Evidence-Based Emergency Medicine/Skills for Evidence-Based Emergency Care
Interval likelihood ratios: Another advantage for the evidence-based diagnostician*,**

https://doi.org/10.1067/mem.2003.274Get rights and content

Abstract

Emergency physicians are often confronted with making diagnostic decisions on the basis of a test result represented on a continuous scale. When the results of continuous data are expressed as binary outcomes using a single cutoff, loss of information and distortion may occur. In this setting, interval likelihood ratios provide a distinct advantage in interpretation over those based on a dichotomized sensitivity and specificity. Dividing the data into intervals uses more of the information contained in the data and allows the clinician to more appropriately interpret the test results and to make valid clinical decisions. This article illustrates the advantages of interval likelihood ratios with examples and demonstrates how to calculate them on the basis of different data formats. Authors and journals need to be encouraged to report the results of studies of performance of diagnostic tests using interval ranges rather than simple dichotomization when the tests involve continuous variables. [Ann Emerg Med. 2003;42:292-297.]

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

. An example of the calculation of interval likelihood ratios from an ROC curve for B-type natriuretic peptide as a predictor of cardiac events in patients with congestive heart failure. BNP, B-type natriuretic peptide. Adapted from Harrison A, Morrison LK, Krishnaswamy P, et al. B-type

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.

**

Reprints not available from the authors.

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