and palliative intent were 38%, 32%, and 26%, 17%, respectively. Univariate regression showed that curative intent 2L had improved OS over palliative radiation (hazard ratio [HR] 0.56, 95% confidence interval [CI] 0.49-0.65, p<0.0001), as did palliative intent systemic therapy (HR 0.79, 95% CI 0.66-0.93, p=0.006). Univariable analysis showed that increasing age was associated with inferior OS compared to pts aged <60 years: age 60-69 years: HR 1.35, 95% CI 1.16-1.59, p=0.0002, 2-y OS 33%, 5-y OS 27%; age 70-79 years: HR 1.64, 95% CI 1.40-1.92, p<0.0001, 2-y OS 29%, 5-y OS 16%; age ≥80 years: HR 2.08, 95% CI 1.73-2.49, p<0.0001, 2-y OS 20%, 5-y OS 12%). Pts with short TTNT (< 12 mos 1,231 pts, 73% of cohort) had worse OS compared to late TTNT ≥ 24 mos: (<3 mos: HR 1.45, 95% CI 1.19-1.77, p=0.0002, 2-y OS 32%, 5-y OS 29%; 3-6 mos: HR 1.51, 95% CI 1.23-1.85, p=0.0001, 2-y OS 33%, 5-y OS 29%; 6-12 mos: HR 1.88, 95% CI 1.53-2.31, p<0.0001, 2-y OS 22%, 5-y OS 14%), while those with TTNT 12-24 mos did not (HR 1.27, 95% CI 0.99-1.61, p=0.054, 2-y OS 39%, 5-y OS 22%). Patients with TTNT < 12 mos had inferior OS compared to ≥ 24 mos for curative (5-y OS 28% vs. 54%) and palliative intent 2L (5-y OS 11% vs. 20%). In multivariable analysis (adjusting for sex, rurality, income, LDH, and comorbidity burden), predictors of mortality included older age (per 10 y increase, adjusted HR [aHR] 1.17, 95% CI 1.11-1.23, p<0.0001; dichotomized as ≥ 70 y vs. < 70 y, aHR 1.34, 95% CI 1.16-1.55, p<0.0001), shorter TTNT (<12 mos vs. ≥ 12 mos aHR 1.39, 95% CI 1.21-1.60, p<0.0001), and LDH level 200-399 vs. < 200, aHR 1.39, 95% CI 1.17-1.65, p<0.0001; LDH level ≥ 400 aHR 2.53, 95% CI 2.12-3.03, p<0.0001) and treatment choice curative intent or palliative intent therapy (aHR 0.73, 95% CI 0.62-0.86 vs. aHR 0.74, 95% CI 0.62-0.88, when compared to palliative radiation). Conclusions In this contemporary population-based review, ~ 20% of patients relapsed after 1L therapy, with 5-y OS of only 26%. Independent predictors of inferior outcomes were time to relapse, age, and LDH. Pts with TTNT < 12 mos had worse OS outcomes compared to those with later relapse. Furthermore, our data highlight the need to assess novel therapies in older pts (> 70 y) to address the disparity in outcomes. Future analyses are required to evaluate the impact of novel treatment (CAR-T or bispecifics) in the 2L+ setting on outcomes at a population level.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".