The leaf plot: a novel way of presenting the value of tests.

Abstract

The power and accuracy of clinical tests is usually reported either in terms of their sensitivity and specificity, their predictive values, or their likelihood ratios, but these concepts can be difficult for many GPs to apply to real-life clinical situations.1 ### Sensitivity and specificity These are independent of the prevalence of the condition (or its equivalent in an individual patient, your estimate of their pre-test probability of having the condition), and so cannot answer the clinician’s question of ‘How much does a positive or a negative result for this test or sign influence the probability of my provisional diagnosis?’ Correctly interpreting these values is difficult, and requires us to grasp non-intuitive concepts with ‘both sides of our brains’.2 ### Positive (PPV) and negative (NPV) predictive values These seem to make more sense, but are misleading because they can only be applied to populations with the same prevalence of the condition as was present in the study that generated them. For example, studies in special educational facilities show that finding a child with a single-palmar-crease gives a PPV of them having Down’s syndrome of about 75%, but if you notice this pattern in the setting of a normal infant having a 6-week check it then would only have a PPV of about 10%. ### Positive and negative likelihood ratios These seem more helpful because they determine how a test result will alter the pre-test odds, but it is not straightforward to quantify their impact for an individual patient. The clinician has to estimate that person’s pre-test odds of having the diagnosis (= probability/1 – probability), and then multiply that by the appropriate likelihood ratio to find their new odds. Because these methods are difficult to apply accurately in real practice, they may cause doctors to make vast errors when estimating the significance of screening results.3 Very few GPs use them in any formal way, instead relying …