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Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on
Wenlong Zou1, Haipeng Zhao2, Ming Ren3
1Department of Neurology, Beijing Anzhen Hospital, Capital Medical University, Beijing, China.
Insights
Bayesian network models can predict acute ischemic stroke (AIS) after off-pump coronary artery bypass grafting (OPCABG). Key risk factors include female sex, stroke history, carotid stenosis, and elevated GA, D-dimer, RDW, and BUN levels.
Area of Science:
- Cardiovascular Surgery
- Neurology
- Medical Informatics
Background:
- Acute ischemic stroke (AIS) is a potential complication following off-pump coronary artery bypass grafting (OPCABG).
- Identifying risk factors and developing predictive models for AIS post-OPCABG is crucial for patient management.
Purpose of the Study:
- To identify risk factors for AIS in patients undergoing OPCABG.
- To develop and validate predictive models for AIS using Bayesian network (BN) methods.
Main Methods:
- Retrospective analysis of electronic health records from 10,184 adult patients who underwent OPCABG.
- Development of a BN model using a tabu search algorithm on an 80% training set.
- Validation of the BN model against a 20% test set, comparing its performance with logistic regression models using ROC and calibration curves.
Main Results:
- 151 patients (1.5%) developed AIS post-OPCABG.
- Significant risk factors for AIS included female sex, history of ischemic stroke, severe carotid artery stenosis, and elevated glycated albumin (GA), D-dimer, erythrocyte distribution width (RDW), and blood urea nitrogen (BUN) levels.
- BN models outperformed logistic regression models, achieving higher accuracy and AUC in both training and testing datasets.
Conclusions:
- Female gender, ischemic stroke history, carotid stenosis, RDW-CV, GA, D-dimer, BUN, and type 2 diabetes mellitus are potential predictors of AIS after OPCABG in this Chinese cohort.
- The BN model demonstrated superior predictive efficiency compared to logistic regression.
- BN models show promise for early diagnosis and prevention of AIS in patients undergoing OPCABG.
Background:
This study aimed to identify the risk factors of acute ischemic stroke (AIS) occurring during hospitalization in patients following off-pump coronary artery bypass grafting (OPCABG) and utilize Bayesian network (BN) methods to establish predictive models for this disease.
Methods:
Data were collected from the electronic health records of adult patients who underwent OPCABG at Beijing Anzhen Hospital from January 2018 to December 2022. Patients were allocated to the training and test sets in an 8:2 ratio according to the principle of randomness. Subsequently, a BN model was established using the training dataset and validated against the testing dataset. The BN model was developed using a tabu search algorithm. Finally, receiver operating characteristic (ROC) and calibration curves were plotted to assess the extent of disparity in predictive performance between the BN and logistic models.
Results:
A total of 10,184 patients (mean (SD) age, 62.45 (8.7) years; 2524 (24.7%) females) were enrolled, including 151 (1.5%) with AIS and 10,033 (98.5%) without AIS. Female sex, history of ischemic stroke, severe carotid artery stenosis, high glycated albumin (GA) levels, high D-dimer levels, high erythrocyte distribution width (RDW), and high blood urea nitrogen (BUN) levels were strongly associated with AIS. Type 2 diabetes mellitus (T2DM) was indirectly linked to AIS through GA and BUN. The BN models exhibited superior performance to logistic regression in both the training and testing sets, achieving accuracies of 72.64% and 71.48%, area under the curve (AUC) of 0.899 (95% confidence interval (CI), 0.876-0.921) and 0.852 (95% CI, 0.769-0.935), sensitivities of 91.87% and 89.29%, and specificities of 72.35% and 71.24% (using the optimal cut-off), respectively.
Conclusion:
Female gender, IS history, carotid stenosis (> 70%), RDW-CV, GA, D-dimer, BUN, and T2DM are potential predictors of IS in our Chinese cohort. The BN model demonstrated greater efficiency than the logistic regression model. Hence, employing BN models could be conducive to the early diagnosis and prevention of AIS after OPCABG.
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