Evidence map›Paper›PMID 40341153›Full record

ArticleBMJ open2025

Association between the TyG index and the risk of in-hospital mortality from early sepsis-related acute kidney injury in critically ill patients: a secondary analysis of MIMIC-IV 2008-2022.

Chengyi Feng, Xin Li, Zifang Fan, Zihan Zhang, Jia Di

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Article in BMJ open, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

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Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

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5 · Who and what money

Authors and funding

5 authors.

Chengyi Feng *Third Affiliated Hospital of Soochow University, Changzhou, China.ORCID http://orcid.org/0000-0003-0973-6711
Xin Li *Third Affiliated Hospital of Soochow University, Changzhou, China.
Zifang FanThird Affiliated Hospital of Soochow University, Changzhou, China.
Zihan ZhangThird Affiliated Hospital of Soochow University, Changzhou, China.
Jia DiThird Affiliated Hospital of Soochow University, Changzhou, China brightsea@163.com.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aims to investigate the relationship between the triglyceride-glucose (TyG) index in patients with early sepsis-associated acute kidney injury (SA-AKI) and the risk of in-hospital mortality.

designSecondary data analysis.

settingThis study analysed secondary data from the Medical Information Mart for Intensive Care (MIMIC) 2008-2022.

participantsA total of 1632 participants were enrolled in the final analysis. PRIMARY AND SECONDARY OUTCOME MEASURES: A secondary data analysis study was conducted using data from the MIMIC IV 3.0 database. Participants were divided into four groups based on the quartiles of the TyG index. The primary outcome was all-cause in-hospital mortality. The association between the TyG index and in-hospital mortality among SA-AKI patients was assessed using multivariate COX proportional hazards regression analysis and restricted cubic spline regression analysis. Subgroup and sensitivity analyses were performed to verify the robustness of results.

resultsA total of 1632 patients were included in the study. The in-hospital mortality rate was 31.13%, and the intensive care unit (ICU) mortality rate was 25.25%. Multivariate COX regression analysis showed that the TyG index was independently associated with an increased risk of in-hospital mortality (HR 1.14 (95% CI 1.02 to 1.27); p=0.02) and ICU mortality (HR 1.17; (95% CI 1.04 to 1.32); p=0.01). The restricted cubic spline regression model indicated that the risk of in-hospital and ICU mortality increased linearly with the increase in the TyG index. Sensitivity analysis demonstrated that the effect size and direction were consistent across different subgroups, and the results were stable.

conclusionA high TyG index is associated with increased mortality during hospitalisation in patients with SA-AKI. Larger-scale prospective studies are needed to confirm these findings.

Indexed as

Acute Kidney InjuryBlood GlucoseHospital MortalitySepsisTriglyceridesAgedCritical IllnessFemaleHumansIntensive Care UnitsMaleMiddle AgedProportional Hazards ModelsRisk FactorsBlood GlucoseTriglyceridesAcute renal failureIntensive Care UnitsINTENSIVE & CRITICAL CAREMortality

Identifiers

PMID40341153
PMCPMC12060882

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.