A mathematical approach to medical diagnosis: application of polycythemic states utilizing clinical findings with values continuously distributed

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.

Generalized likelihood ratios for quantitative diagnostic test scores

The reduction of quantitative diagnostic test scores to the dichotomous case is a wasteful and unnecessary simplification in the era of high-speed computing. Physicians could make better use of the information embedded in quantitative test results if modern generalized curve estimation techniques were applied to the likelihood functions of Bayes' theorem. Hand calculations could be completely avoided and computed graphical summaries provided instead. Graphs showing posttest probability of disease as a function of pretest probability with confidence intervals (POD plots) would enhance acceptance of these techniques if they were immediately available at the computer terminal when test results were retrieved. Such constructs would also provide immediate feedback to physicians when a valueless test had been ordered.

Computer-aided medical diagnosis: literature review

The difficulty of the medical diagnostic task and the advantages of the computer as an aid in this task are discussed. The general strategy and structure of any computer-aided system is presented, and the relationship of diagnostic accuracy to key variables involved in the development, test and use of a computer-aided diagnostic system is examined. These variables include: the computer algorithm, the source of the information used to develop the data base, the number and type of diseases under investigation, the number and type of indicants used, the source of the test sample, and the source of the validated diagnosis. A table of 58 empirically tested computer-aided medical diagnostic systems is presented; each system is summarised in relation to the variables mentioned above and diagnostic accuracy.

Doctors as decision-makers: a computer-assisted study of diagnosis as a cognitive skill

When viewed as a sequence of decisions clinical diagnosis becomes amenable to detailed investigation in terms of standard statistical concepts. A study of six clinicians diagnosing identical sets of cases of non-toxic goitre is used to illustrate an objective technique for studying the diagnostic process with the aid of a digital computer. Considerable variation in clinicians' routes to correct diagnosis is shown when these routes are compared in detail by five statistical measures related to the effective use of the information available to the clinicians. For rapid analysis of diagnostic skill two visual methods are presented. These can be developed for teaching undergraduates the interpretative skills involved in diagnosis and for studying such skills in experienced clinicians.