Association of COVID-19 pandemic with indolent lymphoma care delivery and outcomes in Ontario, Canada: A population-based analysis.
Bibliographic record
Abstract
e18800 Background: Due to concern for infection risk, the coronavirus disease 2019 (COVID-19) pandemic presented a unique challenge for optimal management of indolent non-Hodgkin lymphoma (iNHL). We examined treatment (trt) selection, healthcare utilization, and COVID-19 outcomes of pts with iNHL receiving first-line (1L) systemic treatment during pre-pandemic vs. pandemic period. Methods: We performed a retrospective cohort study using administrative databases in Ontario, Canada, comparing outcomes in pts with iNHL who initiated trt from, with end of follow-up Mar 31 2022. The primary outcome was trt pattern (eg, 1L regimen, rituximab [R] maintenance use); secondary outcomes were death, toxicities, healthcare utilization (emergency department visit [ED], hospitalization), SARS-CoV-2 outcomes (infection, ED visit, hospitalization/death). Adjusted hazard ratios (aHR) from cause-specific proportional hazards models were used to estimate associations between factors and outcomes. Results: We identified 4,143 pts (1,079 pandemic, 3,064 pre-pandemic), median age 69 yrs, 44% female. In both pre- and pandemic periods, bendamustine (B)+R was the most frequent prescribed regimen, with no difference in number of cycles or dose delays (Table). During the pandemic, fewer pts received R maintenance and completed the full course (aHR 0.81, 95% confidence interval [CI] 0.71-0.92, p = 0.0010) (Table). Pts treated during the pandemic had less healthcare utilization (ED visit aHR 0.77, 95% CI 0.68, 0.88, p < 0.0001; hospitalization aHR 0.81, 95% CI 0.70-0.94, p = 0.0067) and trt-related complications (infection aHR 0.69, 95% CI 0.57-0.82, p < 0.0001; febrile neutropenia aHR 0.66, 95% CI 0.47-0.94, p = 0.020), with no difference in death (aHR 0.79, 95% CI 0.58-1.08, p = 0.14). R use (first dose to 1 yr post last dose) was associated with higher risk of SARS-CoV-2 infection (aHR 1.56, 95% CI 1.09-2.24, p = 0.015) and COVID-19 complications (ED visit aHR 4.28, 95% CI 1.79-10.26, p = 0.0011; hospitalization/death 1.81, 95% CI 1.11-2.93, p = 0.016). Conclusions: During the pandemic, BR remained the preferred regimen for iNHL trt, while R maintenance use was less. Despite the similar 1L regimen, healthcare utilization and infectious complications were less in the pandemic cohort. R use was associated with nearly 2-fold risk of COVID-19 hospitalization/death. [Table: see text]
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.002 | 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) | 0.003 | 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".