Evidence map›Paper›PMID 41605586›Full record

ArticleBMJ open2026

Predictive value of stress hyperglycaemia ratio and haemoglobin glycation index for mortality risks in critically ill patients: a comparative retrospective analysis of the MIMIC-IV database using machine learning-based predictive modelling.

Wanlu Zhou, Min Zheng, Ting Wu, Ruizheng Shi

Abstract readComparative Study
In one paragraph

Article in BMJ open, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

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1 · What the graph read from it

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2 · The registry

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

4 authors.

Wanlu ZhouDepartment of Cardiovascular Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Min ZhengDepartment of Cardiovascular Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.
Ting WuDepartment of Cardiovascular Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China.ORCID http://orcid.org/0000-0003-1708-2869
Ruizheng ShiDepartment of Cardiovascular Medicine, Xiangya Hospital, Central South University, Changsha, Hunan, China xyshiruizheng@csu.edu.cn.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

objectivesThis study aimed to evaluate the association between the stress hyperglycaemia ratio (SHR) and the Haemoglobin Glycation Index (HGI), and mortality risks in critically ill patients.

designA retrospective study and machine learning (ML)-based predictive modelling.

settingThis retrospective cohort study used data from the Medical Information Mart for Intensive Care-IV (MIMIC-IV) database.

participantsA total of 3106 patients were included in the study and were divided into different groups according to the value level of SHR and HGI. PRIMARY OUTCOME MEASURE: 360-day mortality.

resultsWhen treated the SHR as a continuous variable, a significant correlation exists between the SHR and 360-day mortality risks in critically ill patients (HR, 1.32; 95% CI 1.13 to 1.55). When regarded the SHR as a categorical variable, patients in the highest group were significantly associated with an increased risk of 360-day mortality compared with that of those in the lowest group (HR, 1.38; 95% CI 1.14 to 1.68). HGI, when treated as a continuous variable, was also closely associated with 360-day mortality (HR, 0.94; 95% CI 0.89 to 1.00). According to the results, the SHR index outperformed HGI at predicting all-cause 360-day mortality and adding the SHR index to the basic model for 360-day mortality improved its predictive ability (area under the curve, 0.818 for the basic model vs 0.821 for the basic model+SHR index). Furthermore, the ML-based model demonstrated the crucial contribution of SHR in predicting 360-day mortality risk of critically ill patients. Consistent with the 360-day mortality results, similar and statistically significant trends towards higher mortality at both 30 days and 90 days were observed.

conclusionsSHR and HGI showed a strong association with 360-day and short-term mortality risks. The SHR index appears to be the most promising index for prevention and risk stratification in critically ill patients.

Indexed as

Critical IllnessGlycated HemoglobinHyperglycemiaAgedDatabases, FactualFemaleHumansIntensive Care UnitsMachine LearningMaleMiddle AgedPredictive Learning ModelsPredictive Value of TestsRetrospective StudiesRisk AssessmentRisk FactorsGlycated HemoglobinAdult intensive & critical careMortalityOther metabolic, e.g. iron, porphyriaRisk Factors

Identifiers

PMID41605586
PMCPMC12853491

What Socratic holds

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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 Socratic graph.