Evidence based medicine Diagnostic tests Ross Lawrenson.
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Transcript of Evidence based medicine Diagnostic tests Ross Lawrenson.
![Page 1: Evidence based medicine Diagnostic tests Ross Lawrenson.](https://reader036.fdocuments.us/reader036/viewer/2022062322/5697c0191a28abf838cce3ee/html5/thumbnails/1.jpg)
Evidence based medicine
Diagnostic tests
Ross Lawrenson
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Diagnostic tests
• When looking at a paper about a diagnostic test we ask ourselves three questions.
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Diagnostic tests
• Is this test useful?
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Diagnostic tests
• Is this test useful?
• Is it reliable?
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Diagnostic tests
• Is this test useful?
• Is it reliable?
• Is it valid?
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Is this test useful?
• The test should have been researched in a study population relevant to the individual or population in whom it is to be used.
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Reliability
• Reliability refers to the repeatability or reproducibility of a test.
• It can be assessed by repeating the test using the same or different observers.
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Validity
• Relates to whether the test measures what it purports to measure. Is the result true?
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Validity
• For example if you measure blood pressure in an obese patient and use a cuff that is too small you are likely to get a falsely high reading. The reading maybe reliable (you get the same blood pressure if you do it again) but it lacks validity.
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Sensitivity and specificity
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Disease Healthy TotalTest + a b a+bTest - c d c+dTotal a+c b+d
Sensitivity and specificity
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Sensitivity and specificity
Disease Healthy Total
Test +ve a b a+b
Test -ve c d c+d
Total a+c b+d a+b+c+d
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Sensitivity
• The probability that the test will be positive if the disease is present
• = a/a+c
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Sensitivity
• The probability that the test will be positive if the disease is present
• = a/a+c
• A sensitive test is likely to also record a number of false positive tests
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Sensitivity
If the cut off point of this test is set low then it will be sensitive (all patients with disease will testpositive) but there will also be a number of false positives
DiseasedHealthy
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Specificity
• Theprobability that the test will be negative if the disease is truly absent.
• d/b+d
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Specificity
• Theprobability that the test will be negative if the disease is truly absent.
• d/b+d
• In this situation there is a high likelihood of false negatives.
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High specificity, low sensitivity
Normal
Abnormal
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Sensitivity and specificity
There is usually a trade off between sensitivity and specificity. The more sensitive a test the fewer false negative tests. This is important for a rare and serious diseases such as phenylketonuria. Similarly the more specific a test the fewer false positives that are likely to occur which can be important in common diseases such as diabetes.
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Positive predictive value
The probability of truly having the disease when a screening test is positive.
a/a+b
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Negative predictive value
The probability of being disease free when the screening test is negative.
d/c+d
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Likelihood ratios
For a positive test result = (a/a+c)/(b/b+d)
For a negative test result = (c/a+c)/(d/b+d)
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Accuracy of the test
(a+d)/(a+b+c+d)
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Example
5000 women underwent a test for blood glucose at 24 weeks following a glucose load. 243 women were found to have a blood glucose greater than 6.8 mmol/L and were referred for an OGTT. 186 were found to have gestational diabetes. Four women who initially had tested negative were diagnosed as having diabetes later in their pregnancy.
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Example
Prevalence
Sensitivity
Specificity
Positive predictive value
Negative predictive value
Likelihood ratio + test
Likelihood ratio - test
Accuracy
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Diabetes No diabetes Total
Positive 186 57 243
Negative 4 4753 4757
Total 190 4810 5000
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Example
Prevalence 190/5000
Sensitivity 186/190
Specificity 4753/4810
Positive predictive value 186/243
Negative predictive value 4753/4757
Likelihood ratio + test (186/190)/(57/4810)
Likelihood ratio - test (4/190)/(4753/4810)
Accuracy 186+4753/5000
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Example
Prevalence 3.8%
Sensitivity 97.9%
Specificity 98.8%
Positive predictive value 76.5%
Negative predictive value 99.9%
Likelihood ratio + test 82.6
Likelihood ratio - test .02
Accuracy 98.8%
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Gold standard
Gold standard in diabetes is the OGTT. Other tests may have a gold standard that is too expensive or invasive for routine use e.g. fluoroscein angiography for diabetic retinopathy.
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Gold standard
.
.
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Gold standard• The gold standard is the test or battery of tests that will
most accurately diagnose a particular disease or condition.
• Thus traditionally the OGTT has been seen as the gold standard when testing for diabetes. Other diagnostic tests may have a gold standard that is too expensive or invasive for routine use e.g. fluoroscein angiography for diabetic retinopathy.
• Sometimes the gold standard is a battery of tests or symptoms e.g. the Jones criteria for rheumatic fever
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Receiver operator curves
By plotting the sensitivity and specificity of a test for different cut off points a ROC can be produced which helps illustrate the optimum cut off point to use.
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Receiver operator curves
0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1.00
0.2
0.4
0.6
0.8
1
False positive rate
True positive rate
>280
>80>40
ROC for creatinine kinase for diagnosing MI
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Receiver operator curves
Different curves may be found for different populations or for different prevalence.
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Observer variation
Intra observer variation
Inter observer variation
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Percent agreement.
KAPPA
Estimating observer variation
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Percent agreement
Abnormal Suspect Normal
Abnormal A B C
Suspect D E F
Normal G H I
Percent agreement = (A+E+I) / Total X100
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Pathologist's diagnosis of melanoma
37 cases of melanoma submitted by a panel of melanoma experts of cases they considered definite cases.Reviewed by two pathologistsOne considered 21 cases malignant and 16 benign, the other considered 10 malignant, one indeterminate and 26 benign
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Percent agreement
Melanoma Indeterminate Benign
Melanoma 10 1 10
Indeterminate 0 0 0
Benign 0 0 16
Percent agreement = (10+0+16)/37 X100 = 70 %
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KAPPA
Second Exam
Normal Retinopathy Total
First Normal 46 10 56
Exam Retinopathy 12 32 44
Total 58 42 100
Observed agreement = 46 + 32/100 = 78%
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KAPPA
Second Exam
Normal Retinopathy Total
First Normal 58%x56 42%x56 56
Exam Retinopathy 58%x44 42%x44 44
Total 58 42 100
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KAPPA
Second Exam
Normal Retinopathy Total
First Normal 32.5 23.5
Exam Retinopathy 25.5 18.5
Total
Agreement expected by chance=32.5+18.5/100=51%
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KAPPA = % obseved agreement - % expected by chance
Estimating observer variation
100% - (percent agreement expected by chance)
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KAPPA = 78 - 51/49 = 0.55
Kappa can be between 0 and 1
Usually a score above 0.4 indicates a reasonable level of agreement and above 0.6 is good.
Estimating observer variation
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KAPPA
Organ Agreement Kappa
Liver necrosis 47% 0.2
Rectal cancer grading
50-69% 0.1-0.5
Hodgkins classification
56% 0.4
Breast cancer classification
73% 0.4
Bandolier 37