Evaluación del algoritmo de la Sociedad Europea de Cardiología de dolor torácico en pacientes con diabetes mellitus / Performance of the European Society of Cardiology Algorithm for the Assessment of Chest Pain in Patients with Diabetes Mellitus

RESUMEN Introducción: Dado que los pacientes con diabetes tienen habitualmente niveles de troponina más elevados que la población general, nos propusimos evaluar el comportamiento del algoritmo de la Sociedad Europea de Cardiología que utiliza la medición de troponina de alta sensibilidad al ingreso y 1 hora después en estos pacientes. Material y métodos: Se evaluaron 1140 pacientes que consultaron por dolor torácico con electrocardiograma sin supradesnivel del segmento ST. El algoritmo estratifica los pacientes en tres grupos de riesgo: "externar", "observar" e "internar". Se valoró el comportamiento del algoritmo para el evento infarto a 30 d. Resultados: En total, 124 pacientes (10,8%) tenían diabetes. Ninguno de los clasificados como "externar" (40,3%) presentó infarto a 30 días. En los "internar" (23,4%), el evento se produjo en el 82,8%, mientras que en el grupo "observar" (36,3%), en el 6,8%. La sensibilidad y el valor predictivo negativo fueron similares entre pacientes con diabetes y sin esta (100% vs. 98,5% p = 0,865 y 100% vs. 99,8% p = 0,44), pero la proporción de pacientes para "externar" fue menor en diabéticos (40,3% vs. 72,1%, p <0,001). En cuanto a la precisión para "internar" pacientes, la especificidad fue menor en diabéticos, pero el valor predictivo positivo fue mayor (90,9% vs. 97,2%, p <0,001 y 83% vs. 76%, p <0,001). La proporción de pacientes para "internar" fue mayor en diabéticos (23% vs. 8,6%, p <0,001). Conclusiones: El uso del algoritmo en pacientes con diabetes mostró una alta sensibilidad y un alto valor predictivo negativo para "externar" comparable a la población general. En cuanto al grupo "internar", presentó menor especificidad, pero alto valor predictivo positivo. Esto lo transforma en una útil herramienta para la práctica diaria.

ABSTRACT Background: Patients with diabetes usually have higher troponin levels than the general population. Objective: The aim of our study was to evaluate the performance of the European Society of Cardiology algorithm which uses high sensitivity cardiac troponin levels on admission and after 1 hour in these patients. Methods: A total of 1,140 patients with chest pain and ECG without ST-segment elevation were evaluated. The algorithm stratifies patients in three risk groups: rule-out, observe and rule-in. We evaluated the performance of the algorithm to predict myocardial infarction at 30 days. Results: A total of 124 patients (10.8%) had diabetes. None of the patients in the rule-out group (40.3%) presented myocardial infarction at 30 days. In the rule-in group (23.4%), the event occurred in 82.8% of cases and in 6.8% in the observe group (36.3%). Sensitivity and negative predictive value were similar in patients with and without diabetes (100% vs. 98.5%, p= 0.865 and 100% vs. 99.8%, p=0.44), but the proportion of patients in the rule-out group was lower in diabetics (40.3% vs. 72.1%, p<0.001). The accuracy of the algorithm to rule in patients was evaluated by its specificity which was lower in diabetics, but the positive predictive value was greater (90.9% vs. 97.2%, p<0.001 and 83% vs. 76%, p<0.001). The proportion of patients in the rule-in group was higher in diabetics (23% vs. 8.6%, p<0.001). Conclusion: The use of the algorithm in patients with diabetes revealed high sensitivity and negative predictive value to rule out, which was similar to that of the general population. Regarding the rule-in group, it had lower specificity but high positive predictive value. This performance makes the algorithm a useful tool for daily practice.

Predicción de riesgo de sufrir un síndrome coronario agudo mediante un algoritmo de Machine Learning (ANGINA) / A Machine Learning Algorithm to Predict Risk for Acute Coronary Syndrome

RESUMEN Introducción: Las consultas por dolor torácico son frecuentes en los servicios de emergencias médicas (SEM). Aún no se ha identificado una estrategia diagnóstica que utilice tanto los datos objetivos como los subjetivos del dolor. Objetivos: Evaluar un clasificador de machine learning para predecir el riesgo de presentar un síndrome coronario agudo (SCA) sin elevación del segmento ST, en pacientes que consultan a un SEM con dolor torácico. Material y métodos: Se analizaron 161 pacientes que consultaron al SEM con dolor torácico. Se registró mediante un clasificador de machine learning las variables objetivas y subjetivas de caracterización del dolor. Resultados: La edad promedio fue de 57 mas/menos 12, 72,7% masculinos eran de sexo masculino y 17,4 % presentaban evento coronario previo. El 57,8% presentaba un síndrome coronario agudo con una incidencia de IAM de 29,8%, de los cuales requirieron revascularización por ATC el 35%, y CRM el 9,9% en el período de seguimiento a 30 días. Como modelo de clasificación se utilizó un Random Forest Classifier que presentó un área bajo la curva ROC de 0,8991, sensibilidad de 0,8552, especificidad de 0,8588 y una precisión de 0,8441. Las variables predictoras más influyentes fueron peso (p = 0,002), edad (p = 5,011e-07), intensidad del dolor (p = 3,0679e-05), tensión arterial sistólica (p = 0,6068) y características subjetivas del dolor (p = 1,590e-04). Conclusiones: Los clasificadores de machine learning son una herramienta útil a fin de predecir el riesgo de sufrir un síndrome coronario agudo a 30 días de seguimiento.

ABSTRACT Background: Consultations for chest pain are common in emergency medical services (EMS). A diagnostic strategy using both objective and subjective pain has not been identified yet. Objective: To evaluate a machine learning classifier as a tool for prediction of the risk of presenting a non-ST segment elevation acute coronary syndrome (ACS) in patients consulting an SEM with chest pain. Methods: 161 patients consulting SEM with chest pain were analyzed. Objective variables of the patient and subjective variables of pain characterization were recorded during the triage stage by means of a machine learning classifier. Results: The mean age was 57.43±12 years, 75% male and 16% had prior cardiovascular disease. 57.8% presented an ACS with an incidence of 29.8%, which 35% required PCI and 9.9% CRM in a 30-day follow-up period. A Random Forest Classifier was used as a classification model. The Random Forest Classifier presented an area under the ROC curve of 0.8991, sensitivity of 0.8552, specificity of 0.8588 and precision of 0.8441. The most strongest predictor variables were weight (p=0.002), age (p=5.011e-07), pain intensity (p=3.0679e-05), systolic blood pressure (p = 0.6068) and subjective pain characteristics (p=1.590e-04). Conclusions: Machine learning classifiers are a useful tool for predicting the risk of acute coronary syndrome at 30 days follow-up period.

The HEART score in the era of the European Society of Cardiology 0/1-hour algorithm.

BACKGROUND: The European Society of Cardiology's 0/1-hour algorithm improves the early triage of patients towards "rule-out" or "rule-in" of non-ST-segment elevation myocardial infarction. The HEART score is a risk stratification tool for patients with undifferentiated chest pain. We sought to evaluate the performance of the European Society of Cardiology 0/1-hour algorithm and the HEART score to evaluate chest pain patients in the emergency department. METHODS: In this prospective study, we applied the European Society of Cardiology 0/1-hour algorithm and the HEART score in 1355 consecutive patients who presented to the emergency department with symptoms suggestive of acute coronary syndrome without ST-segment elevation. Patients were followed for non-ST-segment elevation myocardial infarctions and major adverse cardiac events at 30 days: death, non-ST-segment elevation myocardial infarction, or unplanned coronary revascularization. RESULTS: The European Society of Cardiology 0/1-hour algorithm classified 921 (68.0%) patients as "rule-out" and the HEART score classified 686 (50.6%) patients as "low-risk". The 30-day incidence of non-ST-segment elevation myocardial infarctions was 0.32% in the European Society of Cardiology 0/1-hour algorithm "rule-out" patients versus 0.29% in the HEART score "low-risk" patients (p=0.75). The rate of major adverse cardiac events was 7.7% in the European Society of Cardiology 0/1-hour algorithm "rule-out" patients versus 1.1% in the HEART score "low-risk" patients (p<0.001). CONCLUSION: The European Society of Cardiology 0/1-hour algorithm identified more patients with low risk of non-ST-segment elevation myocardial infarctions at 30 days whereas for major adverse cardiac events, the HEART score had a greater capacity to detect low-risk patients.

Resultados de los primeros pacientes con sospecha de síndrome coronario agudo evaluados con el algoritmo de 1 hora propuesto por la Sociedad Europea de Cardiología / Results of the First Patients with Suspected Acute Coronary Syndrome Evaluated with the 1-hour Algorithm Proposed by the European Society of Cardiology

RESUMEN Introducción: La Sociedad Europea de Cardiología recomienda para la evaluación del dolor torácico un algoritmo con medición seriada de dos troponinas de alta sensibilidad separadas por una hora. Sin embargo, la alta eficacia y seguridad solo se han estimado según supuestos basados en modelos teóricos. Probamos por primera vez su desempeño en nuestro medio cuando se integra en la rutina diaria. Métodos: Estudio prospectivo unicéntrico que incluyó a pacientes no seleccionados que presentaban sospecha de infarto sin elevación del ST en el servicio de emergencias, a los que se les practicó el algoritmo SEC 0/1h utilizando troponina T de alta sensiblidad. Se evaluó el comportamiento en términos de incidencia a 30 días de los eventos de infarto agudo de miocardio, muerte cardiovascular y el combinado de infarto agudo de miocardio, muerte o revascularización coronaria. Resultados: Se incluyeron 1351 pacientes con una edad media de 61 ± 14 años, 12,4% de diabéticos y 35,8% de evento coronario previo. La tasa de infarto agudo de miocardio fue del 11% con una mortalidad del 0,29%. De acuerdo con la aplicación del algoritmo, 917 pacientes fueron catalogados como "externar" (67%); 270, como "observar" (20%); y 164, como "internar" (13%). La tasa del evento infarto agudo de miocardio resultó del 0,3% en "externar"; del 7%, en "observar"; y del 77,4%, en "internar" (p < 0,001). Por su lado, la muerte o revascularización coronaria resultó de 7,7% en "externar"; del 17,7%, en "observar"; y del 80,4%, en "internar" (p < 0,001). Conclusiones: El algoritmo de 1 hora presentó una buena capacidad para estratificar a pacientes que consultan con sospecha de infarto agudo de miocardio con un gran valor predictivo negativo para excluir el evento de infarto a los 30 días, aunque dicho valor disminuye cuando el evento considerado es la necesidad de revascularización coronaria.

ABSTRACT Background: The European Society of Cardiology (ESC) recommends an algorithm for the evaluation of chest pain with serial measurement of two high sensitivity troponins separated by one hour. However, the high efficacy and safety of the algorithm has only been estimated according to assumptions based on theoretical models. We tested for the first time its performance in the real world by incorporating it into the daily routine of our center. Methods: This is a prospective, single center study using the ESC 0/1h algorithm with high sensitivity troponin T on unselected patients who presented at the emergency department with suspected non-ST-segment elevation acute myocardial infarction. Efficacy and safety were assessed in terms of the 30-day incidence of acute myocardial infarction, cardiovascular death and the composite of acute myocardial infarction, death or coronary revascularization. Results: A total of 1,351 patients were included in the study. Mean age was 61±14 years, 12.4% were diabetics and 35.8% had previous history of coronary events. The rate of acute myocardial infarction was 11% and the rate of mortality 0.29%. According to the application of the algorithm, 917 patients were catalogued as "rule out" (67%), 270 as "observe" (20%) and 164 as "rule in" (13%). The rate of acute myocardial infarction was 0.3% in "rule out", 7% in "observe" and 77.4% in "rule in" (p <0.001). Moreover, death or coronary revascularization was 7.7% in "rule out", 17.7% in "observe" and 80.4% in "rule in" (p <0.001). Conclusions: The 1-hour algorithm showed a good capacity to stratify patients presenting with suspicion of acute myocardial infarction and a high negative predictive value to exclude infarction at 30 days, although this capacity decreases when the event considered is the need for coronary revascularization.

Utilidad del score HEART con troponina T de alta sensibilidad para la evaluación de pacientes con dolor torácico / Usefulness of the HEART Score with High-Sensitivity Troponin T for the Evaluation of Patients withChest Pain

RESUMEN Introducción: El score HEART consiste en una prueba sencilla que fue diseñada para estratificar a los pacientes que consultan al servicio de emergencias por dolor torácico, según su riesgo de presentar un síndrome coronario agudo a corto plazo. Fue creado inicialmente con troponina de cuarta generación, pero el advenimiento de la troponina de alta sensibilidad impuso su incorporación al score y la reevaluación de su comportamiento. Objetivo: Nos propusimos evaluar el comportamiento del score HEART con troponina de alta sensibilidad. Material y métodos: Se realizó un estudio prospectivo que incluyó 1464 pacientes (p) que consultaron al servicio de emergencia por dolor torácico y que tenían electrocardiograma sin elevación del segmento ST. Se evaluó la incidencia de MACE (combinado de infarto agudo de miocardio, muerte y revascularización) a 30 días. Resultados: El índice clasificó 739 pacientes (50,5 %) como de bajo riesgo, 515 pacientes (35,2%) de riesgo intermedio y 210 pacientes (14,3%) de alto riesgo. La incidencia de la combinación de infarto agudo de miocardio, muerte y revascularización fue del 1,35% en el primer grupo; del 20%, en el segundo; y del 71%, en el tercero (long rank test p < 0,001). El área bajo la curva global para la combinación de infarto agudo de miocardio, muerte y revascularización fue de 0,91 (0,89-0,93). Conclusiones: El score HEART que utiliza troponina de alta sensibilidad tiene una gran capacidad para clasificar pacientes con dolor torácico de acuerdo con su riesgo de presentar eventos cardiovasculares en el corto plazo.

High-Sensitivity Cardiac Troponin Levels in Supraventricular Tachyarrhythmias.

BACKGROUND: High-sensitivity cardiac troponin assays have provided a significant contribution for the early diagnosis of cardiovascular events. However, elevated cardiac troponin levels may occur in other clinical situations as supraventricular tachyarrhythmias with concerns about the mechanism of this elevation. OBJECTIVES AND METHODS: The goal of this study was to describe the performance of high-sensitivity cardiac troponin T (hs-cTnT) assay in patients presenting to the emergency department with a primary diagnosis of supraventricular tachyarrhythmia and to evaluate its relation with cardiovascular events during follow-up. RESULTS: One hundred patients were included; mean age was 64 ± 12 years and 59.8% were men. The most common arrhythmia at admission was atrial fibrillation (68%), followed by atrial flutter (16%) and reentrant tachycardia (16%). The results of the first determination of hs-cTnT were positive (>14 ng/L) in 44.2% of the patients and the second determination was positive in 50.7% of the cases. The variation between the first and the second troponin levels was 1 (0-5) ng/L, and was >7 ng/L in 24.6% of the cases, with a clear trend toward higher troponin values in reentrant tachycardias. Four events were reported at 30 days; in all the cases the patients had presented atrial fibrillation and there were no significant differences in hs-cTnT values. CONCLUSIONS: There are a significant number of patients with supraventricular tachyarrhythmias who present elevated hs-cTnT levels. The association of this elevation with cardiovascular events seems to be very low.

Predictors of conversion of recent-onset atrial fibrillation treated with vernakalant.

BACKGROUND: Vernakalant is an available drug for the treatment of recent-onset atrial fibrillation, producing conversion between 55% and 87% of the patients treated. In this sense, little is known about the predictors of conversion with this agent. The aim of our study was to analyze the predictors of conversion in our 2-year experience using vernakalant for the treatment of recent-onset atrial fibrillation. METHODS: One hundred twenty-one patients with recent-onset atrial fibrillation without hemodynamic impairment received pharmacological treatment with vernakalant. Clinical variables, history of cardiovascular diseases, and echocardiographic data were recorded. RESULTS: Mean age was 58.1 ± 13.9 years and 13.4% of patients had structural heart disease. Conversion to sinus rhythm was achieved in 84.5% of patients, and 46% required the second dose of vernakalant. After analyzing the predictors of conversion, the presence of structural heart disease was significantly larger in the group without conversion (35.3 vs 9.7%; P = 0.02). The mean ejection fraction in the group with conversion was 61.05 ± 5.7% versus 54.9 ± 8.4% in the group without conversion (P = 0.016). After dichotomizing the variable ejection fraction, patients with ejection fraction <55% had a lower conversion rate (P = 0.001). CONCLUSION: In our study, the absence of any kind of structural heart disease and preserved systolic function were associated with greater conversion rate with vernakalant.