1. Sensitivity:DISEASE Specificity:PRESENTABSENT TEST RESULTS POSITIVEAbA+b NEGATIVECdC+d a+cb+da+b+c+d Lets recall the 2x2 contingency table Probability graph

2. Sensitivity: 65%DISEASE Specificity: 98%PRESENTABSENT TEST RESULTS POSITIVEAbA+b NEGATIVECdC+d a+cb+da+b+c+d= 1000 Solving for Anti CCP Probability graph Lets say there are a 1000 patients of HIV with suspected RA Hence a+b+c+d=1000

3. Sensitivity: 65%DISEASE Specificity: 98%PRESENTABSENT TEST RESULTS POSITIVEAbA+b NEGATIVECdC+d a+c= 200 b+d = 800 a+b+c+d= 1000 20% Solving for Anti CCP Probability graph Pretest probability of RA is very low, say 20% So number of patients likely to have RA clinically Is a+c =20% of 1000=200 Marked by this arrow

4. Sensitivity: 65%DISEASE Specificity: 98%PRESENTABSENT TEST RESULTS POSITIVEA=130bA+b NEGATIVEC=70dC+d a+c= 200 b+d = 800 a+b+c+d= 1000 20% Solving for Anti CCP Probability graph Sensivity of the test is number of people with disease who test positive for the test i.E a/(a+c) Since a+c=200 and sensitivity is 65% A= 130 and b= 70

5. Sensitivity: 65%DISEASE Specificity: 98%PRESENTABSENT TEST RESULTS POSITIVEA=130B= 16A+b NEGATIVEC=70D= 784C+d a+c= 200 b+d = 800 a+b+c+d= 1000 20% Solving for Anti CCP Probability graph Specificity of the test is number of people without disease who test negative for the test i.E d/(b+d) Since b+d=800 and specificity is 98% b= 16 and d= 784

6. Sensitivity: 65%DISEASE Specificity: 98%PRESENTABSENTTotal TEST RESULTS POSITIVEA=130B= 16A+b=146 NEGATIVEC=70D= 784C+d a+c= 200 b+d = 800 a+b+c+d= 1000 89% 20% Solving for Anti CCP Probability graph If test is positive, the post test probability of the disease is given by Positive predictive value Which is A/(A+B)= 130/(130+16)= 130/146= 89% I have shown it in probability graph by green arrow

7. Sensitivity: 65%DISEASE Specificity: 98%PRESENTABSENTTotal TEST RESULTS POSITIVEA=130B= 16A+b=146 NEGATIVEC=70D= 784C+d=854 a+c= 200 b+d = 800 a+b+c+d= 1000 89% 20% 8% Solving for Anti CCP Probability graph If test is negative, the post test probability of the disease is given by False negative rate Which is c/(c+d)= 70/(70_784)= 70/854= 8% I have shown it in probability graph by red arrow

8. Sensitivity: 65%DISEASE Specificity: 98%PRESENTABSENTTotal TEST RESULTS POSITIVEA=130B= 16A+b=146 NEGATIVEC=70D= 784C+d=854 a+c= 200 b+d = 800 a+b+c+d= 1000 89% 20% 8% Solving for Anti CCP Probability graph Appreciate the movement of the arrow in a test with HIGH specificity Its moved from 20% to 89% when test is positive= 69% movement It moves from 20% to 8% when the test is negative= 12% movement This is what I mentioned as SP-P-IN in my earlier discussion Specific test when positive rules in the disease

9. Lets do it for others

10. Sensitivity: 50%DISEASE Specificity: 99%PRESENTABSENTTotal TEST RESULTS POSITIVEA=100B= 8108 NEGATIVEC=100D= 792892 a+c= 200 b+d = 800 a+b+c+d= 1000 92% 20% 11% Try it for anti-Sa Probability graph Pretest probability is 20% The graph is almost same as for anti CCP Notice that the graph will be almost same for specificity more than 95%

11. Sensitivity: 70%DISEASE Specificity: 97%PRESENTABSENTTotal TEST RESULTS POSITIVEA=140B= 24108 NEGATIVEC=60D= 776836 a+c= 200 b+d = 800 a+b+c+d= 1000 85% 20% 7% Try it for anti-MCV Probability graph This graph is almost same as for anti CCP Notice that the graph will be almost same for any specificity more than 95%

12. Lets do it for ANA ANA has a very high sensitivity And very poor specificity Esp in titres of 1:40

13. Sensitivity: 97%DISEASE Specificity: 70%PRESENTABSENTTotal TEST RESULTS POSITIVEA=194B= 240434 NEGATIVEC=6D= 560566 a+c= 200 b+d = 800 a+b+c+d= 1000 45% 20% 1 % ANA in 1:40 titres for low probability Probability graph In a low pre test probability situation=20% the movements are modest despite high sensitivity

14. Sensitivity: 97%DISEASE Specificity: 70%PRESENTABSENTTotal TEST RESULTS POSITIVEA=776B= 60836 NEGATIVEC=24D=140164 a+c= 800 b+d = 200 a+b+c+d= 1000 92.8% 80% 15 % ANA in 1:40 titres for high probability Probability graph Lets take a scenario where pretest probability is high, say 80% Note the movement when the test is negative (from 80->> 15%=65%) A positive test just increases our certainity by 12.8% Recall SN-N-OUT Sensitive tests when negative rule out disease

15. Sensitivity: 97%DISEASE Specificity: 70%PRESENTABSENTTotal TEST RESULTS POSITIVEA=485B= 150635 NEGATIVEC=15D=350365 a+c= 500 b+d = 500 a+b+c+d= 1000 76% 50% 4% ANA in 1:40 titres for intermediate probability Probability graph Lets take a scenario where pretest probability is intermediate=50% Note the movement when the test is negative (from 50->> 4%=46%) A positive test just increases our certainity by 26% Recall SN-N-OUT Sensitive tests when negative rule out disease