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Ann Thorac Surg 2001;72:1845-1848
© 2001 The Society of Thoracic Surgeons
a Providence Health System, Portland, Oregon, USA
* Address reprint requests to Dr Grunkemeier, 9155 SW Barnes Rd, #33, Portland, OR 97225, USA
e-mail: ggrunkemeier@providence.org
For several years, data-driven methodologies have been used in an attempt to improve performance in cardiac surgery programs. The article by Shahian and his colleagues [1] in this issue of The Annals provides a thoughtful and thorough comparison of "Report Cards" and continuous quality improvement (CQI) initiatives. Their conclusions are that CQI, including multi-disciplinary team site visits to identify and share processes and systems, has been proven to be effective. In contrast, "Report Cards" as currently implemented are not satisfactory and have potential to do harm. Their arguments are cogent and comprehensive. They claim that these cardiac surgery report cards are based on "sophisticated mathematical models" which engender "an exaggerated aura of scientific accuracy". Examining some of the deficiencies and limitations of the risk models used for cardiac surgery, from which the report card "Grades" are derived, provides support for their claim.
Curse of a binary outcome
The mathematical models in question are constructed using multivariable regression, which yields a formula which uses the risk factors for an individual patient to provide an estimate (expected value) of his outcome. Building such a model is not a fixed, reproducible exercise, and there are at least 9 reasons why different investigators with the same data set would produce different risk models [2]. For a continuous outcome (cost, length of stay, etc) such a model can exactly predict or at least come close to a patient’s observed value. But operative mortality is a binary outcome, and an ideal formula would result in a classification of alive or dead. Instead, logistic regression provides the expected mortality, the probability that the patient will be an operative death.
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