scholarly journals A Risk Prediction Model of Readmission for Chinese Patients after Coronary Artery Bypass Grafting

2021 ◽  
Vol 24 (3) ◽  
pp. E479-E483
Author(s):  
Guozhen Liu ◽  
Yinghong Zhang ◽  
Wen Zhang ◽  
Yanhong Hu ◽  
Tiao Lv ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Prediction Model ◽  
Risk Prediction ◽  
Artery Bypass ◽  
Risk Prediction Model ◽  
Chinese Patients ◽  
Bypass Grafting ◽  
Lemeshow Test

Background: Predictive models can be used to assess the risk of readmission for patients after coronary artery bypass grafting (CABG). However, the majority of the existing prediction models have been developed based on data of western population. Our objective was to develop and validate a risk prediction model for Chinese patients after CABG. Methods: This study was conducted among 1983 patients who underwent CABG in Wuhan Asian Heart Hospital from January 2017 to October 2019. Pearson's chi-squared and multivariate logistic regression were performed to investigate the risk factors of readmission after CABG. The area under the ROC curve and Hosmer-Lemeshow test were used to validate the discrimination and calibration of the model, respectively. Results: Six risk factors were predictive of readmission: age≥65 years (odds ratio [OR] = 2.19; 95% confidence interval [CI]: 1.11-4.34; P = 0.024),  female (OR = 2.46; 95%CI: 1.26-4.80; P = 0.008), private insurance (OR = 4.23; 95%CI: 1.11-16.11; P = 0.034), diabetes (OR = 2.351; 95%CI: 1.20-4.59; P = 0.012), hypertension (OR = 2.33; 95%CI: 1.16-4.66; P = 0.017), and congenital heart disease (OR = 6.93;95%CI: 2.04-23.52; P = 0.002). The area under the curve c-statistic was 0.876 in the derivation sample and 0.865 in the validation sample. Hosmer-Lemeshow test: P=0.561. Conclusion: The risk prediction model in our study can be used to predict the risk of readmission in Chinese patients after CABG.

A Risk Prediction Model of Readmission after coronary artery bypass grafting (CABG) in China

2020 ◽  
Author(s):  
Guozhen Liu ◽  
Yinghong Zhang ◽  
Wen Zhang ◽  
Liu Hu ◽  
Tiao Lv ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Prediction Model ◽  
Risk Prediction ◽  
Artery Bypass ◽  
Risk Prediction Model ◽  
Bypass Grafting ◽  
Modeling Group ◽  
Independent Risk Factors

Abstract Background At present, there is no risk prediction model suitable for the Chinese population after coronary artery bypass grafting (CABG), this study aims to analyze the risk factors related to readmission after CABG and to construct a risk prediction model of readmission for patients with CABG in China. Methods A total of 1983 patients who had undergone CABG at Wuhan Asian Heart Hospital from 2017 to 2019 were selected to collect general patient information. Univariate analysis was performed on the data of 825 patients in the modeling group to determine potential risk factors, and then independent risk factors of readmission after CABG were determined by multivariate logistic regression. Hosmer-Lemeshow （H-L） test, calibration curve and the area under the receiver operating characteristic （ROC） curve are used to test the calibration and discrimination of the model. Results Six preoperative variables （age≥65, female, Private insurance, diabetes, hypertension level2,3, congenital heart disease）were independent risk factors of readmission after CABG. Our risk prediction model has high application value （the area under the ROC curve of the modeling group is 0.876, and of the validation group is 0.865, H-L test: P=0.561〉0.05）. Conclusion The risk prediction model in our study can be used to predict the risk of readmission in CABG patients in clinical work, providing a basis for more effective perioperative treatment and care to prevent patients from being readmitted to hospital.

Mortality Risk Factors in Chronic Renal Failure Patients after Coronary Artery Bypass Grafting

Renal Failure ◽  
2007 ◽  
Vol 29 (7) ◽  
pp. 823-828 ◽  
Author(s):  
Beril Akman ◽  
Ayse Bilgic ◽  
Gulsah Sasak ◽  
Siren Sezer ◽  
Atilla Sezgin ◽  
...  
Keyword(s):  
Risk Factors ◽  
Renal Failure ◽  
Coronary Artery ◽  
Chronic Renal Failure ◽  
Coronary Artery Bypass Grafting ◽  
Coronary Artery Bypass ◽  
Mortality Risk ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Artery Bypass Grafting

scholarly journals Risk factors for the return of mitral regurgitation after coronary artery bypass grafting and mitral valve reconstruction in patients with ischemic cardiomyopathy

2021 ◽  
Author(s):  
Iuliia Kareva ◽  
Vidadiue Efendiev ◽  
Alexey Nesmachnyy ◽  
Sardor Rakhmonov ◽  
Alexander Chernyavskiy ◽  
...  
Keyword(s):  
Risk Factors ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Mitral Valve ◽  
Mitral Regurgitation ◽  
Coronary Artery Bypass ◽  
Artery Bypass ◽  
Mitral Insufficiency ◽  
Bypass Grafting ◽  
Artery Bypass Grafting

Background and Aim: We aimed to identify risk factors for recurrent mitral regurgitation in two surgical treatment groups: isolated coronary artery bypass grafting (CABG) and CABG combined with mitral valve (MV) repair in patients with moderate ischemic mitral regurgitation (IMR). Methods: A single-centre, prospective, randomised study, which included 76 patients with ICM and moderate mitral regurgitation (MR). Study included two groups: isolated CABG and CABG with MV repair (MVR). Isolated annuloplasty was used to correct mitral insufficiency in the CABG + MVR group. Results: Isolated CABG or CABG combined with MVR in patients with ICM does not lead to a statistically significant decreasing of MR in the long-term period compared to baseline values. However, in one year after surgery, the degree of MR after combined surgery is lower than the initial values. The identification of predictors of the progression of IMR in ICM made it possible to determine the threshold values for the effectiveness of MVR, and the assessment of echocardiographic predictors for annuloplasty helps to choose the right surgical tactic of patients. Conclusions: Coronary revascularization with surgical of IMR in patients with ICM does not increase the number of complications in the early postoperative period compared to the group of isolated CABG. In patients with ICM and moderate MR after isolated CABG, the progression of MR (MR of the 3rd degree, initially 0%, after 12 months 31%, after 36 months 71%; p <0.001) was observed even with an initially moderate expansion of the fibrous ring of the MV.

scholarly journals Predictive potential assessment of preoperative risk factors for atrial fibrillation in patients with coronary artery disease after coronary artery bypass grafting

2020 ◽  
Vol 35 (4) ◽  
pp. 128-136
Author(s):  
K. I. Shakhgeldyan ◽  
V. Y. Rublev ◽  
B. I. Geltser ◽  
B. O. Shcheglov ◽  
V. G. Shirobokov ◽  
...  
Keyword(s):  
Risk Factors ◽  
Atrial Fibrillation ◽  
Coronary Artery Disease ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Preoperative Risk ◽  
Preoperative Risk Factors ◽  
Artery Disease

Introduction. Postoperative atrial fibrillation (POAF) is one of the most common complications of coronary artery bypass grafting (CABG) and occurs in 25–65% of patients.Aim. The study aimed to assess the predictive potential of preoperative risk factors for POAF in patients with coronary artery disease (CAD) after CABG based on machine learning (ML) methods.Material and Methods. An observational retrospective study was carried out based on data from 866 electronic case histories of CAD patients with a median age of 63 years and a 95% confidence interval [63; 64], who underwent isolated CABG on cardiopulmonary bypass. Patients were assigned to two groups: group 1 comprised 147 (18%) patients with newly registered atrial fibrillation (AF) paroxysms; group 2 included 648 (81.3%) patients without cardiac arrhythmia. The preoperative clinical and functional status was assessed using 100 factors. We used statistical analysis methods (Chi-square, Fisher, Mann – Whitney, and univariate logistic regression (LR) tests) and ML tests (multivariate LR and stochastic gradient boosting (SGB)) for data processing and analysis. The models’ accuracy was assessed by three quality metrics: area under the ROC-curve (AUC), sensitivity, and specificity. The cross-validation procedure was performed at least 1000 times on randomly selected data.Results. The processing and analysis of preoperative patient status indicators using ML methods allowed to identify 10 predictors that were linearly and nonlinearly related to the development of POAF. The most significant predictors were the anteroposterior dimension of the left atrium, tricuspid valve insufficiency, ejection fraction <40%, duration of the P–R interval, and chronic heart failure of functional class III–IV. The accuracy of the best predictive multifactorial model of LR was 0.61 in AUC, 0.49 in specificity, and 0.72 in sensitivity. The values of similar quality metrics for the best model based on SGB were 0.64, 0.6, and 0.68, respectively.Conclusion. The use of SGB made it possible to verify the nonlinearly related predictors of POAF. The prospects for further research on this problem require the use of modern medical care methods that allow taking into account the individual characteristics of patients when developing predictive models.

Risk Factors for Late Stroke After Coronary Artery Bypass Grafting

2005 ◽  
Vol 14 (12) ◽  
pp. 48-49
Author(s):  
T. Schachner ◽  
A. Zimmer ◽  
G. Nagele ◽  
G. Laufer ◽  
J. Bonatti
Keyword(s):  
Risk Factors ◽  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Coronary Artery Bypass ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Artery Bypass Grafting
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