Category: Diagnostic Skills

Diagnostic Skills

Probability Tree

The Probability Tree is used by clinicians to solve complex diagnostic challenges. Such diagnostic dilemmas are typically complicated by the lack of clarity/focus during clinical investigation. The application of the strategy called ‘Decision Analysis’, described by Pauker and Kassirer (1) will help estimate the probability of all possible outcomes in a diagnostic challenge and select an optimal course of action. Let us consider data from the study … Continue reading Probability Tree

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Diagnostic Skills

Diagnostic Efficiency

The receiver operating characteristic (ROC) curve is a measure of diagnostic efficiency, which is obtained by graphically plotting a series of true positive rates (sensitivity) against false positive rates (1 – specificity) for a binary outcome system. The ROC curve is a method to visualize the performance of a test with binary outcomes (+ / -) graphically. The area under the ROC (AUROC) curve is equal to the … Continue reading Diagnostic Efficiency

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Diagnostic Skills

Post-test Probability

Post-test probability: is the probability of the presence of a disease after a confirmatory diagnostic test. Method I:  Let us estimate post-test probability by hand calculation using the data from the study by Capra et al (1). To do this, we need to know the pre-test probability and the positive likelihood ratio values. For this purpose, let us assume the pre-test probability as 0.55. And, we will … Continue reading Post-test Probability

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Diagnostic Skills

Likelihood Ratios

Let us continue using the data from the study by Capra et al (1). The likelihood ratio is the percentage of diseased individuals with a given test result divided by the percentage of healthy individuals with the same test result (2). The likelihood ratios (positive and negative) can be calculated by using the following formulae (2): + LR = sensitivity / (1- specificity) = True positive rate / … Continue reading Likelihood Ratios

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Diagnostic Skills

Predictive Values

Let us continue our Predictive Values analysis using data from Capra et al (1). After a confirmed MRI diagnosis, we may have the following questions: What is the probability that the patient with a positive MRI result has the disease? or What is the proportion of patients with positive MRI results have the disease? Calculating the post-test probabilities (in other terms, post-test likelihood values or positive/negative predictive values) will help … Continue reading Predictive Values

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