Evidence map›Paper›PMID 42745751›Full record

ArticleFrontiers in endocrinology2026

Nonlinear association between stress hyperglycemia ratio and mortality in AHRF: validation through consensus clustering and supervised machine learning models.

Qi Zhang, Tao Wang, Haoyue Wang, Liye Ji, Yang Zhao, Xiaoyong Geng, Zhiyong Wang, Yaoyao Tang, Mingxing Fang

Abstract read
In one paragraph

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

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

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

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

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors.

Qi Zhang *Department of Critical Care Medicine, Hebei Medical University Third Hospital, Shijiazhuang, China.
Tao Wang *Department of Medical Imaging, The People's Hospital of Guizhou, Guiyang, China.
Haoyue Wang *Applied Statistics, College of Science, Hebei University of Technology, Tianjin, China.
Liye JiDepartment of Critical Care Medicine, Hebei Medical University Third Hospital, Shijiazhuang, China.
Yang ZhaoDepartment of Endocrinology, Shijiazhuang Traditional Chinese Hospital, Shijiazhuang, China.
Xiaoyong GengDepartment of Cardiology, Hebei Medical University Third Hospital, Shijiazhuang, China.
Zhiyong WangDepartment of Critical Care Medicine, Hebei Medical University Third Hospital, Shijiazhuang, China.
Yaoyao TangDepartment of Critical Care Medicine, Hebei Medical University Third Hospital, Shijiazhuang, China.
Mingxing FangDepartment of Critical Care Medicine, Hebei Medical University Third Hospital, Shijiazhuang, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Purpose: Acute hypoxemic respiratory failure (AHRF), a life-threatening condition in critically ill patients, is associated with poor clinical outcomes. Although the stress hyperglycemia ratio (SHR) may reflect acute metabolic stress, its prognostic role in AHRF remains unclear. This study investigated the association between SHR and mortality risk in patients with AHRF. Methods: We retrospectively analyzed 2514 patients with AHRF from the MIMIC-IV database. Restricted cubic spline analysis was used to assess the potential non-linear association between SHR and 28-day all-cause mortality. Kaplan-Meier survival analysis and Cox proportional hazards models were used to evaluate mortality risk. Secondary predictive analyses assessed the incremental value of SHR beyond SOFA and evaluated 101 candidate machine-learning models or model combinations. Consensus clustering was used as an exploratory approach to identify data-driven clinical phenotypes. Results: In the MIMIC-IV cohort, 28-day all-cause mortality increased most clearly among patients with elevated SHR. In the primary parsimonious Cox model, the highest SHR quartile was associated with increased 28-day all-cause mortality compared with the lowest quartile (HR = 2.972, 95% CI: 2.108-4.189, P < 0.001). Restricted cubic spline analysis suggested a possible non-linear association, with a data-derived inflection point around 0.929 in this cohort. Event-distribution and extreme-value sensitivity analyses indicated that the association between elevated SHR and mortality was not driven solely by extreme SHR values. In secondary predictive analyses, SHR improved discrimination beyond SOFA, and the final CoxBoost + SuperPC model achieved an optimism-corrected C-index of 0.803 in the training cohort and a C-index of 0.826 in the independent validation cohort. Consensus clustering identified three exploratory clinical phenotypes with different severity profiles and mortality risks. Phenotype-stratified analyses suggested a clearer SHR-mortality association in Phenotype II, although formal interaction testing with dichotomized SHR was not statistically significant. Conclusion: SHR was associated with 28-day all-cause mortality in patients with AHRF and may serve as a potential prognostic marker for risk stratification. The most consistent evidence supported an increased mortality risk among patients with elevated SHR, whereas the possible nonlinear pattern, particularly in the lower SHR range, requires further validation. Exploratory phenotype-specific findings should be interpreted cautiously and validated in independent cohorts.

Indexed as

HyperglycemiaHypoxiaRespiratory InsufficiencyStress, PhysiologicalSupervised Machine LearningAgedCluster AnalysisClustering AlgorithmsFemaleHumansMaleMiddle AgedPredictive Learning ModelsPrognosisProportional Hazards ModelsRetrospective Studiesacute hypoxemic respiratory failurecluster analysismachine learningmortalitystress hyperglycemia ratio

Identifiers

PMID42745751
PMCPMC13574610

What Socratic holds

Textmetadata
Read underepoch 390

Registered trials

None linked

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.