1. 2012 SCR Conference Call September 12, 2012 How to read the SAR -Statistical Part Vivian Chunyuan Fei, MD, PhD, MS Continuous Quality Improvement American College of Surgeons 1

2. 2 Outline Logistic Regression Model Hierarchical Regression Model Caterpillar plots Q & A

3. 3 Outcomes: binary (mortality, morbidity, SSI…) Risk-adjustment Demographics: age, sex, race/ethnicity Preoperative morbidities: ventilator dependent, sepsis, cardiac risk factors, etc. Operative factors: surgery (linear risk), emergency, wound class Variable selection: forward Logistic Regression Model

4. 4 O/E

5. 5 Interpretation of O/E O/E<1O/E=1O/E>1 The hospital is doing better than expected The hospital is doing as expected The hospital is doing worse than expected

6. 6 Hierarchical Regression Model Why shall we use this model? Data have clustered structures Shrinkage adjustment What is Hierarchical regression model? Patient-level predictors Hospital, treated as a random variable within which patients are clustered.

7. 7 Odds

8. 8 Odds Ratio

9. 9

10. 10 Interpretation of OR OR<1OR=1OR>1 The hospital is doing better than average The hospital is doing as average The hospital is doing worse than average In the SAR, we presented adjusted odds ratios. The raw numbers are NOT used to compute your Hospital Odds Ratio.

11. 11 Caterpillar plot A side-by-side bar plot of 95% intervals for multiple parameters We use caterpillar plots to visualize and compare hospital profiling

12. 12 Example 1: Total morbidity in all patients Point estimate 95% CI (upper) 95% CI (lower) Low outlier: upper confidence limit<1; High outlier: upper confidence limit<1 Doing better Doing worse

13. 13 Example 2: Mortality in all patients No low outlier, no high outlier

14. 14 Example 3: SSI in all patients

15. 15 Example 4: Mortality in abdominal surgery in non-neonates From logistic regression

16. 16 For more information, please contact Vivian Fei Email: vfei@facs.orgvfei@facs.org Phone: 301-202-5530