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Record W4386591648 · doi:10.1097/ccm.0000000000006028

Elevated Plasma Interleukin-18 Identifies High-Risk Acute Respiratory Distress Syndrome Patients not Distinguished by Prior Latent Class Analyses Using Traditional Inflammatory Cytokines: A Retrospective Analysis of Two Randomized Clinical Trials

2023· article· en· W4386591648 on OpenAlexfundno aff
A. R. Moore, S. Pienkos, Pratik Sinha, Jiazhen Guan, Cecilia O’Kane, Joseph E. Levitt, Jennifer G. Wilson, Manu Shankar‐Hari, Michael A. Matthay, Carolyn S. Calfee, Rebecca M. Baron, Daniel F. McAuley, Angela J. Rogers

Bibliographic record

VenueCritical Care Medicine · 2023
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicInflammasome and immune disorders
Canadian institutionsnot available
FundersMedical Research CouncilGenentechSwedish Orphan BiovitrumChina Scholarship CouncilResearch Councils UKIntensive Care SocietyNational Institute of General Medical SciencesInsmedNational Institute for Health and Care ResearchWellcome TrustNational Heart, Lung, and Blood InstituteNational Institute for Social Care and Health ResearchNational Institutes of HealthQueen's UniversityNovavaxGilead SciencesPublic Health AgencyGlaxoSmithKlineAstraZenecaEli Lilly and Company
KeywordsMedicineARDSRandomized controlled trialInternal medicineHazard ratioLungConfidence interval

Abstract

fetched live from OpenAlex

OBJECTIVES: Interleukin-18 (IL-18) plasma level and latent class analysis (LCA) have separately been shown to predict prognosis and treatment response in acute respiratory distress syndrome (ARDS). IL-18 is a measure of inflammasome activation, a pathway potentially distinct from inflammation captured by biomarkers defining previously published LCA classes. We hypothesized that elevated IL-18 would identify distinct "high-risk" patients not captured by prior LCA classifications. DESIGN: Statins for acutely injured lungs from sepsis (SAILS) and hydroxymethylglutaryl-CoA reductase inhibition with simvastatin in acute lung injury to reduce pulmonary dysfunction trial (HARP-2) are two large randomized, controlled trials in ARDS in which both LCA assignments and IL-18 levels were shown to predict mortality. We first evaluated the overlap between high IL-18 levels (≥ 800 pg/mL) with prior LCA class assignments using McNemar's test and then tested the correlation between IL-18 and LCA biomarkers using Pearson's exact test on log-2 transformed values. Our primary analysis was the association of IL-18 level with 60-day mortality in the hypoinflammatory LCA class, which was assessed using the Fisher exact test and Cox proportional hazards modeling adjusting for age, Acute Physiology and Chronic Health Evaluation score, and gender. Secondary analyses included the association of IL-18 and LCA with mortality within each IL-18/LCA subgroup. SETTING: Secondary analysis of two multicenter, randomized controlled clinical trials of ARDS patients. SUBJECTS: Six hundred eighty-three patients in SAILS and 511 patients in HARP-2. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: We found that 33% of patients in SAILS and HARP-2 were discordant by IL-18 level and LCA class. We further found that IL-18 level was only modestly correlated (0.17-0.47) with cytokines used in the LCA assignment. A substantial subset of individuals classified as hypoinflammatory by LCA (14% of SAILS and 43% of HARP-2) were classified as high risk by elevated IL-18. These individuals were at high risk for mortality in both SAILS (42% 60-d mortality, odds ratio [OR] 3.3; 95% CI, 1.8-6.1; p < 0.001) and HARP-2 (27% 60-d mortality, OR 2.1; 95% CI, 1.2-3.8; p = 0.009). CONCLUSIONS: Plasma IL-18 level provides important additional prognostic information to LCA subphenotypes defined largely by traditional inflammatory biomarkers in two large ARDS cohorts.

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)0.025
metaresearch head score (Gemma)0.032
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 score0.025
Threshold uncertainty score0.135

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0250.032
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.005
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0020.001
Open science0.0010.001
Research integrity0.0010.001
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 head0.079
GPT teacher head0.408
Teacher spread0.329 · 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

Citations22
Published2023
Admission routes1
Has abstractyes

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