CHA2DS2-VASc score as predictor of ischemic stroke in patients undergoing coronary artery bypass grafting and percutaneous coronary intervention

Tian, Yaohua; Yang, Chenlu; Liu, Hui

doi:10.1038/s41598-017-11923-5

Cited by 8 publications

(8 citation statements)

References 32 publications

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“…The score of CHA2DS2-VASc was employed as a risk-measurement tool; it was recorded in treatment guidelines for stroke prevention and is a factor for predicting stroke. Tian et al suggest that CHA2DS2-VASc score should be on the clinical application [10]. This study demonstrated two significant findings: first, preoperative 1-year and perioperative variables are significant predictors.…”

Section: Discussionsupporting

confidence: 50%

“…In this research, the baseline characteristic variables were sex, Charlson comorbidity index (CCI) score, number of anastomosis vessels, and patient comorbidities (Supplementary Materials) including: hypertension, hyperlipidemia, diabetes mellitus (DM), congestive heart failure (CHF), peripheral vascular disease (PVD), coronary artery disease (CAD), chronic obstructive pulmonary disease (COPD), myocardial infarction (M), chronic kidney disease (CKD), end-stage renal disease (ESRD), and stroke. Blood transfusion (94001C, 94002C, 4013C, 94015C, 94003C), mechanical ventilation (57001B, 57002B, 57003B) in the preoperative one year, and CHA2DS2-VASc score [10,11] were also included. CHA2DS2-VAS was calculated for each research patient using a history of hypertension, diabetes mellitus, congestive heart failure, and vascular disease.…”

Section: Comorbidities and Variable Definitionsmentioning

confidence: 99%

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Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Huang

Chen

et al. 2021

Healthcare

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Coronary artery bypass surgery grafting (CABG) is a commonly efficient treatment for coronary artery disease patients. Even if we know the underlying disease, and advancing age is related to survival, there is no research using the one year before surgery and operation-associated factors as predicting elements. This research used different machine-learning methods to select the features and predict older adults’ survival (more than 65 years old). This nationwide population-based cohort study used the National Health Insurance Research Database (NHIRD), the largest and most complete dataset in Taiwan. We extracted the data of older patients who had received their first CABG surgery criteria between January 2008 and December 2009 (n = 3728), and we used five different machine-learning methods to select the features and predict survival rates. The results show that, without variable selection, XGBoost had the best predictive ability. Upon selecting XGBoost and adding the CHA2DS score, acute pancreatitis, and acute kidney failure for further predictive analysis, MARS had the best prediction performance, and it only needed 10 variables. This study’s advantages are that it is innovative and useful for clinical decision making, and machine learning could achieve better prediction with fewer variables. If we could predict patients’ survival risk before a CABG operation, early prevention and disease management would be possible.

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Section: Discussionsupporting

confidence: 50%

Section: Comorbidities and Variable Definitionsmentioning

confidence: 99%

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Huang

Chen

et al. 2021

Healthcare

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“…including advanced age, aortic calcification, renal failure, and diabetes have all been identified to account for the early and delayed incidence of postoperative stroke. [6][7][8] Over several decades, we have witnessed significant technical and technological advancements in the context of CABG. These include routine use of preoperative and intraoperative advanced imaging platforms, improvements in surgical technique, and optimisation of cardiopulmonary bypass systems.…”

mentioning

confidence: 99%

Incidence and predictors of postoperative ischemic stroke after coronary artery bypass grafting

Mohamed

Hirji

Mohamed

et al. 2021

Int. J. Clin. Pract.

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Although coronary artery bypass grafting (CABG) has been shown to be safe and effective in appropriately selected patients, 1,2 ischemic stroke is a potentially devastating complication that is associated with increased risk of both short-and long-term morbidity and mortality. 3,4 The most common mechanism of stroke following CABG is related to emboli originating from the ascending aorta during cross-clamping, cannulation, and creation of proximal anastomoses; however, intracardiac thrombi, hypoperfusion, and air emboli have all been implicated as possible causes. 5 Thus, in light of this complex and multifactorial pathophysiology, several risk factors

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“…The baseline characteristic variables in this study included sex (male/female), age, Charlson comorbidity index (CCI), and CHA2DS2-VAS scores [10,11]. Each patient's comorbidities could be traced to the date before the CABG surgery (2002)(2003)(2004)(2005)(2006)(2007)(2008)(2009)(2010)(2011)(2012)(2013).…”

Section: Comorbidities and Risk Factorsmentioning

confidence: 99%

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

Huang

Chen

et al. 2021

Healthcare

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Most patients face expensive healthcare management after coronary artery bypass grafting (CABG) surgery, which brings a substantial financial burden to the government. The National Health Insurance Research Database (NHIRD) is a complete database containing over 99% of individuals’ medical information in Taiwan. Our research used the latest data that selected patients who accepted their first CABG surgery between January 2014 and December 2017 (n = 12,945) to predict which factors will affect medical expenses, and built the prediction model using different machine learning algorithms. After analysis, our result showed that the surgical expenditure (X4) and 1-year medical expenditure before the CABG operation (X14), and the number of hemodialysis (X15), were the key factors affecting the 1-year medical expenses of CABG patients after discharge. Furthermore, the XGBoost and SVR methods are both the best predictive models. Thus, our research suggests enhancing the healthcare management for patients with kidney-related diseases to avoid costly complications. We provide helpful information for medical management, which may decrease health insurance burdens in the future.

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CHA2DS2-VASc score as predictor of ischemic stroke in patients undergoing coronary artery bypass grafting and percutaneous coronary intervention

Cited by 8 publications

References 32 publications

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Machine-Learning Techniques for Feature Selection and Prediction of Mortality in Elderly CABG Patients

Incidence and predictors of postoperative ischemic stroke after coronary artery bypass grafting

The Prediction Model of Medical Expenditure Appling Machine Learning Algorithm in CABG Patients

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