ABSTRACT
The diagnostic criteria, treatments at the time of admission, and drugs used in patients with acute coronary syndrome are well defined in countless guidelines. However, there is uncertainty about the measures to recommend during patient discharge planning. This document brings together the most recent evidence and the standardized and optimal treatment for patients at the time of discharge from hospitalization for an acute coronary syndrome, for comprehensive and safe care in the patient's transition between care from the acute event to the outpatient care, with the aim of optimizing the recovery of viable myocardium, guaranteeing the most appropriate secondary prevention, reducing the risk of a new coronary event and mortality, as well as the adequate reintegration of patients into daily life.
Los criterios diagnósticos, los tratamientos en el momento de la admisión y los fármacos utilizados en pacientes con síndrome coronario agudo están bien definidos en innumerables guías. Sin embargo, existe incertidumbre acerca de las medidas para recomendar durante la planificación del egreso de los pacientes. Este documento reúne las evidencias más recientes y el tratamiento estandarizado y óptimo para los pacientes al momento del egreso de una hospitalización por un síndrome coronario agudo, para un cuidado integral y seguro en la transición del paciente entre la atención del evento agudo y el cuidado ambulatorio, con el objetivo de optimizar la recuperación de miocardio viable, garantizar la prevención secundaria más adecuada, reducir el riesgo de un nuevo evento coronario y la mortalidad, así como la adecuada reinserción de los pacientes en la vida cotidiana.
Subject(s)
Acute Coronary Syndrome , Patient Discharge , Acute Coronary Syndrome/therapy , Acute Coronary Syndrome/diagnosis , Humans , Latin America , Practice Guidelines as TopicABSTRACT
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.
