Immunosuppression practices during the COVID‐19 pandemic: A multinational survey study of transplant programs
Bibliographic record
Abstract
During the COVID-19 pandemic, there has been wide heterogeneity in the medical management of transplant recipients. We aimed to pragmatically capture immunosuppression practices globally following the early months of the pandemic. From June to September 2020, we surveyed 1267 physicians; 40.5% from 71 countries participated. Management decisions were made on a case-by-case basis by the majority (69.6%) of the programs. Overall, 76.8% performed ≥1 transplantation and many commented on avoiding high-risk transplantations. For induction, 26.5% were less likely to give T-cell depletion and 14.8% were more likely to give non-depleting agents. These practices varied by program-level factors more so than the COVID-19 burden. In patients with mild, moderate and severe COVID-19 symptoms 59.7%, 76.0%, and 79.5% decreased/stopped anti-metabolites, 23.2%, 45.4%, and 68.2% decreased/stopped calcineurin inhibitors, and 25.7%, 43.9%, and 57.7% decreased/stopped mTOR inhibitors, respectively. Also, 2.1%, 30.6%, and 46.0% increased steroids in patients with mild, moderate, and severe COVID-19 symptoms. For prevalent transplant recipients, some programs also reported decreasing/stopping steroids (1.8%), anti-metabolites (10.3%), calcineurin inhibitors (4.1%), and mTOR inhibitors (5.5%). Transplant programs changed immunosuppression practices but also avoided high-risk transplants and increased maintenance steroids. The long-term ramifications of these practices remain to be seen as programs face the aftermath of the pandemic.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".