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Record W4377245534 · doi:10.1056/evidoa2300034

Patient-Level Meta-Analysis of Low-Dose Hydrocortisone in Adults with Septic Shock

2023· article· en· W4377245534 on OpenAlexaff
Romain Pirracchio, Djillali Annane, Andre Kurepa Waschka, François Lamontagne, Yaseen M. Arabi, Pierre‐Édouard Bollaert, Laurent Billot, Bin Du, Josef Briegel, Jeremy Cohen, Simon Finfer, Anthony Gordon, Naomi Hammond, Hervé Hyvernat, Didier Keh, Yi Li, Ling Liu, G. Umberto Meduri, Liliana Mirea, John Myburgh, Charles L. Sprung, Neijla Tilouche, Surat Tongyoo, Balasubramanian Venkatesh, Ruiqiang Zheng, Anthony Delaney

Bibliographic record

VenueNEJM Evidence · 2023
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsUniversité de SherbrookeCentre Hospitalier Universitaire de Sherbrooke
FundersNIHR Imperial Biomedical Research CentreNational Health and Medical Research CouncilMedical Research CouncilAgence Nationale de la Recherche
KeywordsMedicineFludrocortisoneSeptic shockConfidence intervalHydrocortisoneRelative riskIntensive care unitInternal medicineMeta-analysisPlaceboAnesthesiaAdverse effectSepsis

Abstract

fetched live from OpenAlex

BACKGROUND: Trials and study-level meta-analyses have failed to resolve the role of corticosteroids in the management of patients with septic shock. Patient-level meta-analyses may provide more precise estimates of treatment effects, particularly subgroup effects. METHODS: We pooled individual patient data from septic shock trials investigating the adjunctive use of intravenous hydrocortisone. The primary outcome was 90-day all-cause mortality, and it was also analyzed across predefined subgroups. Secondary outcomes included mortality at intensive care unit and hospital discharge, at 28 and 180 days, and vasopressor-, ventilator-, and organ failure–free days. Adverse events included superinfection, muscle weakness, hyperglycemia, hypernatremia, and gastroduodenal bleeding. RESULTS: Of 24 eligible trials (n=8528), 17 (n=7882) provided individual patient data, and 7 (n=5929) provided 90-day mortality. The marginal relative risk (RR) for 90-day mortality of hydrocortisone versus placebo was 0.93 (95% confidence interval [CI], 0.82 to 1.04; P=0.22; moderate certainty). It was 0.86 (95% CI, 0.79 to 0.92) for hydrocortisone with fludrocortisone and 0.96 (95% CI, 0.82 to 1.12) without fludrocortisone. There was no significant differential treatment effect across subgroups. Hydrocortisone was associated with little to no difference in any of the secondary outcomes except vasopressor-free days (mean difference, 1.24 days; 95% CI, 0.74 to 1.73; high certainty). Hydrocortisone may not be associated with an increase in the risk of superinfection (RR, 1.04; 95% CI, 0.95 to 1.15; low certainty), hyperglycemia (RR, 1.05; 95% CI, 0.98 to 1.12; low certainty), or gastroduodenal bleeding (RR, 1.11; 95% CI, 0.83 to 1.48; low certainty). Hydrocortisone may be associated with an increase in the risk of hypernatremia (RR, 2.01; 95% CI, 1.56 to 2.60; low certainty) and muscle weakness (n=2647; RR, 1.73; 95% CI, 1.49 to 1.99; low certainty). CONCLUSIONS: In this patient-level meta-analysis, hydrocortisone compared with placebo was not associated with reduced mortality for patients with septic shock. (Funded by “Programme d’Investissements d’Avenir,” a research Professorship from the National Institute of Health and Care Research, Leadership Fellowships from the National Health and Medical Research Council of Australia, and Emerging Leaders Fellowship from the National Health and Medical Research Council of Australia; PROSPERO registration number, CRD42017062198.)

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)0.022
metaresearch head score (Gemma)0.043
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: Meta-analysis
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.022
Threshold uncertainty score0.114

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0220.043
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0170.048
Bibliometrics0.0040.004
Science and technology studies0.0000.001
Scholarly communication0.0040.001
Open science0.0020.001
Research integrity0.0020.002
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 head0.248
GPT teacher head0.375
Teacher spread0.128 · 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 designMeta-analysis
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

Citations64
Published2023
Admission routes1
Has abstractyes

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