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. 2025 Apr 22:18:2247-2262.
doi: 10.2147/JMDH.S515269. eCollection 2025.

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

Mengli Zhang #  1 , Jinyan Wu #  1 , Lulu Wang  2 , Hui Huang  2 , Huan Duan  3 , Fang Xue  1
Affiliations

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

Mengli Zhang et al. J Multidiscip Healthc. .

Abstract

Objective: To create and verify a model that predicts the risk of stress hyperglycemia (SHG) in patients without diabetes after cardiac surgery.

Design: Retrospective analysis.

Methods: This retrospective analysis analyzed patients without diabetes post cardiac surgery at our hospital between June 2020 and December 2023. The 333 patients from June 2020 to June 2022 constituted the developmental sample and the 162 patients from July 2022 to December 2023 constituted the testing sample.

Results: Of 495 patients, 356 (71.9%) developed SHG. Multivariable analysis identified hyperlipidemia, coronary artery bypass grafting (CABG), hypertension, blood transfusion, body mass index (BMI) ≥28 kg/m², and hyperoxia during cardiopulmonary bypass (PaO2≥300mmHg) as significant factors influencing SHG in patients without diabetes after cardiac surgery. The goodness-of-fit test for the risk prediction model based on these factors showed X² = 0.85, P = 0.588. The area under the receiver operating characteristic curve (AUC) for the modeling group was 0.85, with a maximum Youden index of 0.579, an optimal cutoff value of 0.637, a sensitivity of 83.4%, and a specificity of 74.5%. For the external validation group, the AUC was 0.805, with a Youden index of 0.704, 82.6% sensitivity, and 87.8% specificity, and a diagnostic value of 0.839.

Conclusion: Hyperlipidemia, CABG, hypertension, blood transfusion, BMI ≥28 kg/m², and hyperoxia during CPB (PaCO2≥300mmHg) are significant risk factors for SHG in patients without diabetes following cardiac surgery. The model constructed based on these factors can effectively predict the risk of SHG, providing a basis for early intervention measures reduce the incidence of this condition.

Keywords: R software; SHG; cardiac surgery; contributing factors; nursing; risk prediction model; stress hyperglycemia.

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Conflict of interest statement

The authors have no conflicts of interest related to this scientific work.

Figures

Figure 1
Figure 1
The construction of a nomogram for predicting the risk of stress hyperglycemia in patients.
Figure 2
Figure 2
Calibration of risk factors for SHG in patients.
Figure 3
Figure 3
ROC curve of the modeling group.
Figure 4
Figure 4
Displays the ROC curve for the validation group.
See this image and copyright information in PMC

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