Laboratory of Data for Quality of Care and Outcomes Research (LaDa:QCOR), Catholic University of Brasília, Taguatinga Sul, Brasília, DF, 71966-700, Brazil.
Aramari Apo Institute for Education and Clinical Research, Brasília, DF, Brazil.
Sci Rep. 2023 Jan 19;13(1):1021. doi: 10.1038/s41598-023-27776-0.
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, 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. Among individuals who have experienced ACS (n = 6341 and n = 2242), the predictive accuracy for adverse clinical events 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 69% by using a rule designed for the global cohort.
急性冠状动脉综合征（ACS）是 55 岁以上人群死亡的常见原因。尽管年龄较小的人群较少出现 ACS，但这种临床事件的发病率呈上升趋势，复发率高，并引发了相当大的经济负担。与老年患者相比，年轻的 ACS 患者（yACS）通常代表性不足，且具有独特的流行病学特征。这些差异可能解释了为什么现有的风险预测模型通常会对 yACS 进行惩罚，导致其假阳性率高于老年患者。我们假设探索预测时间、观察窗口和亚组特异性预测等时间框架结构，可以改善时间依赖性预测指标。在经历过 ACS 的个体中（n=6341 和 n=2242），通过使用针对 yACS 的特定规则和划分短期和长期预测窗口，对不良临床事件的预测准确性进行了优化，与使用针对全队列设计的规则相比，可检测到 80%的事件，而不是 69%。
