Impact of Letermovir on Outcomes in Cytomegalovirus Seropositive Patients Undergoing Allogeneic HCT: Exploring Outcomes across Ethnicities
Bibliographic record
Abstract
Cytomegalovirus (CMV) serostatus remains an independent adverse risk factor for increased transplant related morbidity and mortality (TRM) in CMV seropositive (CMV+) recipients versus CMV seronegative recipients1,2. Letermovir (LET) is a novel anti-CMV agent that prevents CMV replication through inhibition of the viral terminase complex3 and is routinely given as primary prophylaxis for CMV seropositive transplant recipients as it results in a lower risk of clinically significant CMV infections4. At our institution we serve a diverse ethnic population and utilize a heterogenous mixture of donor sources and graft-versus-host-disease (GVHD) prophylaxis. We conducted this study to evaluate the impact of LET in our diverse population to evaluate for LET impact on major outcomes and evaluate for efficacy variations by ethnicity. A retrospective study found that 556 CMV+ patients received HCT between 2018 and 2022 at the Princess Margaret Cancer Centre (PMCC), 219 of which received LET prophylaxis. Primary endpoint was comparing those receiving LET and not receiving LET prophylaxis (non-LET) in Overall Survival (OS), Non-Relapse Mortality (NRM), Cumulative Incidence of Relapse (CIR), and GVHD-Free | Relapse-Free Survival (GRFS). Secondary endpoints explored LET impact on Cumulative Incidence of CMV Reactivation (CI-CMV) and OS stratified by self-reported ethnicity. In comparing, OS, CIR, GRFS and NRM for all patients, LET and non-LET, no significant difference in outcomes was observed. Figure 1. A significant LET effect was observed on the CI-CMV for the entire cohort. The most profound LET effect was in the East Asian cohort (LET 22%, non-LET 80%, p<0.001). Figure 2. The South Asian cohort activated CMV most frequently while on LET (38.9%, CI 16.8-60.7). Figure 3. The CI-CMV was significant between cohorts, with LET cohort having fewer reactivations (23.2% vs 32.1% experiencing 1, 5.0% vs 14.3% experiencing 2, and 5.0% vs 21.4% experiencing 3 reactivations; p<0.001). In multivariate analysis, LET reduced CMV reactivations by 53% compared to non-LET (HR=0.47 | CI 0.39-0.55, p<0.001). Our results indicate no observable LET drug effect on OS, CIR, GRFS, and NRM. Our East Asian cohort experienced a significant decrease in CMV reactivation with LET. Our South Asian cohort reactivated CMV more often while on LET compared to other ethnic cohorts. Further research is warranted in extending LET duration in higher risk ethnicities and/or investigating other strategies to decrease CMV reactivation in cohorts experiencing less LET benefit.
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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 | 0.000 | 0.001 |
| 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".