Alexander A Brescia1, J Scott Rankin2, Derek D Cyr3, Jeffrey P Jacobs4, Richard L Prager5, Min Zhang6, Roland A Matsouaka3, Steven D Harrington7, Rachel S Dokholyan3, Steven F Bolling1, Astrid Fishstrom1, Sara K Pasquali8, David M Shahian9, Donald S Likosky10. 1. Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan. 2. West Virginia Heart and Vascular Institute, West Virginia University, Morgantown, West Virginia. 3. Duke Clinical Research Institute, Duke University, Durham, North Carolina. 4. Division of Cardiac Surgery, Department of Surgery, Johns Hopkins University School of Medicine, Baltimore, Maryland. 5. Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan; Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative. 6. Department of Biostatistics, University of Michigan, Ann Arbor, Michigan. 7. Heart and Vascular Institute, Henry Ford Macomb Hospitals, Clinton Township, Michigan. 8. Department of Pediatrics, University of Michigan, Ann Arbor, Michigan. 9. Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts. 10. Department of Cardiac Surgery, University of Michigan, Ann Arbor, Michigan; Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative. Electronic address: likosky@med.umich.edu.
Abstract
BACKGROUND: Although conventional wisdom suggests that differences in patient risk profiles drive variability in postoperative pneumonia rates after coronary artery bypass grafting (CABG), this teaching has yet to be empirically tested. We determined to what extent patient risk factors account for hospital variation in pneumonia rates. METHODS: We studied 324,085 patients undergoing CABG between July 1, 2011, and December 31, 2013, across 998 hospitals using The Society of Thoracic Surgeons Adult Cardiac Database. We developed 5 models to estimate our incremental ability to explain hospital variation in pneumonia rates. Model 1 contained patient demographic characteristics and admission status, while Model 2 added patient risk factors. Model 3 added measures of pulmonary function, Model 4 added measures of cardiac anatomy and function and medications, and Model 5 further added measures of intraoperative and postoperative care. RESULTS: Although 9,175 patients (2.83%) experienced pneumonia, the median estimated distribution of pneumonia rates across hospitals was 2.5% (25th to 75th percentile: 1.5% to 4.0%). Wide variability in pneumonia rates was evident, with some hospitals having rates more than 6 times higher than others (10th to 90th percentile: 1.0% to 6.1%). Among all five models, Model 2 accounted for the most variability at 4.24%. In total, 2.05% of hospital variation in pneumonia rates was explained collectively by traditional patient factors, leaving 97.95% of variation unexplained. CONCLUSIONS: Our findings suggest that patient risk profiles only account for a fraction of hospital variation in pneumonia rates; enhanced understanding of other contributory factors (eg, processes of care) is required to lessen the likelihood of such nosocomial infections.
BACKGROUND: Although conventional wisdom suggests that differences in patient risk profiles drive variability in postoperative pneumonia rates after coronary artery bypass grafting (CABG), this teaching has yet to be empirically tested. We determined to what extent patient risk factors account for hospital variation in pneumonia rates. METHODS: We studied 324,085 patients undergoing CABG between July 1, 2011, and December 31, 2013, across 998 hospitals using The Society of Thoracic Surgeons Adult Cardiac Database. We developed 5 models to estimate our incremental ability to explain hospital variation in pneumonia rates. Model 1 contained patient demographic characteristics and admission status, while Model 2 added patient risk factors. Model 3 added measures of pulmonary function, Model 4 added measures of cardiac anatomy and function and medications, and Model 5 further added measures of intraoperative and postoperative care. RESULTS: Although 9,175 patients (2.83%) experienced pneumonia, the median estimated distribution of pneumonia rates across hospitals was 2.5% (25th to 75th percentile: 1.5% to 4.0%). Wide variability in pneumonia rates was evident, with some hospitals having rates more than 6 times higher than others (10th to 90th percentile: 1.0% to 6.1%). Among all five models, Model 2 accounted for the most variability at 4.24%. In total, 2.05% of hospital variation in pneumonia rates was explained collectively by traditional patient factors, leaving 97.95% of variation unexplained. CONCLUSIONS: Our findings suggest that patient risk profiles only account for a fraction of hospital variation in pneumonia rates; enhanced understanding of other contributory factors (eg, processes of care) is required to lessen the likelihood of such nosocomial infections.
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