In Search of an Optimal Subset of ECG Features to Augment the Diagnosis of Acute Coronary Syndrome at the Emergency Department

• Published in: Journal of the American Heart Association Vol. 10; no. 3; p. e017871
• Main Authors: Bouzid, Zeineb, Faramand, Ziad, Gregg, Richard E., Frisch, Stephanie O., Martin‐Gill, Christian, Saba, Samir, Callaway, Clifton, Sejdić, Ervin, Al‐Zaiti, Salah
• Format: Journal Article
• Language: English
• Published: England John Wiley and Sons Inc 02.02.2021; Wiley
• Subjects: acute coronary syndrome; Acute Coronary Syndrome - diagnosis; Acute Coronary Syndrome - physiopathology; Algorithms; Decision Support Systems, Clinical; dimensionality reduction; ECG; Electrocardiography - methods; Emergency Service, Hospital - statistics & numerical data; Female; Follow-Up Studies; Humans; ischemia; Machine Learning; Male; Middle Aged; Original Research; Predictive Value of Tests; Prospective Studies; ECG; dimensionality reduction; acute coronary syndrome; machine learning; ischemia
• ISSN: 2047-9980; 2047-9980
• DOI: 10.1161/JAHA.120.017871

Background Classical ST-T waveform changes on standard 12-lead ECG have limited sensitivity in detecting acute coronary syndrome (ACS) in the emergency department. Numerous novel ECG features have been previously proposed to augment clinicians' decision during patient evaluation, yet their clinical utility remains unclear. Methods and Results This was an observational study of consecutive patients evaluated for suspected ACS (Cohort 1 n=745, age 59±17, 42% female, 15% ACS; Cohort 2 n=499, age 59±16, 49% female, 18% ACS). Out of 554 temporal-spatial ECG waveform features, we used domain knowledge to select a subset of 65 physiology-driven features that are mechanistically linked to myocardial ischemia and compared their performance to a subset of 229 data-driven features selected by multiple machine learning algorithms. We then used random forest to select a final subset of 73 most important ECG features that had both data- and physiology-driven basis to ACS prediction and compared their performance to clinical experts. On testing set, a regularized logistic regression classifier based on the 73 hybrid features yielded a stable model that outperformed clinical experts in predicting ACS, with 10% to 29% of cases reclassified correctly. Metrics of nondipolar electrical dispersion (ie, circumferential ischemia), ventricular activation time (ie, transmural conduction delays), QRS and T axes and angles (ie, global remodeling), and principal component analysis ratio of ECG waveforms (ie, regional heterogeneity) played an important role in the improved reclassification performance. Conclusions We identified a subset of novel ECG features predictive of ACS with a fully interpretable model highly adaptable to clinical decision support applications. Registration URL: https://www.clinicaltrials.gov; Unique Identifier: NCT04237688.