Background: Deep sternal wound infection (DSWI) following open heart surgery is associated with excessive morbidity and mortality. Contemporary DSWI risk prediction models aim at identifying high-risk patients with varying complexity and performance characteristics. We aimed to optimize the DSWI risk factor set and to identify additional risk factors for early postoperative detection of patients prone to DSWI. Methods: Single-centre retrospective analysis of patients with isolated multivessel coronary artery disease undergoing myocardial revascularization at Paracelsus Medical University Nuremberg between 2007 and 2022 was performed to identify risk factors for DSWI. Three data sets were created to examine preoperative, intraoperative, and early postoperative parameters, constituting the “Baseline”, the “Improved Baseline” and the “Extended” models. The “Extended” data set included risk factors that had not been analysed before. Univariable and stepwise forward multiple logistic regression analyses were performed for each respective set of variables. Results: From 5221 patients, 179 (3.4%) developed DSWI. The “Extended” model performed best, with the area under the curve (AUC) of 0.80, 95%-CI: [0.76, 0.83]. Pleural effusion requiring intervention, postoperative delirium, preoperative hospital stay > 24 h, and the use of fibrin sealant were new independent predictors of DSWI in addition to age, Diabetes Mellitus on insulin, Body Mass Index, peripheral artery disease, mediastinal re-exploration, bilateral internal mammary harvesting, acute kidney injury and blood transfusions. Conclusions: The “Extended” regression model with the short-term postoperative complications significantly improved DSWI risk discrimination after surgical revascularization. Short preoperative stay, prevention of postoperative delirium, protocols reducing the need for evacuation of effusion and restrictive use of fibrin sealant for sternal closure facilitate DSWI reduction. Trial registration: The registered retrospective study was registered at the study centre and approved by the Institutional Review Board of Paracelsus Medical University Nuremberg (IRB-2019-005).
CITATION STYLE
Kamensek, T., Kalisnik, J. M., Ledwon, M., Santarpino, G., Fittkau, M., Vogt, F. A., & Zibert, J. (2024). Improved early risk stratification of deep sternal wound infection risk after coronary artery bypass grafting. Journal of Cardiothoracic Surgery, 19(1). https://doi.org/10.1186/s13019-024-02570-9
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