Early prediction of 30-day mortality in patients with surgical wound infections following cardiothoracic surgery: Development and validation of the SWICS-30 score utilizing conventional logistic regression and artificial neural network

From 1713 patients evaluated (mean age of 60 years (18-89), 55 % female), 143 (8.4 %) experienced 30-day mortality. The SWICS-30 logistic regression score comprised the following variables: age over 65 years, undergoing valve heart surgery, combined coronary and valve heart surgery, heart transplantation, time from surgery to infection diagnosis exceeding 21 days, leukocyte count over 13,000/mm3, lymphocyte count below 1000/mm3, platelet count below 150,000/mm3, and creatinine level exceeding 1.5 mg/dL. These patients were stratified into low (2.7 %), moderate (14.2 %), and high (47.1 %) in-hospital mortality risk categories. Artificial intelligence confirmed accuracy at 90 %.