Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network
Abstract 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...
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BMC
2024-11-01
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| Series: | BMC Medical Informatics and Decision Making |
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| Online Access: | https://doi.org/10.1186/s12911-024-02762-2 |
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| author | Wenlong Zou Haipeng Zhao Ming Ren Chaoxiong Cui Guobin Yuan Boyi Yuan Zeyu Ji Chao Wu Bin Cai Tingting Yang Jinjun Zou Guangzhi Liu |
| author_facet | Wenlong Zou Haipeng Zhao Ming Ren Chaoxiong Cui Guobin Yuan Boyi Yuan Zeyu Ji Chao Wu Bin Cai Tingting Yang Jinjun Zou Guangzhi Liu |
| author_sort | Wenlong Zou |
| collection | DOAJ |
| description | Abstract 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. |
| format | Article |
| id | doaj-art-d9d9db0ea3bd415b8c5e67eef27f81b4 |
| institution | Kabale University |
| issn | 1472-6947 |
| language | English |
| publishDate | 2024-11-01 |
| publisher | BMC |
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| series | BMC Medical Informatics and Decision Making |
| spelling | doaj-art-d9d9db0ea3bd415b8c5e67eef27f81b42024-11-24T12:29:00ZengBMCBMC Medical Informatics and Decision Making1472-69472024-11-0124111210.1186/s12911-024-02762-2Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian networkWenlong Zou0Haipeng Zhao1Ming Ren2Chaoxiong Cui3Guobin Yuan4Boyi Yuan5Zeyu Ji6Chao Wu7Bin Cai8Tingting Yang9Jinjun Zou10Guangzhi Liu11Department of Neurology, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Cardiovascular surgery, Beijing Chaoyang Hospital, Capital Medical UniversityDepartment of Neurology, Shanghai Blue Cross Brain HospitalDepartment of Ophthalmology, Qingdao Central Hospital, University of Health and Rehabilitation SciencesDepartment of Neurology, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Neurology, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Neurology, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Neurology, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Neurology, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Neurology, Beijing Anzhen Hospital, Capital Medical UniversityDepartment of Ultrasonography, Qingdao Endocrine & Diabetes HospitalDepartment of Neurology, Beijing Anzhen Hospital, Capital Medical UniversityAbstract 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.https://doi.org/10.1186/s12911-024-02762-2Bayesian networkStrokeCoronary artery bypass graftingPrediction modelRisk factorType 2 diabetes mellitus |
| spellingShingle | Wenlong Zou Haipeng Zhao Ming Ren Chaoxiong Cui Guobin Yuan Boyi Yuan Zeyu Ji Chao Wu Bin Cai Tingting Yang Jinjun Zou Guangzhi Liu Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network BMC Medical Informatics and Decision Making Bayesian network Stroke Coronary artery bypass grafting Prediction model Risk factor Type 2 diabetes mellitus |
| title | Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network |
| title_full | Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network |
| title_fullStr | Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network |
| title_full_unstemmed | Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network |
| title_short | Risk factors and prediction model for acute ischemic stroke after off-pump coronary artery bypass grafting based on Bayesian network |
| title_sort | risk factors and prediction model for acute ischemic stroke after off pump coronary artery bypass grafting based on bayesian network |
| topic | Bayesian network Stroke Coronary artery bypass grafting Prediction model Risk factor Type 2 diabetes mellitus |
| url | https://doi.org/10.1186/s12911-024-02762-2 |
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