The relationships between acute coronary syndromes (ACS) adverse events and the associated risk factors are typically complicated and nonlinear, which poses significant challenges to clinicians' attempts at risk stratification. Here, we aim to explore the implementation of modern risk stratification tools to untangle how these complex factors shape the risk of adverse events in patients with ACS. We used an interpretable multi-algorithm machine learning (ML) approach and clinical features to fit predictive models to 1,976 patients with ACS in Kuwait. We demonstrated that random forest (RF) and extreme gradient boosting (XGB) algorithms, remarkably outperform traditional logistic regression model (AUCs = 0.84 & 0.79 for RF and XGB, respectively). Our in-hospital adverse events model identified left ventricular ejection fraction as the most important predictor with the highest interaction strength with other factors. However, using the 30-days adverse events model, we found that performing an urgent coronary artery bypass graft was the most important predictor, with creatinine levels having the strongest overall interaction with other related factors. Our ML models not only untangled the non-linear relationships that shape the clinical epidemiology of ACS adverse events but also elucidated their risk in individual patients based on their unique features.
Primary percutaneous coronary intervention (PCI) is the preferred reperfusion method in patients with ST-segment elevation myocardial infarction (STEMI). In patients with STEMI who cannot undergo timely primary PCI, pharmacoinvasive treatment is recommended, comprising immediate fibrinolytic therapy with subsequent coronary angiography and rescue PCI if needed. Improving clinical outcomes following fibrinolysis remains of great importance for the many patients globally for whom rapid treatment with primary PCI is not possible. For patients with acute coronary syndrome who underwent primary PCI, the PLATO trial demonstrated superior efficacy of ticagrelor relative to clopidogrel. Results in the predefined subgroup of patients with STEMI were consistent with the overall PLATO trial. Patients who received fibrinolytic therapy in the 24 hours before randomization were excluded from PLATO, and there is thus a lack of data on the safety of using ticagrelor in conjunction with fibrinolytic therapy in the first 24 hours after STEMI. The TREAT study addresses this knowledge gap; patients with STEMI who had symptom onset within the previous 24 hours and had received fibrinolytic therapy (of whom 89.4% had also received clopidogrel) were randomized to treatment with ticagrelor or clopidogrel (median time between fibrinolysis and randomization: 11.5 hours). At 30 days, ticagrelor was found to be non-inferior to clopidogrel for the primary safety outcome of Thrombolysis in Myocardial Infarction (TIMI)-defined first major bleeding. Considering together the results of the PLATO and TREAT studies, initiating or switching to treatment with ticagrelor within the first 24 hours after STEMI in patients receiving fibrinolysis is reasonable.