Clinical tests: sensitivity and specificity | BJA Education | Oxford Academic
Put another way, if the test is highly sensitive and the test result is negative you rules in disease with a high degree of confidence Specificity rule in or "Spin". Unlike sensitivity and specificity, the PPV and However, if a patient has signs of SLE a test are inserted into a 2×2 contingency table, PPV, NPPV, and likelihood ratio. Sensitivity and specificity are statistical measures of the performance of a binary classification .. Sensitivity is not the same as the precision or positive predictive value (ratio of true The tradeoff between specificity and sensitivity is explored in ROC analysis as a trade off between TPR and FPR (that is, recall and fallout).
Sensitivity, specificity, and other terms The following terms are fundamental to understanding the utility of clinical tests: When evaluating a clinical test, the terms sensitivity and specificity are used.
The terms positive predictive value PPV and negative predictive value NPV are used when considering the value of a test to a clinician and are dependent on the prevalence of the disease in the population of interest. Sensitivity The sensitivity of a clinical test refers to the ability of the test to correctly identify those patients with the disease.
A high sensitivity is clearly important where the test is used to identify a serious but treatable disease e.
Screening the female population by cervical smear testing is a sensitive test. However, it is not very specific and a high proportion of women with a positive cervical smear who go on to have a colposcopy are ultimately found to have no underlying pathology. Specificity The specificity of a clinical test refers to the ability of the test to correctly identify those patients without the disease.
As discussed above, a test with a high sensitivity but low specificity results in many patients who are disease free being told of the possibility that they have the disease and are then subject to further investigation. In this way, nearly all of the false positives may be correctly identified as disease negative.
Positive predictive value The PPV of a test is a proportion that is useful to clinicians since it answers the question: This is defined as how much more likely is it that a patient who tests positive has the disease compared with one who tests negative. Consider the following example: However, if a patient has signs of SLE e. We may also consider a woman who presents with breathlessness post-partum and where one of the differential diagnoses is pulmonary embolism. A D-dimer test would almost certainly be elevated in this patient population; therefore, the test has a low PPV for pulmonary embolism.
However, it has a high NPV for pulmonary embolism since a low D-dimer is unlikely to be associated with pulmonary embolism. Screening this population would therefore yield true positives and true negatives with 20 patients being tested positive when they in fact are well and 20 patients testing negative when they are ill. This discussion highlights the fact that the ability to make a diagnosis or screen for a condition depends both on the discriminatory value of the test and on the prevalence of the disease in the population of interest.
Receiver operator characteristic curves Consider the following hypothetical example: A sample of SpRs is tested before the examination resulting in a range of endorphin values. Now a crucial fact to grasp is that the positive predictive value varies according to the prevalence of the disease in the population from which your patient comes. If you are really keen, you can work this out for yourself; the notes from the Critical Appraisal course Module 4, pages 23 and 24 explain how.
Or else, you can take the intuitive route, as follows So, a patient may get a positive test result but if the prevalence in the population is very low, because of the small number of true cases mixed in with all those false positives, the test result may not mean very much.
Sensitivity and specificity - Wikipedia
Imagine you are a general practitioner and the disease is relatively rare among your patients. The pre-test probability of your patient having a disease will be low, and this will bring down the predictive value of a positive test result, even if the test itself is quite good.
However, when the same patient attends a specialist clinic at the hospital, where a lot of selection has already taken place and a larger proportion of all the patients have the disease, the predictive value of the same test will be much higher.
This was well illustrated in the BMJ article mentioned earlier. More on sensitivity, prevalence and predictive values Conclusion: This introduces likelihood ratios.
So, how do I combine prevalence and sensitivity of the test? Welcome to the world of Likelihood Ratios. These show how much knowing the test result will improve on a diagnostic guess based simply on pre-test probability: This shows how much more likely is a person with the disease to score positive than a person without the disease.
To bring in the prevalence piece there's a neat little nomogram diagram below.
You need to know the likelihood ratio for this particular test, and also the pre-test probability or prevalence. Draw a line through the pre-test probability on the left of the diagram, through the likelihood ratio in the central column, and then read off the post-test probability on the right-hand column.
Sensitivity and specificity
LRs higher than about 5 can be useful in ruling in a disease. There is also a likelihood ratio for a negative test result "LR-". It gives a result below 1; values below about 0.
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- 10.3 - Sensitivity, Specificity, Positive Predictive Value, and Negative Predictive Value
- Understanding and using sensitivity, specificity and predictive values
Calculators from the Knowledge Translation clearinghouse. You have to type in the numbers of cases in each of the four cells of the table. You will find more information on this topic in the Evidence-Based Medicine session on Diagnostic Tests. Using LRs, you can decide on an efficient way to set the sequence of diagnostic tests. LR- is greater than 50 Jekel J et al. Epidemiology, biostatistics, and preventative medicine.
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