Sex and organ-specific risk of major adverse renal or cardiac events in solid organ transplant recipients with COVID-19
Bibliographic record
Abstract
While older males are at the highest risk for poor coronavirus disease 2019 (COVID-19) outcomes, it is not known if this applies to the immunosuppressed recipient of a solid organ transplant (SOT), nor how the type of allograft transplanted may impact outcomes. In a cohort study of adult (>18 years) patients testing positive for COVID-19 (January 1, 2020-June 21, 2021) from 56 sites across the United States identified using the National COVID Cohort Collaborative (N3C) Enclave, we used multivariable Cox proportional hazards models to assess time to MARCE after COVID-19 diagnosis in those with and without SOT. We examined the exposure of age-stratified recipient sex overall and separately in kidney, liver, lung, and heart transplant recipients. 3996 (36.4%) SOT and 91 646 (4.8%) non-SOT patients developed MARCE. Risk of post-COVID outcomes differed by transplant allograft type with heart and kidney recipients at highest risk. Males with SOT were at increased risk of MARCE, but to a lesser degree than the non-SOT cohort (HR 0.89, 95% CI 0.81-0.98 for SOT and HR 0.61, 95% CI 0.60-0.62 for non-SOT [females vs. males]). This represents the largest COVID-19 SOT cohort to date and the first-time sex-age-stratified and allograft-specific COVID-19 outcomes have been explored in those with SOT.
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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 | 0.003 |
| 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 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".