Additional file 2 of A systematic comparison of short-term and long-term mortality prediction in acute myocardial infarction using machine learning models
At the emergency department, it is important to quickly and accurately identify patients at risk of acute myocardial infarction (AMI). One of the main tools for detecting AMI is the electrocardiogram
Background To assess the relationship between glycemic variability, glucose fluctuation trajectory and the risk of in-hospital mortality in patients with acute myocardial infarction (AMI). Methods Thi
BackgroundRecurrence can worsen conditions and increase mortality in ICH patients. Predicting the recurrence risk and preventing or treating these patients is a rational strategy to improve outcomes p
Baseline characteristics of the study population with myocardial infarction (MI) stratified by survival status of MI and 28-day case fatality, Copenhagen General Population Study 2003–2014.
All determinations were performed in the fasting state.ACEI: Angiotensin-converting enzyme inhibitor; ARB: Angiotensin II receptor antagonists; HDLc: high density lipoprotein cholesterol; LDLc: low de