Predictive models of surgical site infections after coronary surgery: insights from a validation study on 7090 consecutive patients

Background: The role of specific scoring systems in predicting risk of surgical site infections (SSIs) after coronary artery bypass grafting (CABG) has not been established. Aim: To validate the most relevant predictive systems for SSIs after CABG. Methods: Five predictive systems (eight models) for SSIs after CABG were evaluated retrospectively in 7090 consecutive patients undergoing isolated (73.9%) or combined (26.1%) CABG. For each model, accuracy of prediction, calibration, and predictive power were assessed with area under receiver-operating characteristic curve (aROC), the Hosmer-Lemeshow test, and the Goodman-Kruskal gamma-coefficient, respectively. Six predictive scoring systems for 30-day in-hospital mortality after cardiac operations were evaluated as to prediction of SSIs. The models were compared one-to-one using the Hanley-McNeil method. Findings: There were 724 (10.2%) SSIs. Whereas all models showed satisfactory calibration (P = 0.176-0.656), accuracy of prediction was low (aROC: 0.609-0.650). Predictive power was moderate (gamma: 0.315-0.386) for every model but one (gamma: 0.272). When compared one-to-one, the Northern New England Cardiovascular Disease Study Group mediastinitis score had a higher discriminatory power both in overall series (aROC: 0.634) and combined CABG patients (aROC: 0.648); in isolated CABG patients, both models of the Fowler score showed a higher discriminatory power (aROC: 0.651 and 0.660). Accuracy of prediction for SSIs was low (aROC: 0.564-0.636) even for six scoring systems devised to predict mortality after cardiac surgery. Conclusion: In this validation study, current predictive models for SSIs after CABG showed low accuracy of prediction despite satisfactory calibration and moderate predictive power. (C) 2019 The Healthcare Infection Society. Published by Elsevier Ltd. All rights reserved.