Association of stress hyperglycemia ratio with 1-year all-cause mortality in patients with acute myocardial infarction and chronic kidney disease: a retrospective two-cohort study

Patients with acute myocardial infarction (AMI) complicated by chronic kidney disease (CKD) represent a high-risk population in whom early risk stratification remains challenging. The stress hyperglycemia ratio (SHR) reflects acute glycemic stress independent of chronic glycemic status; however, its independent prognostic value in this population has not been systematically evaluated in externally validated cohorts. This retrospective two-cohort study included patients with AMI and CKD from the MIMIC-IV database and an independent external coronary care unit cohort. The primary endpoint was 1-year all-cause mortality. Multivariable Cox proportional hazards models assessed the SHR-mortality association. Incremental predictive value was evaluated using discrimination, reclassification, and decision curve analyses. Machine learning models were additionally developed and externally validated for exploratory risk prediction. We included 606 patients from MIMIC-IV and 263 from the external cohort. In fully adjusted Cox models, higher SHR was independently associated with 1-year mortality (HR 1.14, 95% CI 1.03–1.26), with a graded association across quartiles (highest vs. lowest: HR 1.64, 95% CI 1.17–2.32). The prognostic effect was most pronounced during the early post-admission phase. Adding SHR to the baseline model yielded a non-significant improvement in overall discrimination (C-index 0.699 to 0.706; ΔAUC 0.008, P = 0.058), but improved correct reclassification of non-events. The XGBoost model achieved strong internal discrimination (AUC 0.872) but demonstrated attenuated external calibration (AUC 0.718; Brier score 0.253; calibration slope 0.298), underscoring the need for cohort-specific recalibration. Elevated SHR is independently associated with 1-year all-cause mortality in patients with AMI and CKD, particularly during the early post-admission phase. As a readily calculable index of acute metabolic stress, SHR may serve as a clinically accessible adjunctive biomarker to complement early risk stratification in this high-risk population, pending prospective validation and local model recalibration prior to broader clinical implementation.

Authors

Institutions

Publication Details

Journal
European journal of medical research
Published
2026-05-17
DOI
https://doi.org/10.1186/s40001-026-04585-5
Primary Topic
Hyperglycemia and glycemic control in critically ill and hospitalized patients
Type
article
Field-Weighted Citation Impact
0.00
Controls
|||
ALL TIME
JAN
FEB
MAR
APR
MAY
JUN
JUL
AUG
SEP
OCT
article

Association of stress hyperglycemia ratio with 1-year all-cause mortality in patients with acute myocardial infarction and chronic kidney disease: a retrospective two-cohort study

Qianyun Wu, Yuehua Chen, F. Zeng, Zongliang Yu et al.
European journal of medical research
Hyperglycemia and glycemic control in critically ill and hospitalized patients
article

Association of stress hyperglycemia ratio with 1-year all-cause mortality in patients with acute myocardial infarction and chronic kidney disease: a retrospective two-cohort study

Qianyun Wu, Yuehua Chen, F. Zeng, Zongliang Yu, Jiahui Zhou, Feier Wu, Ning Zhang
article en

Abstract

Patients with acute myocardial infarction (AMI) complicated by chronic kidney disease (CKD) represent a high-risk population in whom early risk stratification remains challenging. The stress hyperglycemia ratio (SHR) reflects acute glycemic stress independent of chronic glycemic status; however, its independent prognostic value in this population has not been systematically evaluated in externally validated cohorts. This retrospective two-cohort study included patients with AMI and CKD from the MIMIC-IV database and an independent external coronary care unit cohort. The primary endpoint was 1-year all-cause mortality. Multivariable Cox proportional hazards models assessed the SHR-mortality association. Incremental predictive value was evaluated using discrimination, reclassification, and decision curve analyses. Machine learning models were additionally developed and externally validated for exploratory risk prediction. We included 606 patients from MIMIC-IV and 263 from the external cohort. In fully adjusted Cox models, higher SHR was independently associated with 1-year mortality (HR 1.14, 95% CI 1.03–1.26), with a graded association across quartiles (highest vs. lowest: HR 1.64, 95% CI 1.17–2.32). The prognostic effect was most pronounced during the early post-admission phase. Adding SHR to the baseline model yielded a non-significant improvement in overall discrimination (C-index 0.699 to 0.706; ΔAUC 0.008, P = 0.058), but improved correct reclassification of non-events. The XGBoost model achieved strong internal discrimination (AUC 0.872) but demonstrated attenuated external calibration (AUC 0.718; Brier score 0.253; calibration slope 0.298), underscoring the need for cohort-specific recalibration. Elevated SHR is independently associated with 1-year all-cause mortality in patients with AMI and CKD, particularly during the early post-admission phase. As a readily calculable index of acute metabolic stress, SHR may serve as a clinically accessible adjunctive biomarker to complement early risk stratification in this high-risk population, pending prospective validation and local model recalibration prior to broader clinical implementation.

European journal of medical research
Jiangsu University (CN), First People's Hospital of Kunshan (CN)
Openalex Percentile: Top 7%
Hyperglycemia and glycemic control in critically ill and hospitalized patients
AI Navigator

Ask Laika to Summarize, Analyze, and Connect papers live on the map.

Summarize Papers & Methodologies

Extract key findings, datasets, and comparative methods across publications.

Benchmark Rankings & Visual Analytics

Rank top research institutions, authors, funders, topics, and journals by Field-Weighted Citation Impact (FWCI) and paper volume with instant charts.

Connect Distant Disciplines

Bridge topological clusters on the map to find hidden collaborative intersections.