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Comparative Study
Journal Article
Research Support, Non-U.S. Gov't
Differences Among Cardiologists in Rates of Positive Coronary Angiograms.
Journal of the American Heart Association 2015 October 17
BACKGROUND: Understanding the sources of variation for high-cost services has the potential to improve both patient outcomes and value in health care delivery. Nationally, the overall diagnostic yield of coronary angiography is relatively low, suggesting overutilization. Understanding how individual cardiologists request catheterization may suggest opportunities for improving quality and value. We aimed to assess and explain variation in positive angiograms among referring cardiologists.
METHODS AND RESULTS: We identified all cases of diagnostic coronary angiography at Massachusetts General Hospital from January 1, 2012, to June 30, 2013. We excluded angiograms for acute coronary syndrome. For each angiogram, we identified clinical features of the patients and characteristics of the requesting cardiologists. We also identified angiogram positivity, defined as at least 1 epicardial coronary stenosis ≥50% luminal narrowing. We then constructed a series of mixed-effects logistic regression models to analyze predictors of positive coronary angiograms. We assessed variation by physician in the models with median odds ratios. Over this time period, 5015 angiograms were identified. We excluded angiograms ordered by cardiologists requesting <10 angiograms. Among the remaining 2925 angiograms, 1450 (49.6%) were positive. Significant predictors of positive angiograms included age, male patients, and peripheral arterial disease. After adjustment for clinical variables only, the median odds ratio was 1.23 (95% CI 1.0-1.36), consistent with only borderline clinical variation after adjustment. In the full clinical and nonclinical model, the median odds ratio was 1.07 (95% CI 1.07-1.20), also consistent with clinically insignificant variation.
CONCLUSIONS: Substantial variation exists among requesting cardiologists with respect to positive and negative coronary angiograms. After adjustment for clinical variables, there was only borderline clinically significant variation. These results emphasize the importance of risk adjustment in reporting related to quality and value.
METHODS AND RESULTS: We identified all cases of diagnostic coronary angiography at Massachusetts General Hospital from January 1, 2012, to June 30, 2013. We excluded angiograms for acute coronary syndrome. For each angiogram, we identified clinical features of the patients and characteristics of the requesting cardiologists. We also identified angiogram positivity, defined as at least 1 epicardial coronary stenosis ≥50% luminal narrowing. We then constructed a series of mixed-effects logistic regression models to analyze predictors of positive coronary angiograms. We assessed variation by physician in the models with median odds ratios. Over this time period, 5015 angiograms were identified. We excluded angiograms ordered by cardiologists requesting <10 angiograms. Among the remaining 2925 angiograms, 1450 (49.6%) were positive. Significant predictors of positive angiograms included age, male patients, and peripheral arterial disease. After adjustment for clinical variables only, the median odds ratio was 1.23 (95% CI 1.0-1.36), consistent with only borderline clinical variation after adjustment. In the full clinical and nonclinical model, the median odds ratio was 1.07 (95% CI 1.07-1.20), also consistent with clinically insignificant variation.
CONCLUSIONS: Substantial variation exists among requesting cardiologists with respect to positive and negative coronary angiograms. After adjustment for clinical variables, there was only borderline clinically significant variation. These results emphasize the importance of risk adjustment in reporting related to quality and value.
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