Statistical issues in the analysis and interpretation of outcomes for congenital cardiac surgery.
It is universally agreed that efforts to improve quality benefit from the analysis of outcomes. Yet, it is challenging to compare results across institutions because factors other than performance also impact outcomes. Two factors that complicate the analysis of outcomes after congenital cardiac surgery are case-mix and random statistical variation. Case-mix refers to differences in the mix of patients and their risk-factors at different institutions that may cause some centres to have more frequent complications and lower survival regardless of their true performance. Random statistical variation refers to fluctuations in outcomes that occur at random and follow the laws of probability. A variety of statistical methods exist to address these issues and make provider comparisons more fair. We explain a few common approaches including stratification, regression analysis, and confidence intervals. Concepts are illustrated using artificial data from two hypothetical hospitals, as well as real data from a multi-institution registry.
O'Brien
Sean M
SM
Department of Biostatistics and Bioinformatics and Duke Clinical Research Institute, Duke University Medical Center, Durham, North Carolina 27715, USA. obrie027@mc.duke.edu
Gauvreau
Kimberlee
K
D006348:Cardiac Surgical Procedures / Q000706:statistics & numerical data*; D002648:Child; D003627:Data Interpretation, Statistical; D006330:Heart Defects, Congenital / Q000601:surgery*; D006801:Humans; D011785:Quality Assurance, Health Care / Q000706:statistics & numerical data*; D014481:United States