Predicting ECGs masquerading AS STEMI with machine learning

Abstract Background Advancements in the management of ST elevation myocardial infarction (STEMI) in the last 15 years include early STEMI identification in the prehospital setting leading to expedited cardiac catheterisation laboratory (CCL) activation or fibrinolytic therapy. We have previously reported of patients who met the Glasgow algorithm (GA) ECG criteria for STEMI; 50% did not actually have an acute STEMI according to the 4th universal definition of MI (4UDMI)1. Identifying such patients could avoid unnecessary emergency CCL activations or inappropriate fibrinolytic administration. Methods From June 2010 to October 2021, we identified 2992 patients with ECG transmissions to Liverpool Hospital, Sydney Australia which met GA criteria for STEMI. These were adjudicated according to the 4UDMI for true-positive STEMI. We used univariate and multivariate logistic regression to identify likely patient characteristics that were predictive of true-positive STEMI. We then improved model performance using receiver-operator curve (ROC) with simple random forest machine learning algorithms. Results Among 2994 patients with prehospital ECG transmissions that met GA-criteria, 1553 (51%) were adjudicated as true-positive STEMI, which is comparable to previous findings1. All standard modifiable and non-modifiable risk factors were independently predictive of true-positive STEMI; however, in multivariate logistic regression gender, hypertension and diabetes lost significance. Dyslipidemia and current smoking were positively predictive of true-positive STEMI status (OR = 1.64 and 1.32 respectively), whereas age and previous MI were negatively associated (OR = 0.98 (per year increment) and 0.4). Using these identified predictors in a random forest to predict true-positive STEMI yielded an AUC of 0.65 (Figure 1). Conclusion Specific clarification of dyslipidaemia and current smoking status at first medical contact may be the most salient in predicting a true-positive STEMI during prehospital assessment which may assist in avoiding unnecessary emergency CCL activation or inappropriate fibrinolytic administration.ROC of ST elevation STEMI recognition