Resumo

SPROCKEL, John  e  DIAZTAGLE, Juan Jose. Diagnostic accuracy of a bayesian network model in acute coronary syndromes. Rev. Univ. Ind. Santander. Salud [online]. 2015, vol.47, n.2, pp.179-185. ISSN 0121-0807.

Introduction: The characterization and diagnosis of chest pain, with emphasis on acute coronary syndromes (ACS), is a fundamental requirement for the doctors at the emergency service. Objective: The aim of the present study is to design and evaluate the performance of Bayesian networks to back up the diagnosis of ACS. Methodology: A diagnostic tests study in which two models of Bayesian networks were designed and trained in the framework OpenMarkov, using the variables of the Braunwald angina probability scale in a group of 159 patients, which was validated afterwards in a cohort of 108 adult patients hospitalized with suspicion of ACS in a third level hospital. Results: Low sensitivity was obtained, with adequate specificity and positive predictive values, though (62, 86, and 87% respectively). Performance was better in the cases that had electrocardiogram and negative biomarkers. Conclusion: A model of Bayesian networks trained from the variables of the Braunwald unstable angina probability scale, exhibits an acceptable performance for the diagnosis of ACS.

Palavras-chave : Chest Pain; Acute Coronary Syndromes; Classification/Diagnosis; Bayesian Networks.

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