CO187 The Framing of Time-Dependent Machine Learning Models Improves Risk Estimation Among Young Individuals with Acute Coronary Syndromes

Abstract

Although young individuals (<55 years) are less frequently seen with acute coronary syndromes (ACS), this clinical event shows high recurrence rates and triggers considerable economic burden. Young individuals with ACS (yACS) are usually underrepresented and show idiosyncratic epidemiologic features compared to older subjects. These differences may justify why available risk prediction models usually penalize yACS with higher false positive rates compared to older subjects. We hypothesized that exploring temporal framing structures such as prediction time, observation windows and subgroup-specific prediction, could improve time-dependent prediction metrics.