{Reference Type}: Journal Article
{Title}: A nomogram for prediction of early mortality in patients undergoing cardiac surgery for infective endocarditis: a retrospective single-center study.
{Author}: Liu Y;Li X;Liu Z;Lu C;Ge S;
{Journal}: Scand Cardiovasc J
{Volume}: 58
{Issue}: 1
{Year}: 2024 Dec
{Factor}: 1.992
{DOI}: 10.1080/14017431.2024.2373084
{Abstract}: <B>UNASSIGNED: </B>Despite advancements in surgical techniques, operations for infective endocarditis (IE) remain associated with relatively high mortality. The aim of this study was to develop a nomogram model to predict the early postoperative mortality in patients undergoing cardiac surgery for infective endocarditis based on the preoperative clinical features.<BR><B>UNASSIGNED: </B>We retrospectively analyzed the clinical data of 357 patients with IE who underwent surgeries at our center between January 2007 and June 2023. Independent risk factors for early postoperative mortality were identified using univariate and multivariate logistic regression models. Based on these factors, a predictive model was developed and presented in a nomogram. The performance of the nomogram was evaluated through the receiver operating characteristic (ROC) curve, calibration plot, and decision curve analysis (DCA). Internal validation was performed utilizing the bootstrapping method.<BR><B>UNASSIGNED: </B>The nomogram included nine predictors: age, stroke, pulmonary embolism, albumin level, cardiac function class IV, antibotic use <4weeks, vegetation size ≥1.5 cm, perivalvular abscess and preoperative dialysis. The area under the ROC curve (AUC) of the model was 0.88 (95%CI:0.80-0.96). The calibration plot indicated strong prediction consistency of the nomogram with satisfactory Hosmer-Lemeshow test results (χ2 = 13.490, p = 0.142). Decision curve analysis indicated that the nomogram model provided greater clinical net benefits compared to "operate-all" or "operate-none" strategies.<BR><B>UNASSIGNED: </B>The innovative nomogram model offers cardiovascular surgeons a tool to predict the risk of early postoperative mortality in patients undergoing IE operations. This model can serve as a valuable reference for preoperative decision-making and can enhance the clinical outcomes of IE patients.