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Record W4323350973 · doi:10.1093/jcag/gwac036.248

A248 OUTCOMES OF COVID-19 ILLNESS AND ACCESS TO APPROPRIATE TREATMENT IN LIVER TRANSPLANT RECIPIENTS IN BRITISH COLUMBIA THROUGHOUT THE PANDEMIC

2023· article· en· W4323350973 on OpenAlexaffabout
J Reid, E Yoshida, T Hussaini, J -A Harrigan

Bibliographic record

VenueJournal of the Canadian Association of Gastroenterology · 2023
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 Clinical Research Studies
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineTocilizumabCoronavirus disease 2019 (COVID-19)ImmunosuppressionEmergency medicineLiver transplantationRetrospective cohort studyPandemicIntensive care medicineTransplantationInternal medicine

Abstract

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Abstract Background COVID-19 continues to cause significant illness and mortality worldwide. Solid Organ Transplant Recipients (SOTR) have a higher rate of COVID-19 infection and worse outcomes than those who are immunocompetent. Dexamethasone, tocilizumab, and baricitinib have improved inpatient outcomes. Sotrovimab, remdesivir, and nirmatrelvir/ritonavir have recently been approved for used in high risk, minimally symptomatic outpatients. Previous experience has shown that use of monoclonal antibodies and oral antiviral agents have reduced morbidity and mortality of COVID-19 in SOTR. Purpose To assess the experiences and outcomes of COVID-19 and access to directed therapy in SOTR in British Columbia (BC). Method Data was compiled from patient disclosure to liver transplant clinicians on COVID-19 infection and gathered from patient charts in the SOTR Clinic at Vancouver General Hospital. Inclusion criteria were patients followed at the clinic with a positive COVID-19 test or clinical confirmation of COVID-19 syndrome. This is a retrospective, quality assurance study and did not require ethics review. Result(s) 158 SOTR reported COVID-19 infections between March 2020 and September 2022. 3 patients died within 30 days of COVID-19 infection, 2 (1.26%) of which the cause of death was directly due to COVID-19, and the other who had cholangitis with severe sepsis and multi-organ system failure. 24 patients required admission to hospital, 7 requiring critical care support. 41 patients did not receive any therapy for COVID-19: there was none available at that time (n=26), it was contraindicated due to a drug interaction (n=2) or medical condition (n=1), was refused (n=1), or the infection was reported too late to qualify (n=10). 83% (92/112) of outpatients received available anti-viral treatment: sotrovimab (n=27), remdesivir (n=63), or nirmatrelvir/ritonavir (n=2). In inpatients (n=24), 13 received corticosteroids, 6 dual treated with tocilizumab (n=4) or baracitinib (n=2). 4 inpatients received remdesivir. Three patients were treated in hospital after initiating outpatient therapy, one with progression of COVID-19 illness despite starting remdesivir. Two patients had adverse effects of medications provided: one was prescribed nirmatrelvir/ritonavir by a physician outside of the transplant program, which caused tacrolimus toxicity (serum concentration of 69.4 ng/mL) with nausea, vomiting, and diarrhea. Another patient had an episode of hypotension after receiving sotrovimab and sustained an acute kidney injury (AKI). Both patients fully recovered. There were no deaths on antiviral therapy. Of 145 patients who had laboratory investigations done within 30 days of COVID-19 infection, 16 had a transient rise in liver enzymes, 14 had an AKI and 11required an adjustment in their tacrolimus dose. Conclusion(s) Involving the liver transplant team early in the course of COVID-19 illness allows patients to safely access COVID-19 directed therapy to avoid progression of illness, and medication interactions or toxicity. Please acknowledge all funding agencies by checking the applicable boxes below None Disclosure of Interest None Declared

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)0.000
metaresearch head score (Gemma)0.002
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 score0.070
Threshold uncertainty score0.142

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.002
Science and technology studies0.0020.001
Scholarly communication0.0010.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.048
GPT teacher head0.383
Teacher spread0.335 · 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".

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Citations0
Published2023
Admission routes2
Has abstractyes

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