Performance of the European Society of Cardiology Algorithm for the Assessment of Chest Pain in Patients with Diabetes Mellitus

espanolIntroduccion: Dado que los pacientes con diabetes tienen habitualmente niveles de troponina mas elevados que la poblaciongeneral, nos propusimos evaluar el comportamiento del algoritmo de la Sociedad Europea de Cardiologia que utiliza la medicionde troponina de alta sensibilidad al ingreso y 1 hora despues en estos pacientes. Material y metodos: Se evaluaron 1140 pacientes que consultaron por dolor toracico con electrocardiograma sin supradesnivel del segmento ST. El algoritmo estratifica los pacientes en tres grupos de riesgo: “externar”, “observar” e “internar”. Se valoroel comportamiento del algoritmo para el evento infarto a 30 d. Resultados: En total, 124 pacientes (10,8%) tenian diabetes. Ninguno de los clasificados como “externar” (40,3%) presentoinfarto a 30 dias. 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 proporcion de pacientes para “externar” fue menor en diabeticos (40,3%vs. 72,1%, p Conclusiones: El uso del algoritmo en pacientes con diabetes mostro una alta sensibilidad y un alto valor predictivo negativopara “externar” comparable a la poblacion general. En cuanto al grupo “internar”, presento menor especificidad, pero altovalor predictivo positivo. Esto lo transforma en una util herramienta para la practica diaria. EnglishBackground: 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 useshigh 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 algorithmstratifies patients in three risk groups: rule-out, observe and rule-in. We evaluated the performance of the algorithm to predictmyocardial 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 myocardialinfarction 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= 0.865and 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 Conclusion: The use of the algorithm in patients with diabetes revealed high sensitivity and negative predictive value to ruleout, which was similar to that of the general population. Regarding the rule-in group, it had lower specificity but high positivepredictive value. This performance makes the algorithm a useful tool for daily practice.