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2012 SCR Conference CallSeptember 12, 2012
How to read the SAR-Statistical Part
Vivian Chunyuan Fei, MD, PhD, MSContinuous Quality Improvement
American College of Surgeons
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Outline
• Logistic Regression Model• Hierarchical Regression Model• Caterpillar plots• Q & A
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• 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
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O/E
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Interpretation of O/E
O/E<1 O/E=1 O/E>1
The hospital is doing better than expected
The hospital is doing as expected
The hospital is doing worse than expected
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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.
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Odds
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Odds Ratio
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Odds Ratio
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Interpretation of OR
OR<1 OR=1 OR>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.
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Caterpillar plot
• A side-by-side bar plot of 95% intervals for
multiple parameters• We use caterpillar plots to visualize and compare
hospital profiling
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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
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Example 2: Mortality in all patients
No low outlier, no high outlier
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Example 3: SSI in all patients
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Example 4: Mortality in abdominal surgery in non-neonates
From logistic regression