[Use of Bayesian estimation as a diagnostic tool in clinical practice].
If one of the monitors indicates that perioperative myocardial ischemia is occurring, is it telling the truth or what is the probability of its telling the truth? This kind of question is imperative in the conduct of safe anesthesia practice. For this reason, we need to know, so called, the predictive value, which is essentially an application of Bayesian estimation. In this paper, fundamental knowledge and pitfalls in the issue of Bayesian estimation in relation to a predictive value are discussed and summarized. Prior probability, identical to the concept of prevalence is joined together with available information to form a posterior probability or a predictive value to make any clinically relevant decisions. The terms, sensitivity and specificity are defined and their relation to the predictive value is discussed as well. The architecture of Bayesian estimation is explained by taking examples from the literature.
Morita
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Department of Anesthesia, Teikyo University School of Medicine, Ichihara Hospital.
D000758:Anesthesia; D001499:Bayes Theorem; D003327:Coronary Disease / Q000175:diagnosis; D006801:Humans; D012680:Sensitivity and Specificity