ArticleCardiovascular diabetology2024
Association between the stress hyperglycemia ratio and 28-day all-cause mortality in critically ill patients with sepsis: a retrospective cohort study and predictive model establishment based on machine learning.
Article in Cardiovascular diabetology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 173 papers, 3 of them syntheses that pooled it.
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Who cites it
173 citing papers in PubMed, 3 syntheses or guidelines pooled it.
- Predictive performance of stress hyperglycemia ratio for poor prognosis in critically ill patients: a systematic review and dose-response meta-analysis.European journal of medical research · 2025Pooled it
- Relationship between stress hyperglycaemic ratio (SHR) and critical illness: a systematic review.Cardiovascular diabetology · 2025Pooled it
- Association between stress hyperglycemia ratio and all-cause mortality among ICU patients with sepsis: a systematic review and meta-analysis.Frontiers in medicine · 2025Pooled it
- Prognostic value of stress hyperglycemia ratio in critically Ill patients with acute kidney injury: a machine learning-driven retrospective cohort analysis.Health information science and systems · 2026Article
- METTL7B Expression in Sepsis: Association With Disease Severity and 28-Day Mortality.Journal of clinical laboratory analysis · 2026Article
- Predictive value of stress-induced hyperglycemia for 28-day all-cause mortality in ICU patients with sepsis: a MIMIC-IV-based retrospective cohort study.Clinics (Sao Paulo, Brazil) · 2026Article
- Association between ePWV and delirium risk in icu sepsis patients: a retrospective analysis based on the MIMIC database and development of a predictive model.Clinics (Sao Paulo, Brazil) · 2026Article
- Development and Validation of an Interpretable Machine Learning Model for Predicting Early Pulmonary Metastasis Risk in Osteosarcoma.Cancer medicine · 2026Article
- Correlation between SHR and 28-day all-cause mortality risk in patients with spontaneous intracerebral hemorrhage.BMC neurology · 2026Article
- Nonlinear association between the stress hyperglycemia ratio and 180-day mortality in critically ill patients undergoing extracorporeal circulation during open heart surgery: a MIMIC-IV cohort study.Journal of cardiothoracic surgery · 2026Article
- Combined assessment of triglyceride-glucose index and stress hyperglycemia ratio to predict all-cause mortality in critically ill patients with intracerebral hemorrhage.BMC neurology · 2026Article
- An explainable machine learning method for identifying key atherogenic lipid biomarkers in abdominal obesity among the Southern Chinese population.Lipids in health and disease · 2026Article
- Nonlinear correlation between lactate levels and 28-day all-cause mortality in patients with sepsis complicated by acute respiratory distress syndrome (ARDS): a retrospective study based on the MIMIC-IV database.BMC medical informatics and decision making · 2026Article
- Association between stress hyperglycemia ratio and cognitive function in older patients with diabetes or prediabetes: a cross-sectional study from the NHANES.Endocrine journal · 2026Article
- Stress Hyperglycemia Ratio Predicts 28-Day Mortality in Patients with AMI Cardiogenic Shock: Insights from a Large-Scale Cohort.Journal of clinical medicine · 2026Article
- Identification and Verification of SLC6A15 Involved in Keloid via Bioinformatics Analysis and Machine Learning.Biochemical genetics · 2026Article
- [A clinical auxiliary differential diagnostic model for thoracolumbar spinal tuberculosis based on routine laboratory indicators: A multicenter retrospective cohort study].Zhong nan da xue xue bao. Yi xue ban = Journal of Central South University. Medical sciences · 2026Article
- An explainable machine learning model for early pediatric sepsis prediction using cytokine and routine laboratory data.Pediatric research · 2026Article
- Dynamic prognostic nutritional index and 28-day mortality in sepsis: a joint modeling analysis.Nutrition & metabolism · 2026Article
- Stress-hyperglycemia ratio and glycemic variability predict severity and mortality in sepsis-associated acute respiratory distress syndrome.World journal of emergency medicine · 2026Article
113 more citing papers are in PubMed but not listed here.
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
backgroundSepsis is a severe form of systemic inflammatory response syndrome that is caused by infection. Sepsis is characterized by a marked state of stress, which manifests as nonspecific physiological and metabolic changes in response to the disease. Previous studies have indicated that the stress hyperglycemia ratio (SHR) can serve as a reliable predictor of adverse outcomes in various cardiovascular and cerebrovascular diseases. However, there is limited research on the relationship between the SHR and adverse outcomes in patients with infectious diseases, particularly in critically ill patients with sepsis. Therefore, this study aimed to explore the association between the SHR and adverse outcomes in critically ill patients with sepsis.
methodsClinical data from 2312 critically ill patients with sepsis were extracted from the MIMIC-IV (2.2) database. Based on the quartiles of the SHR, the study population was divided into four groups. The primary outcome was 28-day all-cause mortality, and the secondary outcome was in-hospital mortality. The relationship between the SHR and adverse outcomes was explored using restricted cubic splines, Cox proportional hazard regression, and Kaplan‒Meier curves. The predictive ability of the SHR was assessed using the Boruta algorithm, and a prediction model was established using machine learning algorithms.
resultsData from 2312 patients who were diagnosed with sepsis were analyzed. Restricted cubic splines demonstrated a "U-shaped" association between the SHR and survival rate, indicating that an increase in the SHR is related to an increased risk of adverse events. A higher SHR was significantly associated with an increased risk of 28-day mortality and in-hospital mortality in patients with sepsis (HR > 1, P < 0.05) compared to a lower SHR. Boruta feature selection showed that SHR had a higher Z score, and the model built using the rsf algorithm showed the best performance (AUC = 0.8322).
conclusionThe SHR exhibited a U-shaped relationship with 28-day all-cause mortality and in-hospital mortality in critically ill patients with sepsis. A high SHR is significantly correlated with an increased risk of adverse events, thus indicating that is a potential predictor of adverse outcomes in patients with sepsis.
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