Tyler York

December 12, 2022

Receiver operating curves or ROC curves are often used to compare diagnostic tests and to predict the accuracy of tests. Area under the curve can be calculated from ROC curves. We will learn how to interpret ROC curves and apply our knowledge to solve a few USMLE style questions on ROC curves.

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0.3s Hello everyone. I'm so jacked up in today's video I would like to discuss about Roc curves. 8.8s Roc curve, ball receiver. Operating curve is a plot with sensitivity or two positive rate on the y-axis and 1 - specificity or the false positive rate on the x-axis. 25.4s Roc curves are used to assess the accuracy of a diagnostic test, and also, to compare between different diagnostic test. In the figure, you can see four different Roc curves, a b, c and d. 41.9s The area under Roc curve, is called sauce can be used to predict the accuracy of a test jendely nauc of point eight. Or more is seen in a good test, a test with an Uzi which is equal to or less than 2.75 is. Not a clinically useful test. 67.0s The maximum value for a ucc-1 indicating. A theoretically perfect test with 100% sensitivity and 100% specificity in the figure. The graph shown by D has an AMC of .5 which means that test has a sensitivity and specificity of 50% which is no better than getting the test results by chance. 93.1s In the figure a test a has the highest possible area under the curve and it is the perfect test with highest possible accuracy. 104.1s Accuracy increases as the curve shifts to the top and left higher, the area. Under the curve. Higher is the accuracy of a test. 115.2s To correctly interpret and Roc curve, we need to know what is meant by sensitivity and specificity. Sensitivity is so true. Positive rate, 126.7s It reflects a test ability to correctly, identify all people who have a condition specificity on. The other hand is the true negative rate 137.8s Specificity reflects a test ability to correctly identify people who do not have that condition. 147.1s Sensitivity rules out. Why is specificity rules in a disease and Roc curve can be used to determine a cut-off value for sensitivity and specificity of a diagnostic are screening tests. 162.7s Choosing a cut-off value for sensitivity and specificity of a test should be guided by clinical document when sensitivity increases specificity decreases and vice versa. 176.5s Screening test should have a high sensitivity, confirmatory test should have a high specificity. 186.0s It is always best to have the highest sensitivity and specificity possible, but such choices may not be always available. 196.7s If the available treatment is not cost effective or has serious adverse effects, then use a test with high specific City. On the other hand, if the disease is rapidly fatal and the Piedmont is cost-effective, then use a test with high sensitivity. 216.7s Let's calculate the sensitivity and specificity of two points on the ROC curves. 223.9s At the black dog, the sensitivity is 100%. While the specificity is 80% and the red. The sensitivity is 80%, and the specificity is only 40%. 238.0s Let's have a few questions. 242.2s A screening test is available to diagnose a rapidly fatal infectious disease. The treatment of choice is cost-effective and has a very low risk of adverse effects. 254.8s Which point on the ROC curve. Be what do you choose as a cutoff point for this test? 263.3s For Roc. Kirby point one has the lowest sensitivity and highest specificity, 1.5 has the highest sensitivity, but the lowest specificity 277.8s For our scenario, we need to test which has a high sensitivity so that we do not mistreating any patients. So for this particular test .5 will be the optimal cut off value for sensitivity and specificity. 292.7s Let's do one of the question. You are evaluating four different test. Used to diagnose colon cancer. 301.4s They already presented by The ROC curves. A b c and d, which test would you choose as a diagnostic test? 312.4s The correct answer is option 8 since he has the highest AUC or area under the curve. So I will have the highest accuracy 324.1s Thanks for watching. I hope this video helps you to answer a few questions on Roc curves on the USMLE step 1. All the best.

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