COVID-19 infection in liver transplant recipients: Clinical features and outcomes from a Canadian multicentre cohort
Bibliographic record
Abstract
BACKGROUND: Prior studies have assessed risk factors and clinical outcomes in liver transplant (LT) recipients infected with COVID-19 globally; however, there is a paucity of Canadian data. Our multicentre study aims to examine the characteristics and clinical outcomes of LT patients with COVID-19 infection in Canada. METHODS: Adult LT recipients with reverse transcription-polymerase chain reaction (RT-PCR) confirmed COVID-19, from Canadian tertiary care centres between March 2020 and June 2021 were included. RESULTS: A total of 49 patients with a history of LT and COVID-19 infection were identified. Twenty nine patients (59%) were male, median time from LT was 66 months (IQR 1, 128), and median age was 59 years (IQR 52, 65). At COVID-19 diagnosis, the median alanine transaminase (ALT) was 37 U/L (IQR 21, 41), aspartate aminotransferase (AST) U/L was 34 (IQR 20, 37), alkaline phosphatase (ALP) U/L was 156 (IQR 88, 156), total bilirubin was 11 umol/L (IQR 7, 14), and international normalized ratio (INR) was 1.1 (IQR 1.0, 1.1). The majority of patients (86%) were on tacrolimus (monotherapy or combined with mycophenolate mofetil); median tacrolimus level at COVID-19 diagnosis was 5.3 ug/L (IQR 4.0, 8.1). Immunosuppression was modified in eight (16%) patients post-infection. Eighteen patients (37%) required hospitalization, and three (6%) required intensive care unit (ICU) admission and mechanical ventilation. Four patients (8%) died from complications related to COVID-19 infection. On univariate analysis, neither age, sex, comorbidities nor duration post-transplant were associated with risk of hospitalization or ICU admission. CONCLUSIONS: LT recipients with COVID-19 have high rates of hospitalization but fortunately have low rates of ICU admission and mortality in this national registry.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 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".