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(Circulation. 2005;112:I-358 – I-365.)
© 2005 American Heart Association, Inc.

Surgery for Coronary Artery Disease

Clinical Predictors of Major Infections After Cardiac Surgery

Vance G. Fowler, Jr, MD, MHS; Sean M. O’Brien, PhD; Lawrence H. Muhlbaier, PhD; G. Ralph Corey, MD; T. Bruce Ferguson, MD; Eric D. Peterson, MD, MPH

From the Departments of Medicine (V.G.F., G.R.C., E.D.P.) and Biostatistics (S.M.O., L.H.M.) and Duke Clinical Research Institute (V.G.F., S.M.O., L.H.M., G.R.C.), Duke University Medical Center, Durham, NC; and Louisiana State University Health Sciences Center (T.B.F.), New Orleans, La.

Correspondence to Vance G. Fowler, Jr, MD, MHS Box 3281, Duke University Medical Center, Durham, NC 27710. E-mail fowle003{at}mc.duke.edu

Background— Major infections are infrequent but important complications of cardiac surgery. Predicting their occurrence is essential for future prevention. The objective of the current investigation was to create and validate a bedside scoring system to estimate patient risk for major infection (mediastinitis, thoracotomy or vein harvest site infection, or septicemia) after coronary artery bypass grafting.

Methods and Results— Using the Society of Thoracic Surgeons National Cardiac Database, we analyzed 331 429 coronary artery bypass grafting cases from January 1, 2002, to December 31, 2003, to identify risk factors for major infection. Using logistic regression, 2 models were generated and validated using split-sample validation: (1) One limited to preoperative characteristics (preop model) and (2) one model including both preoperative and intraoperative characteristics (combined model). Major infection occurred in 11 636 patients (3.51%) (25.1% mediastinitis, 32.6% saphenous harvest site, 35.0% septicemia, 0.5% thoracotomy, 6.8% multiple sites). Patients with major infection had significantly higher mortality (17.3% versus 3.0%, P<0.0001) and postoperative length of stay >14 days (47.0% versus 5.9%, P<0.0001) than patients without major infection. Both the preop model (c-index 0.697) and combined model (c-index: 0.708) successfully discriminated between high- and low-risk patients. A simplified risk scoring system of 12 variables accurately predicted risk for major infection.

Conclusions– We identified and validated a model that can identify patients undergoing cardiac surgery who are at high risk for major infection. These high-risk patients may be targeted for perioperative intervention strategies to reduce rates of major infection.

Key Words: infection • diagnosis • bypass • surgery

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