Evidence-Based Emergency Medicine/Skills for Evidence-Based Emergency CareInterval likelihood ratios: Another advantage for the evidence-based diagnostician*,**
Introduction
Clinicians look to the results of diagnostic tests such as peripheral WBC counts and cardiac biomarkers as the basis for modifying their estimates of how likely it is that a particular patient has a clinically important disease, condition, or injury. Studies of the performance of such tests commonly simplify their results by calculating sensitivity and specificity in comparison to a criterion standard for the presence or absence of the disease entity, where all values above a single threshold level are considered “positive,” and all those below it are considered “negative.” This implies that all test results above the threshold increase the likelihood that the disease is present to the exact same degree.
However, using appendicitis as an example, clinicians instinctively recognize that a WBC count of 18×103/μL renders a patient with abdominal pain and a consistent clinical presentation more likely to have appendicitis than if the WBC count were only 12×103/μL (even though both values are elevated). Similarly, a clinician's suspicion that a patient with chest pain is having a myocardial infarction is likely to be much greater if the troponin I level were 5.0 μg/L than if it were 0.5 μg/L, even though both are above a typical standard cutoff of 0.4 μg/L. In this article, we will clarify the nature of the problem presented by diagnostic tests having a continuous set of possible results. We will show how likelihood ratios based on interval ranges help to quantify the differences in diagnostic effect that clinicians instinctively recognize in these settings, and how their use can help to avoid erroneous interpretations of such a test when the results lie near a dichotomous cutoff value.
Section snippets
How do likelihood ratios help clinicians make decisions?
A previous installment of Annals' “Skills for Evidence-Based Emergency Care” series explained the unique value of likelihood ratios in the interpretation of diagnostic test results.1 The likelihood ratio is the ratio of the probability of a given test result in patients with disease to the probability of the same test result in patients without disease. This ratio represents the magnitude of change from a clinician's initial suspicion for disease (pretest probability) to the likelihood of
Why do interval likelihood ratios use more of the data?
To demonstrate the advantage of using interval likelihood ratios rather than a simple dichotomization of a continuous variable, we will use data from a study by Andersson et al.5 The authors evaluated the utility of a broad range of criteria, including the WBC count, in the diagnosis of acute appendicitis in patients admitted to 2 hospitals in Sweden. When the results are displayed in a simple 2×2 table format using a WBC count of 10.0×103/μL as the cutoff, likelihood ratios for positive
How do interval likelihood ratios relate to roc curves?
The ROC curve provides a visual display of the relationship between the choice of possible cutoff values and the corresponding sensitivities and specificities for a continuous diagnostic test variable (Figure 2).2
Acknowledgements
We acknowledge the assistance of Peter C. Wyer, MD, in preparing the manuscript.
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The authors report this study did not receive any outside funding or support.
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Reprints not available from the authors.