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Published on: January 7, 2019
Center-level variation in infection rates after coronary artery bypass grafting
Terry Shih1, Min Zhang1, Mallika Kommareddi1
1From the Departments of Surgery (T.S.), Biostatistics (M.Z.), and Cardiac Surgery (M.K., R.L.P., D.S.L.), University of Michigan, Ann Arbor; Department of Cardiac Surgery, Meijer Heart and Vascular Institute at Spectrum Health, Grand Rapids, MI (T.J.B.); Heart and Vascular Institute, Henry Ford Macomb Hospitals, Clinton Township, MI (S.D.H.); Department of Cardiac Surgery, McLaren Bay Region, Bay City, MI (R.J.H.); Department of Cardiothoracic and Vascular Surgery, McLaren Greater Lansing, MI (G.R.); and Michigan Society of Thoracic and Cardiovascular Surgeons Quality Collaborative, Ann Arbor (P.F.T., R.L.P., D.S.L.).
Insights
Health-care-acquired infections (HAIs) after cardiac surgery varied significantly between hospitals. Patient case mix did not explain these differences, suggesting other factors influence infection risk.
Area of Science:
- Medical research
- Infectious disease epidemiology
- Surgical outcomes
Background:
- Health-care-acquired infections (HAIs) are a major cause of death and complications following cardiac surgery.
- Previous studies identified patient-specific risk factors for HAIs.
- This study investigated institutional differences in HAI rates, considering patient case mix.
Purpose of the Study:
- To determine if health-care-acquired infection (HAI) rates differ across medical centers performing coronary artery bypass grafting (CABG).
- To assess the extent to which patient case mix explains observed variations in HAI rates.
- To identify potential areas for future research into HAI prevention strategies.
Main Methods:
- Analysis of 20,896 patients undergoing isolated CABG surgery across 33 Michigan hospitals (2009-2012).
- Inclusion of HAIs such as pneumonia, sepsis, and surgical site infections; exclusion of endocarditis cases.
- Estimation of predicted HAI rates using multivariable logistic regression.
Main Results:
- The overall observed HAI rate was 5.1%, with significant variation across institutions (0.9% to 19.1%).
- Predicted HAI risk varied by 2.8% across centers, while observed rates varied by 18.2%.
- HAI subtypes showed differences across centers with varying HAI rates.
Conclusions:
- A substantial 18.2% difference in observed HAI rates exists among hospitals for CABG patients.
- Patient case mix did not account for the observed variability in HAI rates.
- Further investigation into organizational and clinical care systems is needed to understand HAI risk.
Background:
Health-care-acquired infections (HAIs) are a leading cause of morbidity and mortality after cardiac surgery. Prior work has identified several patient-related risk factors associated with HAIs. We hypothesized that rates of HAIs would differ across institutions, in part attributed to differences in case mix.
Methods And Results:
We analyzed 20 896 patients undergoing isolated coronary artery bypass grafting surgery at 33 medical centers in Michigan between January 1, 2009, and June 30, 2012. Overall HAIs included pneumonia, sepsis/septicemia, and surgical site infections, including deep sternal wound, thoracotomy, and harvest/cannulation site infections. We excluded patients presenting with endocarditis. Predicted rates of HAIs were estimated using multivariable logistic regression. Overall rate of HAI was 5.1% (1071 of 20 896; isolated pneumonia, 3.1% [n=644]; isolated sepsis/septicemia, 0.5% [n=99]; isolated deep sternal wound infection, 0.5% [n=96]; isolated harvest/cannulation site, 0.5% [n=97]; isolated thoracotomy, 0.02% [n=5]; multiple infections, 0.6% [n=130]). HAI subtypes differed across strata of center-level HAI rates. Although predicted risk of HAI differed in absolute terms by 2.8% across centers (3.9-6.7%; min:max), observed rates varied by 18.2% (0.9-19.1%).
Conclusions:
There was a 18.2% difference in observed HAI rates across medical centers among patients undergoing isolated coronary artery bypass grafting surgery. This variability could not be explained by patient case mix. Future work should focus on the impact of other factors (eg, organizational and systems of clinical care) on risk of HAIs.
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