1. Measuring prognosis Patients want to know likely outcome
Baseline risk for treatment decisions
Best way: clinical prediction guide
Measure large number of variables, outcome
Construct regression model
Test in varied clinical settings
RCT to see if it changes practice

3. Prognostic power Discrimination versus calibration Discrimination
Area under the ROC curve
Time-to-event: c-statistic
0.5 to 1.0
Less than 0.6 not useful
0.6 to 0.75 possibly useful
> 0.75 clearly useful

5. EuroScore II predicts
mortality after cardiac surgery
C-statistic 0.80
Overestimates mortality
in higher risk patients
Result could be patients
Inappropriately declining
surgery.

6. Calibration versus discrimination
1 yr survival after heart transplant 85%
Survival less than 85% required for transplant
Seattle heart failure
score c-statistics 0.63
Overestimates
survival in higher risk
Inappropriate
declining transplant

7. Seattle heart failure in another cohort

8. Comparison of two models
Compare how models classify patients
Cases: higher predicted risk better
Controls: lower predicted risk better
Net reclassification
So far % reclassified in cases + % in controls
Range - 200% to + 200%
Better would be % of total better reclassified
Range – 100% to + 100%

9. Overall 730/11,000 or 6.7%

10. Overall -170/11,000 or -1.3%

11. Should we add CRP to Framingham?
Predicting CV risk
Framingham c-statistic 62%
Add c-reactive protein 66%

12. Adding percentages NRI 8.7%
Overall NRI 0.3%

13. Do authors provide information about both the discrimination and calibration of the model? If not, skepticism is warranted.
What is the discriminatory capacity of the model? Does the model adequately discriminate between patients at varying risk?
How well calibrated is the model? Does a visual representation suggest a consistent good fit between predicted and observed outcomes in patients at different risk categories?
When comparing two models, does one of the models re-classify patients more accurately?