• Oscar Calvillo-Argüelles, Carlos R Sierra-Fernández, Jorge Padilla-Ibarra, Hugo Rodriguez-Zanella, Karla Balderas-Muñoz, Maria Alexandra Arias-Mendoza, Carlos Martínez-Sánchez, Sharon Selmen-Chattaj, Beatriz E Dominguez-Mendez, Pim van der Harst, and Luis Eduardo Juarez-Orozco.
• Division of Cardiology, Peter Munk Cardiac Center, Toronto General Hospital, University Health Network, University of Toronto, Toronto, ON, Canada; Department of Clinical Cardiology, National Institute of Cardiology "Ignacio Chávez,", Mexico City, Mexico.
• Chest. 2020 Oct 1; 158 (4): 1669-1679.

BackgroundOSA conveys worse clinical outcomes in patients with coronary artery disease. The STOP-BANG score is a simple tool that evaluates the risk of OSA and can be added to the large number of clinical variables and scores that are obtained during the management of patients with myocardial infarction (MI). Currently, machine learning (ML) is able to select and integrate numerous variables to optimize prediction tasks.Research QuestionCan the integration of STOP-BANG score with clinical data and scores through ML better identify patients who experienced an in-hospital cardiovascular event after acute MI?Study Design And MethodsThis is a prospective observational cohort study of 124 patients with acute MI of whom the STOP-BANG score classified 34 as low (27.4%), 30 as intermediate (24.2%), and 60 as high (48.4%) OSA-risk patients who were followed during hospitalization. ML implemented feature selection and integration across 47 variables (including STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction) to identify those patients who experienced an in-hospital cardiovascular event (ie, death, ventricular arrhythmias, atrial fibrillation, recurrent angina, reinfarction, stroke, worsening heart failure, or cardiogenic shock) after definitive MI treatment. Receiver operating characteristic curves were used to compare ML performance against STOP-BANG score, Killip class, GRACE score, and left ventricular ejection fraction, independently.ResultsThere were an increasing proportion of cardiovascular events across the low, intermediate, and high OSA risk groups (P = .005). ML selected 7 accessible variables (ie, Killip class, leukocytes, GRACE score, c reactive protein, oxygen saturation, STOP-BANG score, and N-terminal prohormone of B-type natriuretic peptide); their integration outperformed all comparators (area under the curve, 0.83 [95% CI, 0.74-0.90]; P < .01).InterpretationThe integration of the STOP-BANG score into clinical evaluation (considering Killip class, GRACE score, and simple laboratory values) of subjects who were admitted for an acute MI because of ML can significantly optimize the identification of patients who will experience an in-hospital cardiovascular event.Copyright © 2020 American College of Chest Physicians. Published by Elsevier Inc. All rights reserved.

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