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Patients with hypertrophic cardiomyopathy (HCM) and left ventricular outflow tract (LVOT) obstruction who remain symptomatic despite optimal medical therapy may undergo a septal myectomy (Morrow procedure). This procedure involves excising a portion of the hypertrophied septum below the aortic valve using a heart-lung machine to improve blood flow through the LVOT. Effective preoperative and postoperative nursing management ensures successful patient outcomes, minimizes complications, and...
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An In-Hospital Mortality Risk Model for Elderly Patients Undergoing Cardiac Valvular Surgery Based on LASSO-Logistic

Kun Zhu1, Hongyuan Lin1, Xichun Yang2

  • 1Cardiac Surgery Centre, Fuwai Hospital, Chinese Academy of Medical Sciences and Peking Union Medical College, Beijing 100037, China.

Journal of Cardiovascular Development and Disease
|February 24, 2023
PubMed
Summary
This summary is machine-generated.

New models using LASSO-logistic regression and machine learning accurately predict in-hospital mortality risk in elderly patients undergoing cardiac valvular surgery, outperforming existing scores.

Keywords:
LASSO-logistic regressionmachine learningmortality riskprediction modelsvalvular heart disease

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Area of Science:

  • Cardiology
  • Medical Informatics
  • Geriatric Medicine

Background:

  • Elderly patients undergoing cardiac valvular surgery face a significant risk of in-hospital mortality.
  • Developing accurate risk prediction models is crucial for improving patient outcomes.

Purpose of the Study:

  • To develop and validate a novel prediction model for in-hospital mortality in elderly patients undergoing cardiac valvular surgery.
  • To compare the performance of machine learning (ML) and least absolute shrinkage and selection operator (LASSO)-logistic regression models against the EuroSCORE II.

Main Methods:

  • Utilized clinical and peri-operative data from 7163 elderly patients from the Chinese Cardiac Surgery Registry (CCSR).
  • Developed prediction models using LASSO-logistic regression and ML algorithms, including linear discriminant analysis (LDA) and support vector classification (SVC).
  • Compared model discrimination and calibration with EuroSCORE II using training and testing cohorts.

Main Results:

  • The study included 7163 elderly patients (mean age 69.8 years, 45.0% women) with an in-hospital mortality rate of 4.05%.
  • Seven key risk factors were identified: age, prior cardiac surgery, cardiopulmonary bypass duration (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, LDA, SVC, and logistic regression (LR) models demonstrated superior discrimination and calibration compared to EuroSCORE II.

Conclusions:

  • The identified risk factors effectively predict in-hospital mortality in this elderly patient cohort.
  • LASSO-logistic regression, LDA, SVC, and LR models offer robust tools for predicting in-hospital mortality risk in elderly patients undergoing cardiac valvular surgery.