Efficacy of prognostic nutrition index in combination with D-dimer in predicting postoperative clinical adverse events after acute type A aortic dissection: a single center retrospective study.

Abstract

The aim of this study was to identify the predictive factors for adverse clinical events after surgery in patients with acute type A aortic dissection (AAAD), and to explore the predictive value of preoperative prognostic nutritional index (PNI) combined with D-dimer for these events. This study was a retrospective analysis of clinical data of 153 patients with AAAD who underwent emergency surgery at our center from January 2019 to January 2022. Patients were divided into adverse event group and non-adverse event group based on whether they experienced adverse clinical events after surgery. Univariate and multivariable logistic regression analyses were performed to identify the risk factors for adverse events, and the predictive efficacy was evaluated by the area under the receiver operating characteristic curve (ROC-AUC). A total of 153 AAAD patients were included in the study, and were divided into the adverse event group (n = 46) and the non-adverse events group (n = 107) based on whether or not they experienced clinical adverse events after surgery. The optimal cutoff value was determined using ROC curves, and multivariate logistic regression analysis was performed. Ultimately, it was found that preoperative PNI < 42.45 and D-dimer > 15.05 were independent predictors of postoperative clinical adverse events in AAAD patients. The odd ratios (OR) value for preoperative PNI < 42.45 is 3.596 [95% Confidence Interval (CI): 1.508-8.923, p = 0.004], while the OR value for D-dimer > 15.05 is 7.572 [95% CI: 3.094-20.220, p < 0.001]. The combination of these two indicators has a high predictive value (AUC = 0.843, 95% CI: 0.774-0.912, p < 0.001) and is superior to using either variable alone. Preoperative PNI < 42.45 and D-dimer > 15.05 are independent predictive factors for postoperative adverse events during hospitalization in patients with AAAD. The combination of these two indicators can improve the predictive accuracy, which is superior to using either variable alone.