The Framing of time-dependent machine learning risk prediction models among young individuals with acute coronary syndromes

Abstract

Acute coronary syndrome (ACS) is a common cause of death in individuals older than 55 years. Although younger individuals are less frequently seen with ACS, this clinical event has increasing incidence trends 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 window shifts, coupled with subgroup-specific prediction, could improve time-dependent prediction metrics. In a large cohort of ACS individuals (nglobal_cohort=6341 and nyACS=2242), the predictive accuracy for adverse clinical events in ACS was optimized by using specific rules for yACS and splitting short-term and long-term prediction windows, leading to the detection of 80% of events, compared to 68% by using a rule designed for the global cohort.