Hormone replacement therapy and COVID-19 outcomes in solid organ transplant recipients compared with the general population
Bibliographic record
Abstract
Exogenous estrogen is associated with reduced coronavirus disease (COVID) mortality in nonimmunosuppressed/immunocompromised (non-ISC) postmenopausal females. Here, we examined the association of estrogen or testosterone hormone replacement therapy (HRT) with COVID outcomes in solid organ transplant recipients (SOTRs) compared to non-ISC individuals, given known differences in sex-based risk in these populations. SOTRs ≥45 years old with COVID-19 between April 1, 2020 and July 31, 2022 were identified using the National COVID Cohort Collaborative. The association of HRT use in the last 24 months (exogenous systemic estrogens for females; testosterone for males) with major adverse renal or cardiac events in the 90 days post-COVID diagnosis and other secondary outcomes were examined using multivariable Cox proportional hazards models and logistic regression. We repeated these analyses in a non-ISC control group for comparison. Our study included 1135 SOTRs and 43 383 immunocompetent patients on HRT with COVID-19. In non-ISC, HRT use was associated with lower risk of major adverse renal or cardiac events (adjusted hazard ratio [aHR], 0.61; 95% confidence interval [CI], 0.57-0.65 for females; aHR, 0.70; 95% CI, 0.65-0.77 for males) and all secondary outcomes. In SOTR, HRT reduced the risk of acute kidney injury (aHR, 0.79; 95% CI, 0.63-0.98) and mortality (aHR, 0.49; 95% CI, 0.28-0.85) in males with COVID but not in females. The potentially modifying effects of immunosuppression on the benefits of HRT requires further investigation.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 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) | 0.003 | 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".