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Record W2319916234 · doi:10.1186/s13054-016-1230-8

The influence of corticosteroid treatment on the outcome of influenza A(H1N1pdm09)-related critical illness

2016· article· en· W2319916234 on OpenAlexafffundabout
Jesse W. Delaney, Ruxandra Pinto, Jennifer Long, François Lamontagne, Neill K. J. Adhikari, Anand Kumar, John C. Marshall, Philippe Jouvet, Niall D. Ferguson, Donald Griesdale, Lisa Burry, Karen E. A. Burns, Jamie Hutchison, Sangeeta Mehta, Kusum Menon, Robert Fowler

Bibliographic record

VenueCritical Care · 2016
Typearticle
Languageen
FieldMedicine
TopicAdrenal Hormones and Disorders
Canadian institutionsChildren's Hospital of Eastern OntarioUniversity of OttawaSickKids FoundationUniversity of British ColumbiaVancouver General HospitalMount Sinai HospitalHospital for Sick ChildrenUniversité de MontréalUniversity of ManitobaSt. Joseph’s Healthcare HamiltonUniversity Health NetworkSt. Joseph's HospitalSunnybrook Health Science CentreSt. Michael's HospitalManitoba HealthCentre Hospitalier Universitaire Sainte-JustineMcMaster UniversityUniversity of TorontoUniversité de SherbrookeSunnybrook Hospital
FundersCanadian Institutes of Health ResearchPublic Health AgencyPublic Health Agency of CanadaHeart and Stroke Foundation of Canada
KeywordsMedicineInterquartile rangeConfoundingOdds ratioLogistic regressionConfidence intervalInternal medicineObservational studyCorticosteroidCohort study

Abstract

fetched live from OpenAlex

BACKGROUND: Patients with 2009 pandemic influenza A(H1N1pdm09)-related critical illness were frequently treated with systemic corticosteroids. While observational studies have reported significant corticosteroid-associated mortality after adjusting for baseline differences in patients treated with corticosteroids or not, corticosteroids have remained a common treatment in subsequent influenza outbreaks, including avian influenza A(H7N9). Our objective was to describe the use of corticosteroids in these patients and investigate predictors of steroid prescription and clinical outcomes, adjusting for both baseline and time-dependent factors. METHODS: In an observational cohort study of adults with H1N1pdm09-related critical illness from 51 Canadian ICUs, we investigated predictors of steroid administration and outcomes of patients who received and those who did not receive corticosteroids. We adjusted for potential baseline confounding using multivariate logistic regression and propensity score analysis and adjusted for potential time-dependent confounding using marginal structural models. RESULTS: Among 607 patients, corticosteroids were administered to 280 patients (46.1%) at a median daily dose of 227 (interquartile range, 154-443) mg of hydrocortisone equivalents for a median of 7.0 (4.0-13.0) days. Compared with patients who did not receive corticosteroids, patients who received corticosteroids had higher hospital crude mortality (25.5% vs 16.4%, p = 0.007) and fewer ventilator-free days at 28 days (12.5 ± 10.7 vs 15.7 ± 10.1, p < 0.001). The odds ratio association between corticosteroid use and hospital mortality decreased from 1.85 (95% confidence interval 1.12-3.04, p = 0.02) with multivariate logistic regression, to 1.71 (1.05-2.78, p = 0.03) after adjustment for propensity score to receive corticosteroids, to 1.52 (0.90-2.58, p = 0.12) after case-matching on propensity score, and to 0.96 (0.28-3.28, p = 0.95) using marginal structural modeling to adjust for time-dependent between-group differences. CONCLUSIONS: Corticosteroids were commonly prescribed for H1N1pdm09-related critical illness. Adjusting for only baseline between-group differences suggested a significant increased risk of death associated with corticosteroids. However, after adjusting for time-dependent differences, we found no significant association between corticosteroids and mortality. These findings highlight the challenges and importance in adjusting for baseline and time-dependent confounders when estimating clinical effects of treatments using observational studies.

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)0.007
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 score0.068
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.007
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0010.000
Open science0.0000.000
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 head0.036
GPT teacher head0.355
Teacher spread0.319 · 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

Citations97
Published2016
Admission routes3
Has abstractyes

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