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Association between SHR and mortality in critically ill patients with CVD: a retrospective analysis and machine
Wanlu Zhou1, Junxi Liao2, Ting Wu2
1Department of Cardiovascular Medicine, The third Xiangya Hospital, Central South University, No.138 Tongzipo Road, Yuelu District, Changsha, 410008, Hunan Province, China.
Insights
The stress hyperglycemia ratio (SHR) is linked to higher mortality in critically ill cardiovascular disease (CVD) patients. This glucose metabolism measure shows promise as an illness severity indicator.
Area of Science:
- Critical Care Medicine
- Cardiovascular Disease Research
- Metabolic Syndrome Studies
Background:
- The stress hyperglycemia ratio (SHR) is an emerging biomarker for illness severity in critical care.
- Its association with adverse outcomes in critically ill cardiovascular disease (CVD) patients requires further investigation.
Purpose of the Study:
- To evaluate the relationship between SHR and mortality in critically ill patients with CVD.
- To assess SHR's predictive value for adverse outcomes in this patient cohort.
Main Methods:
- Analysis of clinical data from 1,913 critically ill CVD patients from the MIMIC-IV database.
- Utilized Restricted Cubic Spline (RCS) regression, Cox proportional hazards models, and Kaplan-Meier survival analysis.
- Developed and validated machine learning (ML) predictive models, assessing SHR's role using SHAP analysis.
Main Results:
- A significant positive linear association was observed between SHR and mortality risk.
- Higher SHR levels were consistently linked to increased mortality in critically ill CVD patients.
- Machine learning models, particularly XGBoost, showed strong predictive performance, with SHR being a critical predictor.
Conclusions:
- SHR is a significant independent predictor of mortality in critically ill patients with CVD.
- SHR demonstrates potential as a valuable prognostic indicator in this high-risk population, supported by ML-based models.
Background:
The stress hyperglycemia ratio (SHR), a measure of glucose metabolism, has emerged as a novel indicator of illness severity in critically ill patients. This study aims to evaluate the association between SHR and adverse outcomes in critically ill patients with cardiovascular disease (CVD).
Methods:
Clinical data of 1,913 critically ill patients with CVD were extracted from the MIMIC-IV database. The primary outcomes were 360-day, 28-day, and 7-day mortality. Restricted cubic spline (RCS) regression and Cox proportional hazards models were utilized to assess the relationship between SHR and mortality risk in critically ill patients with CVD. Kaplan-Meier survival analysis was conducted to estimate survival rates across SHR quartiles. Additionally, five predictive models were developed using machine learning (ML) algorithms, and the predictive value of SHR was assessed using the SHapley Additive exPlanation (SHAP) algorithm.
Results:
RCS regression analyses demonstrated a positive linear association between SHR and mortality risk, indicating that higher SHR were linked to an increased risk of adverse outcomes. Kaplan-Meier curves and Cox regression further confirmed that a higher SHR was significantly associated with an elevated risk of mortality in patients with CVD compared to a lower SHR. Predictive machine learning (ML) models were constructed. EXtreme Gradient Boosting (XGBoost) algorithm demonstrated the best performance, with SHR playing a critical role in prediction as identified by SHAP analysis.
Conclusions:
SHR is significantly correlated with mortality in critically ill patients with CVD. Based on ML-based predictive models and ROC curves, SHR appears to be a promising indicator in this population.
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