MétaCan
Menu
← Back to cohort
Record W3200684353 · doi:10.1136/gutjnl-2021-basl.57

P048 Geographic variability in rates of intensive care unit admission in patients with chronic liver disease and critical COVID-19: International registry data

2021· article· en· W3200684353 on OpenAlexaff
Thomas Marjot, Andrew M. Moon, Matthew J. Armstrong, Ignacio García‐Juárez, Anand V. Kulkarni, Steven Masson, Nneka N. Ufere, David T. Wong, Luke Baldelli, A. Sidney Barritt, Eleanor Barnes, Gwilym J. Webb

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldMedicine
TopicCOVID-19 and healthcare impacts
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineIntensive care unitPandemicMortality rateMechanical ventilationLiver diseaseCirrhosisEmergency medicineCoronavirus disease 2019 (COVID-19)Intensive careSeverity of illnessAlcoholic liver diseaseIntensive care medicineInternal medicineDiseaseInfectious disease (medical specialty)

Abstract

fetched live from OpenAlex

Background and Aims The COVID-19 pandemic has provided a unique opportunity to evaluate global intensive care unit (ICU) admission practices for a common indication. Patients with chronic liver disease (CLD) and cirrhosis may also have limited or variable access to ICU. We aimed to describe international ICU admission rates and outcomes in patients with CLD and critical COVID-19. Methods Data were combined from two international registries (SECURE-Liver and COVID-Hep) for patients with CLD and COVID-19 which was deemed severe enough to require ICU by the reporting clinician. Rates of ICU admission or decline, and respective outcomes were compared by country. Results Between 25th March 2020 and 3rd February 2021, 319 patients with CLD and COVID-19 from 27 countries were judged to require ICU. The proportion of patients ultimately accepted to ICU varied according to country (figure 1A), although mortality following ICU admission was similar by country (figure 1B). Factors associated with being declined ICU admission included advancing age, cirrhosis, alcohol related liver disease, and UK origin. To explore national differences further, we compared cases from the USA and UK, the two greatest contributing countries. Rates of ICU admission differed significantly between the USA and UK [77/79 (95%) vs. 22/77 (29%); p<0.001]. However, there were no differences in mortality in those admitted to ICU (42/75 [56%] vs. 10/22 [45%]; p=0.468; figure 1B), or after receiving invasive ventilation (29/59 [49%] vs. 9/17 [53%]; p=1.000). There were also no differences in age, sex, Charlson Comorbidity Index, or baseline liver disease severity between countries, both in those requiring and admitted to ICU. This included comparable rates of cirrhosis (53/79 [67%] vs. 56/77 [72%]; p=0.723). Only four USA patients were declined ICU admission of whom 2 (50%) died, whereas 55 UK patients were declined ICU admission of whom 51 (93%) died. In both USA and UK cohorts, the reason for not admitting patients to ICU was due to this being deemed inappropriate by the responsible clinician, except for one case in both countries where no ICU bed was available. Notably, information relating to patient wishes, long-term outcomes in survivors, and granular detail regarding organ support requirements were not available. Conclusion Patients with CLD and critical COVID-19 were over 3-times more likely to be admitted to ICU in the USA than the UK despite having similar baseline characteristics. However, the rates of mortality following ICU admission were comparable between the two countries. The differing thresholds for escalation to ICU but similar post admission outcomes warrants further discussion.

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)0.002
metaresearch head score (Gemma)0.008
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 score0.011
Threshold uncertainty score0.022

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.008
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.004
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0020.000

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 head0.075
GPT teacher head0.417
Teacher spread0.342 · 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

Citations1
Published2021
Admission routes1
Has abstractyes

Explore more

Same topicCOVID-19 and healthcare impacts→French-language works237,207→