Analysis and Comparison of Operator-Specific Outcomes in Interventional Cardiology

Abstract

Background Medical consumers are increasingly requesting methods to discriminate among the results of different providers. Standards for appropriate modeling, risk adjustment, and evaluation (“scorecarding”) in this setting are not well developed, although such evaluation is being performed by the medical insurance industry and by several states in the United States. Our objectives were to develop and examine clinically meaningful methodology for assessing the operator-specific results for percutaneous coronary revascularization. Methods and Results From a multicenter database of patients treated since January 1, 1990, we used training and validation samples (n=4860) to develop several models for risk adjustment and applied them to 38 providers performing 25 to 523 procedures in the database. Models were developed using multivariable logistic regression techniques for combinations of the end points of death, myocardial infarction, bypass surgery, and procedural success. Models were evaluated for predictive accuracy by using receiver operating characteristic (ROC) analysis, for the capacity to discriminate between superior and inferior provider outcomes, and for subjectivity and concordance. Major complications occurred in 3.6% of patients. The area under the ROC curve (with perfect discriminatory accuracy, area=1.0; with no apparent accuracy, area=0.5) in the validation sample, and frequency of identification of operators with outcomes outside the 95% CI for the outcome in question for the models were for death, 0.85 and 7.9%; for death, Q-wave infarction, and bypass surgery, 0.77 and 13.2%; for death, all infarction, and bypass surgery, 0.66 and 10.5%; and for procedural success, 0.76 and 23.7%. For the models as a group, identification of outliers was inversely related to provider volume ( P =.05). Models evaluating non–Q-wave infarction or requiring measurement of percent diameter stenosis were identified as being most susceptible to provider manipulation. Conclusions For percutaneous coronary revascularization, modeling to discriminate between provider outcomes is limited by the low incidence of major adverse events, subjectivity or susceptibility to manipulation of more frequently occurring adverse events, the generally modest predictive capacity of the models, and the low volume of individual provider treatments. Modeling will be most useful in the identification of providers with extremely poor outcomes and for discrimination between providers with very large procedural volume. Until improved understanding of the biological and mechanical correlates of major complications allows the development of more predictive models, interpretation of the results of scorecarding, particularly for low-volume providers, should be made with caution.

Volume-Outcome Relation for Physicians and Hospitals Performing Angioplasty for Acute Myocardial Infarction in New York State

“…A n inverse relationship between both physician and hospital PTCA volumes and adverse outcomes has been demonstrated in numerous studies [1][2][3][4][5][6][7][8][9][10][11][12] involving thousands of patients undergoing predominantly elective procedures. It is not known whether a distinct volume-outcome relation exists for PTCA performed as primary reperfusion therapy for acute myocardial infarction (AMI) (primary angioplasty).…”

ACCF/AHA/SCAI 2007 Update of the Clinical Competence Statement on Cardiac Interventional Procedures

“…The author of this study concluded that given these improvements, lower minimum volume standards might be justifiable in less populated areas, where the alternative is no access to angioplasty at all. Importantly, procedural volume is only one of many factors contributing to the variability of measured outcomes (58,82,89). Furthermore, there is no clear "cut-off " above or below which hospitals, or groups of hospitals in aggregate, perform well or poorly.…”

“…There are statistical associations between activity levels and short-term complication rates (emergency CABG and mortality) (17,58,85,89,97,101) for both institutions and for individual operators. In particular, low-volume operators operating at low-volume hospitals had an increased mortality rate.…”

Association of Body Mass Index with In-Hospital Left Ventricular Failure after Percutaneous Coronary Interventions

“…12 A number of studies have shown that lean patients (<20 kg/m 2 ) and those with normal BMI (20-24.9 kg/m 2 ) are at a higher risk for adverse in-hospital outcomes and post-PCI complications than overweight (25-29.9 kg/m 2 ) and obese (≥30 kg/m 2 ) patients. [12][13][14] . This unexpected phenomenon was explained by "obesity paradox".…”