Delayed Mortality Among Solid Organ Transplant Recipients Hospitalized for COVID-19
Bibliographic record
Abstract
INTRODUCTION: Most studies of solid organ transplant (SOT) recipients with COVID-19 focus on outcomes within one month of illness onset. Delayed mortality in SOT recipients hospitalized for COVID-19 has not been fully examined. METHODS: We used data from a multicenter registry to calculate mortality by 90 days following initial SARS-CoV-2 detection in SOT recipients hospitalized for COVID-19 and developed multivariable Cox proportional-hazards models to compare risk factors for death by days 28 and 90. RESULTS: Vital status at day 90 was available for 936 of 1117 (84%) SOT recipients hospitalized for COVID-19: 190 of 936 (20%) died by 28 days and an additional 56 of 246 deaths (23%) occurred between days 29 and 90. Factors associated with mortality by day 90 included: age > 65 years [aHR 1.8 (1.3-2.4), p =<0.001], lung transplant (vs. non-lung transplant) [aHR 1.5 (1.0-2.3), p=0.05], heart failure [aHR 1.9 (1.2-2.9), p=0.006], chronic lung disease [aHR 2.3 (1.5-3.6), p<0.001] and body mass index ≥ 30 kg/m 2 [aHR 1.5 (1.1-2.0), p=0.02]. These associations were similar for mortality by day 28. Compared to diagnosis during early 2020 (March 1-June 19, 2020), diagnosis during late 2020 (June 20-December 31, 2020) was associated with lower mortality by day 28 [aHR 0.7 (0.5-1.0, p=0.04] but not by day 90 [aHR 0.9 (0.7-1.3), p=0.61]. CONCLUSIONS: In SOT recipients hospitalized for COVID-19, >20% of deaths occurred between 28 and 90 days following SARS-CoV-2 diagnosis. Future investigations should consider extending follow-up duration to 90 days for more complete mortality assessment.
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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 | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".