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

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For more information, please contact Vivian FeiEmail: vfei@facs.orgPhone: 301-202-5530