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

OBJECTIVE: 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.

DESIGN AND METHODS: 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.

RESULTS: 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% (range 73-89%) and a specificity of 94% (range 83-99%) to predict the probability of remission. Eighty-one patients who were in remission at 1 year were followed up for relapse and 23 patients developed relapse of the disease. The Bayes' equation was able to predict relapse in only 3 out of 23 patients.

CONCLUSIONS: Using various parameters, remission of Cushing disease can be predicted by applying Bayes' theorem of conditional probability with a sensitivity and a specificity of 82% and 94%, respectively. This study provided an objective way of predicting remission after TSS and relapse in patients with Cushing disease giving a weightage advantage to every parameter.

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