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

How to use the leaf plot

The starting probability of a diagnosis is shown diagonally along the leaf ‘vein’ from nil at the bottom left, to complete certainty at the top right (Figure 1). This is your best guess of approximately how likely it is that your patient has that condition; the precise position is not critical. Once you have decided where your patient’s pre-test probability sits on the leaf’s vein, then the impact that a positive test result will have on that probability is shown by the height of the vertical jump up to the red line directly above. Similarly, the impact of a negative test is shown by how far the probability drops down as it falls to the blue line. It follows that if the pink and blue areas are close to the central vein like a willow leaf, the test is weak and will make little difference to your decision making, whereas a test that produces a broad-leafed plot that reaches towards the corners of the graph will be much more useful.

• Download figure
• Open in new tab
• Download powerpoint

Figure 1.

Leaf plot to see how useful it would be to check for urine cloudiness to help diagnose a child’s urine infection (UTI), assuming that the test has a sensitivity of 0.75 and specificity of 0.94. The initial estimated probability of the diagnosis of UTI can be anywhere on the diagonal black line from 0 at the bottom left to 1 at the top right, depending on the clinical details. The impact of a positive test (a cloudy urine) is shown by the red line and shaded area, and is easy to read from the left-hand axis, and the impact of a negative test (a clear urine) is shown in blue on the right. Points A, B, and C are used to illustrate three clinical examples given in the text.

A worked example

Here we will see how to find out whether it is useful to check if a child’s urine looks cloudy when you are considering the diagnosis of them having a urinary tract infection (UTI). If three-quarters of children with a UTI have cloudy urine (sensitivity 0.75), and 94% of healthy children pass clear samples (specificity 0.94), the test would generate the leaf plot shown in Figure 1. It is immediately obvious that the red area is bigger than the blue, indicating that a cloudy urine (positive test) has greater power to rule in UTIs than a clear one (negative test) does to rule them out. Now let us consider three clinical scenarios that might present in primary care, corresponding to points A, B, and C on the leaf vein.

Point A would be what would happen if you decided to screen a healthy child for a UTI by checking if they had cloudy urine. The chances of a UTI in children with crystal-clear urine would fall from an already very low level to even closer to zero, and the chances of a child with cloudy urine having a UTI would still be less than evens. Not a useful screening test.

Point B could represent the starting probability of a UTI for an otherwise well 6-year-old female presenting to her GP with slight stinging on micturition, after passing a concentrated urine on a hot day. You might have a moderate (say, one-in-three) concern that she could have a first UTI. Here, a clear urine would reduce her probability of having a UTI to about one in eight, enabling you to watch and wait, whereas a cloudy sample would increase her probability of having a UTI to over 85%, which might prompt you to culture a mid-stream urine. Possibly a useful test in these circumstances.

Point C might be a 2-year-old female who you know has bilateral renal scarring caused by recurrent febrile UTIs and vesicoureteric reflux, and who has become febrile again and started vomiting. Here, because her starting probability of having another UTI is high (say, about 95%), finding a clear urine would still leave her with about an 85% chance of having an infection, and a cloudy test would merely increase her probability from 95% to near certainty. Neither of these mild alterations to an already high risk would alter your decision to immediately culture a urine sample and commence antibiotic treatment while awaiting microbiological confirmation. It would therefore be a waste of time for you to look at the urine clarity in this setting. Although assessing the turbidity of urine is a trivial task, other tests might be time consuming, cause delay, and be costly.

Other examples

Other leaf plots of commonly used screening tests are shown in Figure 2. The prostate-specific antigen test for prostate cancer⁵ only provides weak additional diagnostic help, as shown by its ‘willow’ leaf shape. The d-dimer test for pulmonary embolus⁶ is often misused.⁷ With a narrow pink side and a broad blue side, the leaf plot makes is clear that a positive result is not useful for making the diagnosis and that the main use of the test is excluding a pulmonary embolus in patients with low baseline risk. The leaf plot of the 10 g Semmes-Weinstein monofilament examination has a broad pink side, which means a positive test in a diabetic patient makes that a peripheral neuropathy much more likely,⁸ but a negative test does not strongly rule a neuropathy out.

• Download figure
• Open in new tab
• Download powerpoint

Figure 2.

Leaf plots for three commonly used screening tests. The prostate-specific antigen test for prostate cancer⁵ produces a ‘willow leaf’ appearance showing only a limited ability to confirm or exclude prostate cancer (a), the d-dimer assay for pulmonary embolus⁶ has a broad blue side which shows that a negative test helps to rule out a pulmonary embolus, but that a positive test makes little impact on making the diagnosis (b), and the monofilament tests in diabetes⁶ has a very broad pink side that indicates a positive result makes a peripheral neuropathy very likely, but a negative test has less power to rule one out (c).