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Risk Factors and Predictive Model for Stress Hyperglycemia After Cardiac Surgery in Non-Diabetic Patients

Mengli Zhang1, Jinyan Wu1, Lulu Wang2

  • 1School of Nursing, Bengbu Medical University, Bengbu, Anhui, People's Republic of China.

Journal of Multidisciplinary Healthcare
|April 28, 2025
PubMed

Insights

A new model effectively predicts stress hyperglycemia (SHG) risk in non-diabetic patients after cardiac surgery. Key risk factors include hyperlipidemia, hypertension, and hyperoxia during cardiopulmonary bypass, enabling early intervention.

Area of Science:

  • Cardiology
  • Endocrinology
  • Critical Care Medicine

Background:

  • Stress hyperglycemia (SHG) is a common complication in patients without diabetes following cardiac surgery.
  • Predicting SHG risk is crucial for timely intervention and improved patient outcomes.

Purpose of the Study:

  • To develop and validate a predictive model for SHG risk in non-diabetic patients undergoing cardiac surgery.
  • Identify significant risk factors associated with SHG development post-cardiac surgery.

Main Methods:

  • Retrospective analysis of 495 non-diabetic patients undergoing cardiac surgery (June 2020 - December 2023).
  • Developmental sample (n=333) and external validation sample (n=162).
  • Multivariable analysis to identify significant risk factors and build a predictive model.

Main Results:

  • 71.9% of patients developed SHG.
  • Significant risk factors identified: hyperlipidemia, coronary artery bypass grafting (CABG), hypertension, blood transfusion, BMI ≥28 kg/m², and hyperoxia during cardiopulmonary bypass (PaO₂≥300mmHg).
  • The model demonstrated good predictive performance with an AUC of 0.85 in the development group and 0.805 in the validation group.

Conclusions:

  • Hyperlipidemia, CABG, hypertension, blood transfusion, elevated BMI, and hyperoxia during CPB are significant predictors of SHG in this population.
  • The developed risk prediction model is effective for identifying patients at high risk of SHG.
  • Early identification facilitates targeted interventions to reduce SHG incidence and associated complications.
Abstract

Keywords:
R softwareSHGcardiac surgerycontributing factorsnursingrisk prediction modelstress hyperglycemia

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