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Record W4406023543 · doi:10.1007/s10238-024-01545-3

Putative biomarkers of hepatic dysfunction in critically ill sepsis patients

2025· article· en· W4406023543 on OpenAlexafffund
Logan R. Van Nynatten, Maitray A. Patel, Mark Daley, Michael R. Miller, Gediminas Cepinskas, Marat Slessarev, James A. Russell, Douglas D. Fraser

Bibliographic record

VenueClinical and Experimental Medicine · 2025
Typearticle
Languageen
FieldMedicine
TopicSepsis Diagnosis and Treatment
Canadian institutionsSt. Paul's HospitalUniversity of British ColumbiaLondon Health Sciences CentreWestern University
FundersLondon Health Sciences FoundationAcademic Medical Organization of Southwestern Ontario
KeywordsSepsisMedicineInternal medicineOrgan dysfunctionGastroenterologySOFA scoreHepatic dysfunctionHematologyBilirubinSeverity of illness

Abstract

fetched live from OpenAlex

Sepsis is a major cause of morbidity and mortality worldwide. Among the various types of end-organ damage associated with sepsis, hepatic injury is linked to significantly higher mortality rates compared to dysfunction in other organ systems. This study aimed to investigate potential biomarkers of hepatic injury in sepsis patients through a multi-center, case-control approach. We enrolled three matched cohorts: 37 sepsis patients with hepatic dysfunction (S-HD), 37 sepsis patients without hepatic dysfunction (S-CON), and 18 healthy controls (HC). We measured five proposed biomarkers of hepatic dysfunction-ARG1, MDH1, GSTα, 5-NT, and SDH-using multiplex immunoassays. These biomarkers were compared to traditional markers of hepatic dysfunction, including albumin, bilirubin, ALT, AST, and GGT, across the cohorts using both conventional statistical methods and machine learning techniques. The median age of participants was comparable across cohorts: S-HD (65.0 years, IQR 49.5-82.5), S-CON (65.0 years, IQR 48.0-81.5), and HC (62.5 years, IQR 53.0-65.0; P = 0.794). Patients with hepatic dysfunction (S-HD) exhibited higher illness severity scores compared to those without hepatic dysfunction (S-CON): MODS scores were median 7.0 (IQR 4.0-10.0) in S-HD versus median 4.0 (IQR 2.0-7.0) in S-CON (P = 0.005), and SOFA scores were median 7.0 (IQR 4.0-11.0) in S-HD versus median 3.0 (IQR 2.0-6.0) in S-CON (P < 0.001). Hemoglobin and platelet counts were lower, while creatinine levels were higher in S-HD compared to S-CON (P < 0.05). On ICU Day 1, bilirubin, ALT, AST, GGT, and INR were significantly elevated in S-HD relative to S-CON (P ≤ 0.001), and albumin levels were lower (P < 0.05). Additionally, ARG1, GSTα, 5-NT, and SDH were significantly higher in S-HD patients on ICU Day 1 compared to S-CON (P < 0.05). ARG1, MDH1, and SDH showed positive correlations with AST, ALT, and MODS (P < 0.01). From ICU Day 1 to Day 7, ARG1, GSTα, SDH, and AST levels significantly decreased in S-HD patients (P < 0.05), whereas MDH1 and 5-NT levels did not. Among the proposed biomarkers, GSTα and 5-NT did not correlate with traditional hepatic dysfunction markers but were significant in identifying S-HD patients (feature importance 0.131 and 0.097, respectively) in a random forest classification model. This comprehensive model demonstrated excellent performance in distinguishing sepsis patients with hepatic injury, with sensitivity 0.93, specificity 0.94, NPV 0.94, PPV 0.94, and AUC 0.94. The biomarkers ARG1, MDH1, GSTα, 5-NT, and SDH show promise as novel indicators of hepatic dysfunction associated with sepsis. This study provides a foundational basis for subsequent research aimed at characterizing and clinically validating these markers. Future investigations should focus on integrating these potential biomarkers into routine laboratory assessments for sepsis and related hepatic injury.

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)0.002
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 score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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 head0.067
GPT teacher head0.431
Teacher spread0.364 · 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

Citations13
Published2025
Admission routes2
Has abstractyes

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