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

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

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.

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

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.

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

BACKGROUND

Data on the incidence and outcomes of ischemic stroke in patients undergoing coronary artery bypass grafting (CABG) in the current era are limited. The goal of this study was to examine contemporary trends, predictors, and outcomes of ischemic stroke following CABG in a large nationally representative database over a 12-year-period.

METHODS

The National Inpatient Sample was used to identify all adult (≥18 years) patients who underwent CABG between 2004 and 2015. The incidence and predictors of post-CABG ischemic stroke were assessed and in-hospital outcomes of patients with and without post-CABG stroke were compared.

RESULTS

Out of 2 569 597 CABG operations, ischemic stroke occurred in 47 279 (1.8%) patients, with a rising incidence from 2004 (1.2%) to 2015 (2.3%) (P < .001). Patient risk profiles increased over time in both cohorts, with higher Charlson comorbidity scores observed amongst stroke patients. Stroke was independently associated with higher rates of in-hospital mortality (3-fold), longer lengths of hospital stay (~6 more days), and higher total hospitalisation cost (~$80 000 more). Age ≥60 years and female sex (OR 1.33, 95% CI 1.31-1.36) were the strongest predictors of stroke (both P < .001). Further, on-pump CABG was not an independent predictor of stroke (P = .784).

CONCLUSION

In this nationally representative study we have shown that the rates of postoperative stroke complications following CABG have increased over time to commensurate with a parallel increase in overall baseline patient risks. Given the adverse impact of stroke on in-hospital morbidity and mortality after CABG, further studies are warranted to systematically delineate factors contributing to this striking trend.

Myocardial Infarction Complicated by Ischemic Stroke: Risk Factors, Prognosis, Unresolved Problems and Possible Methods of Prevention

The present work is devoted to the analysis of modern publications on various aspects of the development and course of ischemic stroke in the presence of acute myocardial infarction. A literature search was conducted on the websites of cardiological and neurological societies, as well as on the PubMed, EMBASE, eLibrary databases using the keywords: myocardial infarction, acute coronary syndrome, stroke, acute cerebrovascular accident, myocardial infarction, acute coronary syndrome, stroke. The authors of this review found that although stroke is a relatively rare complication of myocardial infarction, its prevention is an extremely significant task, since it is associated with high mortality, disability and a significant increase in the cost of treatment. So, it is extremely important to detect thrombosis of the left ventricular cavity in a timely manner, to register preexisting atrial fibrillation that occurs earlier or for the first time, followed by the appointment of anticoagulant therapy. Timely reperfusion treatment, the use of statins and modern dual antithrombotic therapy can reduce the risk of developing cerebrovascular accident in patients with myocardial infarction. It is likely that a decrease in the activity of subclinical inflammation after myocardial infarction will also reduce the risk of stroke, as was recently shown in the COLCOT study. Currently, it remains relevant to search for new knowledge about the risk factors for stroke, which complicated the course of myocardial infarction, which will allow developing more effective and personalized preventive measures in a patient with acute coronary syndrome.