COVID-19 pandemic and worldwide organ transplantation: a population-based study
Bibliographic record
Abstract
BACKGROUND: Preliminary data suggest that COVID-19 has reduced access to solid organ transplantation. However, the global consequences of the COVID-19 pandemic on transplantation rates and the effect on waitlisted patients have not been reported. We aimed to assess the effect of the COVID-19 pandemic on transplantation and investigate if the pandemic was associated with heterogeneous adaptation in terms of organ transplantation, with ensuing consequences for waitlisted patients. METHODS: In this population-based, observational, before-and-after study, we collected and validated nationwide cohorts of consecutive kidney, liver, lung, and heart transplants from 22 countries. Data were collected from Jan 1 to Dec 31, 2020, along with data from the same period in 2019. The analysis was done from the onset of the 100th cumulative COVID-19 case through to Dec 31, 2020. We assessed the effect of the pandemic on the worldwide organ transplantation rate and the disparity in transplant numbers within each country. We estimated the number of waitlisted patient life-years lost due to the negative effects of the pandemic. The study is registered with ClinicalTrials.gov, NCT04416256. FINDINGS: Transplant activity in all countries studied showed an overall decrease during the pandemic. Kidney transplantation was the most affected, followed by lung, liver, and heart. We identified three organ transplant rate patterns, as follows: countries with a sharp decrease in transplantation rate with a low COVID-19-related death rate; countries with a moderate decrease in transplantation rate with a moderate COVID-19-related death rate; and countries with a slight decrease in transplantation rate despite a high COVID-19-related death rate. Temporal trends revealed a marked worldwide reduction in transplant activity during the first 3 months of the pandemic, with losses stabilising after June, 2020, but decreasing again from October to December, 2020. The overall reduction in transplants during the observation time period translated to 48 239 waitlisted patient life-years lost. INTERPRETATION: We quantified the impact of the COVID-19 pandemic on worldwide organ transplantation activity and revealed heterogeneous adaptation in terms of organ transplantation, both at national levels and within countries, with detrimental consequences for waitlisted patients. Understanding how different countries and health-care systems responded to COVID-19-related challenges could facilitate improved pandemic preparedness, notably, how to safely maintain transplant programmes, both with immediate and non-immediate life-saving potential, to prevent loss of patient life-years. FUNDING: French national research agency (INSERM) ATIP Avenir and Fondation Bettencourt Schueller.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 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".