Machine Learning-Based Predictive Model of Aortic Valve Replacement Modality Selection in Severe Aortic Stenosis Patients.

The current recommendation for bioprosthetic valve replacement in severe aortic stenosis (AS) is either surgical aortic valve replacement (SAVR) or transcatheter aortic valve replacement (TAVR). We evaluated the performance of a machine learning-based predictive model using existing periprocedural variables for valve replacement modality selection. We analyzed 415 patients in a retrospective longitudinal cohort of adult patients undergoing aortic valve replacement for aortic stenosis.

Machine learning-based predictive risk models for 30-day and 1-year mortality in severe aortic stenosis patients undergoing transcatheter aortic valve implantation.

Background: Predictive risk score for mortality plays an important role in the decision-making in patient selection and risk stratification for TAVI. Existing established predictive risk scores had poor discrimination performance in the prediction of mortality after the TAVI.

Objectives: The present study aimed to develop machine learning-based predictive models for 30-day and 1-year mortality in severe aortic stenosis patients undergoing TAVI.

Clinical Characteristics and In-Hospital Mortality in Patients with STEMI during the COVID-19 Outbreak in Thailand.

Background: Nowadays, current evidence on the effects of the COVID-19 outbreak on ST-elevation myocardial infarction (STEMI) patients is discrepant. The aim of this study was to compare and identify any changes in STEMI patients between the pre-COVID-19 period and during the COVID-19 outbreak. Methods: We conducted a retrospective cohort study to evaluate consecutive STEMI patients admitted from 1 September 2018 to 30 September 2021.