Problems in the management of elderly patients with valvular heart disease.

Yoshida, Kiyoshi; Yoshikawa, Junichi; Shakudo, M; Jyo, Yasuko; Takao, Seiichi; Shiratori, Kenichi; Koizumi, Kazuhiro; Okumachi, F; Katô, Hiroshi; Okada, Yukikatsu; Shomura, T

doi:10.1253/jcj.52.466

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“…The high postoperative mortality rate in elderly patients undergoing cardiac valvular surgery has been of great concern to cardiothoracic surgeons. The in-hospital mortality rate in the aggregate cohort was approximately 4.05% in our research, which was significantly higher than the overall population mortality rate (2.16–2.64%) and slightly better than the 8–20% mortality rate reported in previous studies [ 13 , 15 ]. Yoshida et al found that the in-hospital mortality rate was 9.6% in patients 65 years or older, whereas in patients younger than 65 years, the rate was 3.2% [ 15 ].…”

Section: Discussioncontrasting

confidence: 74%

“…The in-hospital mortality rate in the aggregate cohort was approximately 4.05% in our research, which was significantly higher than the overall population mortality rate (2.16–2.64%) and slightly better than the 8–20% mortality rate reported in previous studies [ 13 , 15 ]. Yoshida et al found that the in-hospital mortality rate was 9.6% in patients 65 years or older, whereas in patients younger than 65 years, the rate was 3.2% [ 15 ]. The analysis by Susheel K. Kodali et al found that in-hospital mortality in patients over 80 years undergoing cardiac valvular surgery could be as high as 20%, which may be higher if multiple valves were involved or combined with CABG surgery [ 13 ].…”

Section: Discussioncontrasting

confidence: 74%

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An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning

Kun

Lin

Yang

et al. 2023

JCDD

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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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Section: Discussioncontrasting

confidence: 74%