Key result
LASSO and machine learning models outperform EuroSCORE II in predicting in-hospital mortality.
Why the study?
A new prediction model was needed to preferably evaluate and predict in-hospital mortality risk in elderly patients receiving cardiac valvular surgery.
Do LASSO-logistic regression and machine learning models better predict in-hospital mortality compared to EuroSCORE II in elderly patients undergoing cardiac valvular surgery?
Observational (n=7,163)
Yes
Do LASSO-logistic regression and machine learning models better predict in-hospital mortality compared to EuroSCORE II in elderly patients undergoing cardiac valvular surgery?
Machine learning models, including LASSO-logistic regression, LDA, SVC, and LR, provide superior prediction of in-hospital mortality compared to EuroSCORE II in elderly patients undergoing cardiac valvular surgery.
May enhance preoperative risk stratification in elderly valvular surgery; leaves open external validation before practice change.
BACKGROUND: To preferably evaluate and predict the risk for in-hospital mortality in elderly patients receiving cardiac valvular surgery, we developed a new prediction model using least absolute shrinkage and selection operator (LASSO)-logistic regression and machine learning (ML) algorithms. METHODS: Clinical data including baseline characteristics and peri-operative data of 7163 elderly patients undergoing cardiac valvular surgery from January 2016 to December 2018 were collected at 87 hospitals in the Chinese Cardiac Surgery Registry (CCSR). Patients were divided into training (N = 5774 [80%]) and testing samples (N = 1389 [20%]) according to their date of operation. LASSO-logistic regression models and ML models were used to analyze risk factors and develop the prediction model. We compared the discrimination and calibration of each model and EuroSCORE II. RESULTS: A total of 7163 patients were included in this study, with a mean age of 69.8 (SD 4.5) years, and 45.0% were women. Overall, in-hospital mortality was 4.05%. The final model included seven risk factors: age, prior cardiac surgery, cardiopulmonary bypass duration time (CPB time), left ventricular ejection fraction (LVEF), creatinine clearance rate (CCr), combined coronary artery bypass grafting (CABG) and New York Heart Association (NYHA) class. LASSO-logistic regression, linear discriminant analysis (LDA), support vector classification (SVC) and logistic regression (LR) models had the best discrimination and calibration in both training and testing cohorts, which were superior to the EuroSCORE II. CONCLUSIONS: The mortality rate for elderly patients undergoing cardiac valvular surgery was relatively high. LASSO-logistic regression, LDA, SVC and LR can predict the risk for in-hospital mortality in elderly patients receiving cardiac valvular surgery well.
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Zhu et al. (2023) conducted an observational in Elderly patients undergoing cardiac valvular surgery (n=7,163). LASSO-logistic regression and machine learning models vs. EuroSCORE II was evaluated on In-hospital mortality. LASSO-logistic regression and machine learning models demonstrated superior discrimination and calibration compared to EuroSCORE II for predicting in-hospital mortality (overall rate 4.05%).
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