Abstract:In this article we provide an introduction to the use of likelihood ratios in clinical ophthalmology. Likelihood ratios permit the best use of clinical test results to establish diagnoses for the individual patient. Examples and step-by-step calculations demonstrate the estimation of pretest probability, pretest odds, and calculation of posttest odds and posttest probability using likelihood ratios. The benefits and limitations of this approach are discussed.
“…Computing post-test odds after a series of diagnostic tests is much easier than using the sensitivity/specificity method 29 . It is an alternative method that could be used for additional information beyond the BMI criteria; for example, if we have LR values for other diagnostic tests, such as cardiovascular risk markers.…”
AbstrAct:Objective: To compare the sensitivity and specificity of body mass index (BMI)-based classification systems and to determine the optimal cut-offs for predicting excess body fatness in schoolchildren. Methods: 2795 schoolchildren aged 7 -10 years were examined. Excess body fatness was defined as the standardized residuals of the sum of three skinfold thickness ranking at or above the 90th percentile. The international BMI-based system recommended by the World Health Organization (WHO-2007) was evaluated on the basis of its sensitivity and specificity for detecting excess body fatness and compared with a national BMI reference (Brazil-2006). Likelihood ratios analysis was used to select the optimal cut-offs in each curve. Results: The two classification systems presented high sensitivity (92.5 -98.6%) and moderate specificity (75.9 -85.0%) for both sexes. The optimal BMI cut-offs improved specificity with no marked loss of sensitivity. Using the proposed BMI cut-offs, the post-test probability of predicting excess body fatness for children classified as non-overweight decreased from 10 (pre-test probability) to 1.4% in girls and to 1.1% in boys. For overweight children, this probability increased to more than 46.0%.
Conclusion:The results showed that both the WHO-2007 and Brazil-2006 classification systems can be used as screening instruments for excess body fatness, and that one of the limitations of using the BMI-for-age references could be improved by refining the existing cut-offs.
“…Computing post-test odds after a series of diagnostic tests is much easier than using the sensitivity/specificity method 29 . It is an alternative method that could be used for additional information beyond the BMI criteria; for example, if we have LR values for other diagnostic tests, such as cardiovascular risk markers.…”
AbstrAct:Objective: To compare the sensitivity and specificity of body mass index (BMI)-based classification systems and to determine the optimal cut-offs for predicting excess body fatness in schoolchildren. Methods: 2795 schoolchildren aged 7 -10 years were examined. Excess body fatness was defined as the standardized residuals of the sum of three skinfold thickness ranking at or above the 90th percentile. The international BMI-based system recommended by the World Health Organization (WHO-2007) was evaluated on the basis of its sensitivity and specificity for detecting excess body fatness and compared with a national BMI reference (Brazil-2006). Likelihood ratios analysis was used to select the optimal cut-offs in each curve. Results: The two classification systems presented high sensitivity (92.5 -98.6%) and moderate specificity (75.9 -85.0%) for both sexes. The optimal BMI cut-offs improved specificity with no marked loss of sensitivity. Using the proposed BMI cut-offs, the post-test probability of predicting excess body fatness for children classified as non-overweight decreased from 10 (pre-test probability) to 1.4% in girls and to 1.1% in boys. For overweight children, this probability increased to more than 46.0%.
Conclusion:The results showed that both the WHO-2007 and Brazil-2006 classification systems can be used as screening instruments for excess body fatness, and that one of the limitations of using the BMI-for-age references could be improved by refining the existing cut-offs.
“…In other words, the PPV and NPV of a particular test will be different in a high-risk population than for an individual patient with low to average risk. 1 For example, a mammographic density observed in the breast of a 60-year-old woman whose sister had breast cancer means something entirely different than the same density seen in the breast of a 30-year-old asymptomatic woman with a negative family history. The PPV of mammography is higher in the 60-year-old woman with a positive family history than in the 30-year-old with no risk factors because the prevalence of breast cancer increases with age.…”
Section: Diagnostic Test Characteristicsmentioning
confidence: 96%
“…Posttest odds can be converted to posttest probability through the following formula: posttest probability = posttest odds/ (posttest odds + 1). 1 The posttest probability then represents the level of diagnostic certainty for this patient.…”
“…1 Accordingly, the use of tests in the clinic has to be directed by the ''subjective'' clinical examination, else the results make no sense. Appropriate interpretation of ''objective'' tests involves the use of likelihood ratios, preferably the use of multilevel likelihood ratios that can be combined with the pretest probability for the individual patient and the test result obtained for that patient.…”
There has been a push in medical diagnostics and treatment to move toward objective testing and away from subjective testing. However, it is not certain that the superiority of one over the other is as clear cut as is usually considered to be the case. This issue is raised because there are advantages and disadvantages to every testing modality. The important considerations are degree of validity, of relevance, and of ease of obtaining the test, regardless of the type of testing. When a clinical diagnosis is certain or virtually certain, there is no need to use a test for diagnostic purposes. There still might be a justification for testing, however, even in such situations. In most instances, meaningful interpretation of a test result requires clinical information. Such clinical information is, of course, subjective, but still necessary. The use of tests in the clinic has to be directed by the "subjective" clinical examination, else the results make no sense. Subjective is "subjective" and as such open to biased interpretation. "Objective tests" also are often open to as much bias and misinterpretation as are subjective evaluations; they can seduce us into a false sense of security. It is essential to remember that the need for clinical evaluation will remain, and that the quality of the clinical evaluation is what is most critical to appropriate evaluation of any test result.
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