Risk Stratification in Chest Pain: Impact on the Diagnosis of Acute Coronary Syndrome

Abstract Background The implementation of institutional protocols in the emergency department (ED) for risk stratification in patients with chest pain has been recommended. Objective To assess the sensitivity, specificity and predictive value of an institutional risk stratification protocol for chest pain suggestive of acute coronary syndrome (ACS). Method Cross-sectional study conducted based on the computerized records of patients treated with the use of a chest pain protocol adapted from the Manchester protocol. The level of risk was stratified by applying five colors representing the respective levels. Each color represents a level of severity and a maximum waiting time for receiving medical care. Red and orange were considered to be high priority, while patients with yellow, green or blue indications were considered to represent a low priority. To compare the type of diagnosis and the classification of priority for receiving care, the Pearson's chi-square test was used, considering a significance level of p< 0.05 for all tests. Results The records of 1,074 patients admitted to the cardiology ED were analyzed. Men (54%), with a mean age of 60 ± 15 years, with complaints of chest pain (44%) of moderate intensity (80%) were predominant the study. Of these patients, 19% were classified as high priority, while 81% were considered to represent a low priority. ACS was confirmed in 23% of the patients, with 34% of them being classified as high priority and 66% as low priority. The sensitivity of the risk stratification protocol for chest pain was 33.7% and the specificity was 86.0%, with a positive and negative predictive value of 41.7% and 81.3%, respectively. Conclusion The Institutional risk stratification protocol for chest pain suggestive of ACS presented satisfactory specificity and a low degree of sensitivity. Int J Cardiovasc Sci. 2020; [online].ahead print, PP.0-0

[Bayesian tragedy, from clinical iatrogenesis to biotechnology]. / La tragedia bayesiana desde la iatrogenia clínica hasta la biotecnología.

Diagnostic algorithms, as well as the biotechnological design, require the calculation of conditional probability, given the presence of certain positive data, in the context of prevalence, sensitivity and specificity; It is necessary to estimate the probability that the patient has a certain disease. Sometimes, with a test of scrutiny, it goes from a probability of 1/1000 to 1/20, constituting a great diagnostic advance, reducing the uncertainty spectacularly; However, the tragedy is that most doctors believe that the probability changed from 0.1% (1/1000) to more than 90%, which is outrageously wrong. Iatrogeny arises from the error in answering the question: "given that the test is positive, what is the probability that the patient has the disease?" In other cases, tragedy is to apply a test to an individual belonging to a subpopulation for which it was not designed. In addition, it is evident that the fascination for the sensitivity avoids the application of less sensitive methods in populations that are abandoned; It is not a matter of making better tests than those that the State does to the patients it attends, but of making less accurate tests for the patients that the State does not attend.

Los algoritmos diagnósticos así como el diseño biotecnológico, requieren del cálculo de la probabilidad condicional, dada la presencia de ciertos datos positivos en el contexto de la prevalencia, de la sensibilidad y de la especificidad, se necesita estimar la probabilidad de que el paciente tenga una determinada enfermedad. A veces, con una prueba de escrutinio se pasa de una probabilidad de 1/1000 a 1/20, constituyendo un gran avance diagnóstico, reduciendo la incertidumbre espectacularmente; sin embargo, la tragedia consiste en que la mayoría de los médicos creen que la probabilidad cambió de 0.1% (1/1000) a más del 90%, lo que es escandalosamente errado. La iatrogenia nace del error al contestar a la pregunta: "Dado que la prueba es positiva, ¿cuál es la probabilidad de que el paciente tenga la enfermedad?" En otros casos lo trágico consiste en aplicar una prueba a un individuo que pertenece a una subpoblación para la que esta no fue diseñada. Adicionalmente, se pone en evidencia que la fascinación por la sensibilidad evita que se apliquen métodos menos sensibles en poblaciones que están abandonadas, pues no se trata de hacer mejores pruebas que las que hace el Estado a los pacientes que atiende, sino de hacer pruebas de menor exactitud a los pacientes que no atiende el Estado.