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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

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. Method...

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Autores principales: Zhu, Kun, Lin, Hongyuan, Yang, Xichun, Gong, Jiamiao, An, Kang, Zheng, Zhe, Hou, Jianfeng
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963974/
https://www.ncbi.nlm.nih.gov/pubmed/36826583
http://dx.doi.org/10.3390/jcdd10020087
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author Zhu, Kun
Lin, Hongyuan
Yang, Xichun
Gong, Jiamiao
An, Kang
Zheng, Zhe
Hou, Jianfeng
author_facet Zhu, Kun
Lin, Hongyuan
Yang, Xichun
Gong, Jiamiao
An, Kang
Zheng, Zhe
Hou, Jianfeng
author_sort Zhu, Kun
collection PubMed
description 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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spelling pubmed-99639742023-02-26 An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning Zhu, Kun Lin, Hongyuan Yang, Xichun Gong, Jiamiao An, Kang Zheng, Zhe Hou, Jianfeng J Cardiovasc Dev Dis Article 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. MDPI 2023-02-17 /pmc/articles/PMC9963974/ /pubmed/36826583 http://dx.doi.org/10.3390/jcdd10020087 Text en © 2023 by the authors. https://creativecommons.org/licenses/by/4.0/Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
spellingShingle Article
Zhu, Kun
Lin, Hongyuan
Yang, Xichun
Gong, Jiamiao
An, Kang
Zheng, Zhe
Hou, Jianfeng
An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning
title An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning
title_full An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning
title_fullStr An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning
title_full_unstemmed An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning
title_short An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic Regression and Machine Learning
title_sort in-hospital mortality risk model for elderly patients undergoing cardiac valvular surgery based on lasso-logistic regression and machine learning
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9963974/
https://www.ncbi.nlm.nih.gov/pubmed/36826583
http://dx.doi.org/10.3390/jcdd10020087
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