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Ann Thorac Surg 2007;83:S13-S26
© 2007 The Society of Thoracic Surgeons
,*
a Duke Clinical Research Institute, Durham, North Carolina
b Tufts University School of Medicine, Boston, Massachusetts
c Department of Health Care Policy, Harvard Medical School, and cDepartment of Biostatistics, Harvard School of Public Health, Boston, Massachusetts
d Division of Cardiothoracic Surgery, University of Florida, Jacksonville, Florida
e Division of Cardiovascular & Thoracic Surgery, University of Kentucky Chandler Medical Center, Lexington, Kentucky
f Sentara Cardiovascular Research Institute, Norfolk, Virginia
g The Society of Thoracic Surgeons, Chicago, Illinois
h The Society of Thoracic Surgeons, Seattle, Washington
Accepted for publication January 12, 2007.
* Address correspondence to Dr Shahian, The Society of Thoracic Surgeons, 633 N Saint Clair St, Suite 2320, Chicago, IL 60611 (Email: shahian@comcast.net).
| The first 300 words of the full text of this article appear below. |
| Executive Summary |
|---|
The QMTF evaluated various options for combining 11 National Quality Forum (NQF)endorsed process and outcome measures, both within and across the four domains of care chosen by the Task Force (Perioperative Medical Care, Operative Care, Risk-Adjusted Operative Mortality, and Postoperative Risk-Adjusted Major Morbidity). These methods included simple or weighted averaging, a composite opportunity model similar to that used by the Centers for Medicare & Medicaid Services (CMS), "all or none" scoring, scaled combinations, and latent variable models. Each method was illustrated using actual 2004 STS data from 133,149 coronary artery bypass procedures. Provider performance was estimated using Bayesian random-effects approaches to account for small sample size and to incorporate risk adjustment for outcomes.
Latent variable modeling failed to provide accurate estimates of provider performance when tested with actual STS data. Most other methods of combining individual measures within a given domain produced similar and consistent estimates of performance (Spearman rank correlations 0.95 to 0.98), and an all or none approach was selected.
Combining scores across domains was accomplished by rescaling and then adding the domain-specific estimates. When this methodology is applied to actual STS data, a one percentage point improvement in mortality has the same impact on the overall composite score as does an 8% improvement in the morbidity rate, an 11% improvement in the frequency of internal mammary artery usage, or a 28% change in the frequency of using all four NQF-recommended medications.
The QMTF
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