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Ann Thorac Surg. 2005 Dec;80(6):2106-13.

Massachusetts cardiac surgery report card: implications of statistical methodology.

Author information

  • 1Department of Surgery, Caritas St. Elizabeth's Medical Center, Boston, Massachusetts 02135, USA. david.shahian@caritaschristi.org

Abstract

BACKGROUND:

Choice of statistical methodology may significantly impact the results of provider profiling, including cardiac surgery report cards. Because of sample size and clustering issues, logistic regression may overestimate systematic interprovider variability, leading to false outlier classification. Theoretically, the use of hierarchical models should result in more accurate representation of provider performance.

METHODS:

Extensively validated and audited data were available for all 4,603 isolated coronary artery bypass grafting procedures performed at 13 Massachusetts hospitals during 2002. To produce the official Massachusetts cardiac surgery report card, a 19-variable predictor set and a hierarchical generalized linear model were employed. For the current study, this same analysis was repeated with the 14 predictors used in the New York Cardiac Surgery Reporting System. Two additional analyses were conducted using each set of predictor variables and applying standard logistic regression. For each of the four combinations of predictors and models, the point estimates of risk-adjusted 30-day mortality, 95% confidence or probability intervals, and outlier status were determined for each hospital.

RESULTS:

Overall unadjusted mortality for coronary bypass operations was 2.19%. For most hospitals, there was wide variability in the point estimates and confidence or probability intervals of risk-adjusted mortality depending on statistical model, but little variability relative to the choice of predictors. There were no hospital outliers using hierarchical models, but there was one outlier using logistic regression with either predictor set.

CONCLUSIONS:

When used to compare provider performance, logistic regression increases the possibility of false outlier classification. The use of hierarchical models is recommended.

PMID:
16305853
[PubMed - indexed for MEDLINE]
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