An objective way to predict remission and relapse in Cushing disease using Bayes’ theorem of probability

In this study on patients with Cushing disease, post-transsphenoidal surgery (TSS), we attempt to predict the probability of remaining in remission, at least for a year and relapse after that, using Bayes’ theorem and the equation of conditional probability. The number of parameters, as well as the weightage of each, is incorporated in this equation. The study design was a single-centre ambispective study. Ten clinical, biochemical, radiological and histopathological parameters capable of predicting Cushing disease remission were identified. The presence or absence of each parameter was entered as binary numbers. Bayes’ theorem was applied, and each patient’s probability of remission and relapse was calculated. A total of 145 patients were included in the study. ROC plot showed a cut-off value of the probability of 0.68, with a sensitivity of 82