Integrating the STOP-BANG Score and Clinical Data to Predict Cardiovascular Events After Infarction: A Machine Learning Study.

BACKGROUND: OSA 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 QUESTION: Can 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 METHODS: This 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. RESULTS: There 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). INTERPRETATION: The 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.

[Left main coronary aneurysm and antiphospholipid syndrome: survival at 12 years. A case report and literature review]. / Aneurisma de tronco de coronaria izquierda y síndrome antifosfolípido: sobrevida a 12 años. Informe de un caso y revisión de la literatura.

Coronary artery aneurysms are a relatively infrequent finding with an incidence of 1% to 2% per year. Its cause can be atherosclerosis, congenital or due to other causes less common. Its initial manifestation can be myocardial infarction and sudden death as a result of rupture or distal embolization. The large coronary aneurysms, non-atherosclerotic, located in the common part of the left main coronary artery are exceptional. The diagnostic method of choice is the coronary angiography; however, non-invasive techniques such as transthoracic including tridimensional mode and transesophageal echocardiography, magnetic resonance imaging and computed tomography may have an important role in the detection and follow-up of these anomalies. The natural history of coronary aneurysm is not quite known. We present the case of a patient of 44 years, following an acute coronary event was diagnosed with an aneurysm in the left main and antiphospholipid syndrome. The patient received conservative treatment on the basis of antiplatelet and anticoagulant without presenting major cardiovascular events or other complications in 12 years of follow-up.