scholarly journals DEVELOPMENT OF A PROVINCIAL RISK PREDICTION MODEL FOR ISOLATED CORONARY ARTERY BYPASS GRAFTING

2018 ◽  
Vol 34 (10) ◽  
pp. S10-S11
Author(s):  
L. Stevens ◽  
F. Dagenais ◽  
L. Perrault ◽  
H. Jeanmart ◽  
G. David ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Prediction Model ◽  
Coronary Artery Bypass ◽  
Risk Prediction ◽  
Artery Bypass ◽  
Risk Prediction Model ◽  
Bypass Grafting ◽  
Artery Bypass Grafting

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.

Development and validation of a prediction model for strokes after coronary artery bypass grafting

2003 ◽  
Vol 76 (2) ◽  
pp. 436-443 ◽  
Author(s):  
David C Charlesworth ◽  
Donald S Likosky ◽  
Charles A.S Marrin ◽  
Christopher T Maloney ◽  
Hebe B Quinton ◽  
...  
Keyword(s):  
Coronary Artery ◽  
Coronary Artery Bypass Grafting ◽  
Prediction Model ◽  
Coronary Artery Bypass ◽  
Artery Bypass ◽  
Bypass Grafting ◽  
Artery Bypass Grafting ◽  
Development And Validation
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