Meaningful evaluation of quality of care must account for variations in the population of patients receiving treatment, or “case-mix”. In adult cardiac surgery, empirical clinical data, initially from tens of thousands, and more recently hundreds of thousands of operations, have been used to develop risk-models, to increase the accuracy with which the outcome of a given procedure on a given patient can be predicted, and to compare outcomes on non-identical patient groups between centres, surgeons and eras.
In the adult cardiac database of The Society of Thoracic Surgeons, algorithms for risk-adjustment are based on over 1.5 million patients undergoing isolated coronary artery bypass grafting and over 100,000 patients undergoing isolated replacement of the aortic valve or mitral valve. In the pediatric and congenital cardiac database of The Society of Thoracic Surgeons, 61,014 operations are spread out over greater than 100 types of primary procedures. The problem of evaluating quality of care in the management of pediatric patients with cardiac diseases is very different, and in some ways a great deal more challenging, because of the smaller number of patients and the higher number of types of operations.
In the field of pediatric cardiac surgery, the importance of the quantitation of the complexity of operations centers on the fact that outcomes analysis using raw measurements of mortality, without adjustment for complexity, is inadequate. Case-mix can vary greatly from program to program. Without stratification of complexity, the analysis of outcomes for congenital cardiac surgery will be flawed. Two major multi-institutional efforts have attempted to measure the complexity of pediatric cardiac operations: the Risk Adjustment in Congenital Heart Surgery-1 method and the Aristotle Complexity Score. Both systems were derived in large part from subjective probability, or expert opinion. Both systems are currently in wide use throughout the world and have been shown to correlate reasonably well with outcome.
Efforts are underway to develop the next generation of these systems. The next generation will be based more on objective data, but will continue to utilize subjective probability where objective data is lacking. A goal, going forward, is to re-evaluate and further refine these tools so that, they can be, to a greater extent, derived from empirical data. During this process, ideally, the mortality elements of both the Aristotle Complexity Score and the Risk Adjustment in Congenital Heart Surgery-1 methodology will eventually unify and become one and the same. This review article examines these two systems of stratification of complexity and reviews the rationale for the development of each system, the current use of each system, the plans for future enhancement of each system, and the potential for unification of these two tools.
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