Dr Y had applied the Ottawa ankle rules—decision rules designed to exclude fractures of the malleolus and the midfoot—and found no bone tenderness.1
He had previously visited the website of a centre for evidence based medicine2
and printed out a list of diagnostic tests that can rule out, or rule in, the condition in question without requiring further investigations.
The probability of disease, given a positive or negative test result (post-test probability), is usually obtained by calculating the likelihood ratio of the test result and using formulas based on Bayes's theorem (see box 1), or a nomogram,3
to convert the estimated probability of the suspected diagnosis before the test result was known (pretest probability) into a post-test probability, which takes the result into account.4
Likelihood ratios indicate how many times more likely a test result is to be expected in a patient with the disease compared with a person free of the disease and thus measure a test's ability to modify pretest probabilities.
David Sackett and others have argued that such calculations are unnecessary when a test is highly sensitive or highly specific.4-6
In this situation the likelihood ratio of a negative test will generally be very small, and the likelihood ratio of a positive test very large. A negative test will thus rule out, and a positive result rule in, disease. Two mnemonics that capture the properties of such tests have been coined: SnNOut (high sensitivity, negative, rules out) and SpPIn (high specificity, positive, rules in).4
This concept has become increasingly popular, with many websites for evidence based medicine listing such tests and inviting users to nominate further SpPIn and SnNOut tests. The understanding of the SnNOut principle among medical students was recently examined in a randomised trial.7
Negative results from highly sensitive tests can rule a diagnosis out (sensitive, negative, out = SnNOut), and positive results from highly specific tests can rule a diagnosis in (specific, positive, in = SpPIn)
Studies quoted as showing SpPIn or SnNOut properties may be affected by spectrum bias, partial verification bias, or incorporation bias. Others may be too small to define test characteristics with sufficient precision
The power of a test to rule a diagnosis out does not depend exclusively on its sensitivity, as suggested by the SnNOut rule, but is reduced by low specificity. Similarly, the power to rule in depends on both specificity and sensitivity
The evidence from studies of a test's accuracy should be critically assessed, and post-test probabilities (with 95% confidence intervals) should be calculated when evaluating potential SnNOut or SpPIn tests
Assuming that a diagnosis can be ruled in or out with confidence, when in reality it cannot, could have serious consequences for patients
The website listed the Ottawa ankle rules as a SnNOut test,2
indicating that in the teacher's case a fracture could safely be ruled out without radiography. Indeed, the patient made an uneventful and full recovery within four weeks. Alerted by the patient's alcoholic breath, Dr Y wondered whether an alcohol problem might have contributed to the accident and used the CAGE questions (see box 2) to investigate this further. According to the same website,2
the CAGE instrument has SpPIn properties, ruling the diagnosis in if two or more questions are answered affirmatively. The patient confirmed that she felt she should cut down on alcohol and that she had felt bad repeatedly about her drinking. Dr X, who had known her for over 10 years, explained that the patient's alcohol intake was moderate and well controlled, and that she was socially well integrated but very health conscious and somewhat anxious. A few days later, Dr X got a telephone call from the patient, who was clearly upset about the locum doctor's suggestion that she had an alcohol problem. A further consultation was required to clarify the situation and restore trust.
Box 1: Definitions of concepts and terms
Sensitivity—The proportion of people with the disease who are correctly identified by a positive test result (“true positive rate”)
Specificity—The proportion of people free of the disease who are correctly identified by a negative test result (“true negative rate”)
SnNOut—Mnemonic to indicate that a negative test result (N) of a highly sensitive test (Sn) rules out the diagnosis (Out)
SpPIn—Mnemonic to indicate that a positive test result (P) of a highly specific test (Sp) rules in the diagnosis (In)
Likelihood ratios—Measure of a test result's ability to modify pretest probabilities. Likelihood ratios indicate how many times more likely a test result is in a patient with the disease compared with a person free of the disease.
Likelihood ratio of a positive test result (LR+)—The ratio of the true positive rate to the false positive rate: sensitivity/(1-specificity)
Likelihood ratio of a negative test result (LR-)—The ratio of the false negative to the true negative rate: (1-sensitivity)/specificity
Pretest probability (prevalence)—The probability that an individual has the target disorder before the test is carried out
—The probability that an individual with a specific test result has the target condition (post-test odds/[1+post-test odds]) or
Pretest odds—The odds that an individual has the target disease before the test is carried out (pretest probability/[1-pretest probability])
Post-test odds—The odds that a patient has the target disease after being tested (pretest odds×LR)
Positive predictive value (PPV)—The proportion of individuals with positive test results who have the target condition. This equals the post-test probability given a positive test result
Negative predictive value (NPV)—The proportion of individuals with negative test results who do not have the target condition. This equals one minus the post-test probability given a negative test result