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An In-hospital Mortality Risk Model for Patients Undergoing Coronary Artery Bypass Grafting in China
Zhan Hu1, Sipeng Chen1, Junzhe Du1
1Department of Cardiovascular Surgery, Fuwai Hospital, National Center for Cardiovascular Diseases, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing, People's Republic of China.
Insights
A new risk model predicts in-hospital mortality for coronary artery bypass grafting (CABG) in China. This tool aids surgeons in identifying high-risk patients for improved outcomes.
Area of Science:
- Cardiovascular Surgery
- Health Services Research
- Biostatistics
Background:
- Increasing surgical volumes and evolving patient risk profiles in China necessitate updated risk assessment for coronary artery bypass grafting (CABG).
- Existing risk models may not accurately reflect current patient demographics and outcomes in the Chinese population.
Purpose of the Study:
- To develop and validate a novel risk prediction model for in-hospital mortality following CABG in China.
- To identify key demographic and clinical factors associated with CABG mortality.
- To compare the performance of the new model against established risk scores like EuroSCORE II and SinoSCORE.
Main Methods:
- Analysis of 56,776 patients undergoing CABG from January 2013 to December 2016 across 87 Chinese hospitals.
- Random division of patients (2013-2015) into training (75%) and testing (25%) sets, with 2016 data (n=15,047) used for external validation.
- Model discrimination assessed using the Harrell C statistic and calibration evaluated with the Hosmer-Lemeshow goodness-of-fit test.
Main Results:
- The final model incorporated 16 unique risk variables to predict in-hospital mortality (overall 2.1%).
- The model demonstrated good discrimination (C-statistic: 0.79 training, 0.79 test, 0.78 validation) and calibration (P > .05) across all samples.
- The developed model outperformed both EuroSCORE II and SinoSCORE in discrimination and calibration.
Conclusions:
- A new 16-factor risk model accurately predicts in-hospital mortality after CABG in China.
- This updated model offers improved risk stratification capabilities for surgeons and healthcare providers.
- The model's superior performance suggests its utility in optimizing patient selection and resource allocation for CABG procedures in China.
Background:
To meet the demand of increasing surgical volume and changing of patient's risk profiles of coronary artery bypass grafting in China, we developed a new risk model that predicts in-hospital mortality.
Methods:
The analysis included patients who underwent coronary artery bypass grafting between January 2013 and December 2016 at 87 hospitals in the Chinese Cardiac Surgery Registry. Patients in years 2013 to 2015 were randomly divided into training (n = 31,297 [75%]) and test (n = 10,432 [25%]) samples; 2016 patients (n = 15047) comprised the validation sample. Demographic and clinical risk factors were identified. The Harrell C statistic was used to evaluate model discrimination, and the Hosmer-Lemeshow goodness-of-fit test was used to assess calibration.
Results:
The 56,776 patients were a mean age of 61.8 (SD, 8.8) years, and 24.6% were women. Overall, in-hospital mortality was 2.1%. The final model included 21 risk factors represented by 16 unique variables. The model achieved good discrimination, with a C statistic of 0.79 (95% confidence interval [CI], 0.77-0.80) in the training sample, 0.79 (95% CI, 0.76-0.82) in the test sample, and 0.78 (95% CI, 0.76-0.81) in the validation sample. Model calibration was good according to the Hosmer-Lemeshow test (P > .05 in the 3 samples). Compared with the European System for Cardiac Operative Risk Evaluation 2011 revision (EuroSCORE II) and the Sino(Chinese) System for Coronary artery bypass grafting Operative Risk Evaluation (SinoSCORE), the model had better discrimination and calibration.
Conclusions:
We developed and evaluated a model with 16 risk factors that predicted in-hospital mortality risk after coronary artery bypass grafting in China. This updated model may help surgeons and hospitals better identify high-risk patient.
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