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Record W4293663170 · doi:10.1503/cmaj.220620

Severity of COVID-19 among solid organ transplant recipients in Canada, 2020–2021: a prospective, multicentre cohort study

2022· article· en· W4293663170 on OpenAlexaffvenueabout
Victoria Hall, Javier T Solera, Ghadeer Al-Alahmadi, Tina Marinelli, Héloïse Cardinal, Charles Poirier, Geneviève Huard, G. V. Ramesh Prasad, Sacha A. De Serres, Debra Isaac, Rahul Mainra, Caroline Lamarche, Ruth Sapir‐Pichhadze, Susan Gilmour, Atul Humar, Deepali Kumar

Bibliographic record

VenueCanadian Medical Association Journal · 2022
Typearticle
Languageen
FieldMedicine
TopicSARS-CoV-2 and COVID-19 Research
Canadian institutionsMcGill University Health Centre
Fundersnot available
KeywordsInterquartile rangeMedicineContext (archaeology)ImmunosuppressionProspective cohort studyInternal medicineCohortOrgan transplantationCohort studyCoronavirus disease 2019 (COVID-19)TransplantationDisease

Abstract

fetched live from OpenAlex

Background: Severe COVID-19 appears to disproportionately affect people who are immunocompromised, although Canadian data in this context are limited. We sought to determine factors associated with severe COVID-19 outcomes among recipients of organ transplants across Canada. Methods: We performed a multicentre, prospective cohort study of all recipients of solid organ transplants from 9 transplant programs in Canada who received a diagnosis of COVID-19 from March 2020 to November 2021. Data were analyzed to determine risk factors for oxygen requirement and other metrics of disease severity. We compared outcomes by organ transplant type and examined changes in outcomes over time. We performed a multivariable analysis to determine variables associated with need for supplemental oxygen. Results: A total of 509 patients with solid organ transplants had confirmed COVID-19 during the study period. Risk factors associated with needing (n = 190), compared with not needing (n = 319), supplemental oxygen included age (median 62.6 yr, interquartile range [IQR] 52.5–69.5 yr v. median 55.5 yr, IQR 47.5–66.5; p < 0.001) and number of comorbidities (median 3, IQR 2–3 v. median 2, IQR 1–3; p < 0.001), as well as parameters associated with immunosuppression. Recipients of lung transplants (n = 48) were more likely to have severe disease with a high mortality rate (n = 15, 31.3%) compared with recipients of other organ transplants, including kidney (n = 48, 14.8%), heart (n = 1, 4.4%), liver (n = 9, 11.4%) and kidney–pancreas (n = 3, 12.0%) transplants (p = 0.02). Protective factors against needing supplemental oxygen included having had a liver transplant and receiving azathioprine. Having had 2 doses of SARS-CoV-2 vaccine did not have an appreciable influence on oxygen requirement. Multivariable analysis showed that older age (odds ratio [OR] 1.04, 95% confidence interval [CI] 1.02–1.07) and number of comorbidities (OR 1.63, 95% CI 1.30–2.04), among other factors, were associated with the need for supplemental oxygen. Over time, disease severity did not decline significantly. Interpretation: Despite therapeutic advances and vaccination of recipients of solid organ transplants, evidence of increased severity of COVID-19, in particular among those with lung transplants, supports ongoing public health measures to protect these at-risk people, and early use of COVID-19 therapies for recipients of solid organ transplants.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.113
Threshold uncertainty score0.228

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.003
Science and technology studies0.0020.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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.

Opus teacher head0.010
GPT teacher head0.287
Teacher spread0.276 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations34
Published2022
Admission routes3
Has abstractyes

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