Hormone Replacement Therapy and COVID-19 Outcomes in Kidney Transplant Recipients Compared With the General Population
Bibliographic record
Abstract
Background: In the non-immunosuppressed (non-IS) population, female sex is protective against adverse COVID-19 (C19) outcomes, possibly due to estrogenrelated immunity. Sex-based risk is attenuated in IS kidney transplant recipients (KTRs). Exogenous estrogen is associated with reduced C19 mortality in non-IS postmenopausal females. Here, we aimed to study the impact of estrogen or testosterone hormone replacement therapy (HRT) on C19 outcomes in KTRs compared to the general population. Methods: We studied adult (>45 yrs) KTRs from across the US with C19 from 05-01-20 to 05-12-22, using EHR data from the National COVID Cohort Collaborative. Female and male patients were classified as no HRT, or HRT use in the last 6 months (exogenous systemic estrogens for females; testosterone for males). Using MV cox proportional hazards models and logistic regression, we determined the risk of developing a major adverse renal or cardiac event (MARCE), mortality, and other 90-day post-C19 outcomes. We repeated this analysis in a non-IS control group for comparison. Results: Over the study period, 11,498 KTRs and >1.9M non-IS patients were diagnosed with C19. In non-IS, relative to no HRT use, HRT use in the last 6 months was associated with significantly lower risk of MARCE (Hazard Ratio [HR] 0.54, 95% Confidence Interval [CI] 0.51-0.59, for females; 0.63, 0.56-0.70, for males), mortality (HR 0.45, CI 0.40-0.51, for females; 0.55, 0.45-0.66, for males), and all secondary events for males and females (Figure 1). In KTRs, HRT was not associated with any post-C19 outcome in either males or females; there was a trend towards lower risk in males on HRT vs not on HRT, for most outcomes. Conclusions: HRT was protective against adverse C19 outcomes in older non-IS males and females, but not in KTRs. The modifying effects of IS on the benefits of HRT requires further investigation. Funding: Other NIH Support - AO and EF were supported by CTSA award No. UL1TR002649 from the National Center for Advancing Translational Sciences and the data use was supported by NCATS U24 TR002306 and by the National Institute of General Medical Sciences, U54 GM115458, which funds the Great Plains IDeA-CTR Network. RBM is supported by BMX003272 and the Dr. Dennis Ross Research Fund in Nephrology, University of Nebraska.
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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 | 0.000 |
| 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 | 0.000 |
| 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".