Risk Factors and Construction of a Predictive Model for Postoperative Early Cognitive Dysfunction in Aortic Dissecting Aneurysms
Year: 2025
Type: article
Study design: Observational Study
Abstract: This study aimed to investigate the factors influencing cognitive dysfunction in patients with aortic dissection after surgery and to construct a risk prediction model for it.This retrospective study analyzed 122 patients with aortic dissection who were admitted to our hospital between January 1, 2015, and February 29, 2024. First, we grouped the patients based on the early occurrence of postoperative cognitive dysfunction (POCD) after surgery into the POCD and non-POCD groups. We subsequently compared the general clinical data and relevant surgical information between the two groups. Multivariate binary logistic regression analysis was performed to identify the factors influencing early POCD in patients with aortic dissection. Then, the risk prediction model was constructed. Receiver operating characteristic (ROC) curves were used to analyze each factor's predictive effectiveness.Multivariate binary logistic regression analysis revealed that age at surgery (P = 0.042), intensive care unit (ICU) stay duration (P = 0.003), mechanical ventilation time (P = 0.020), and aortic cross-clamping time (P = 0.010) were independent risk factors for early POCD in patients with aortic dissection, while postoperative oxygen saturation (P < 0.001) acted as an independent protective factor. A predictive model was established, achieving a ROC curve, area under the curve of 0.955, 95% confidence interval (0.917, 0.993), sensitivity of 0.878, and specificity of 0.926.Age at surgery, ICU stay duration, mechanical ventilation time, aortic cross-clamping time, and postoperative oxygen saturation were independent influencing factors for predicting early POCD.
Source: International Heart Journal
DOI: 10.1536/ihj.24-686
Authors: Lu Ding, Yinhuan Xu, Guangpu Fan
Institutions: Peking University, Peking University People's Hospital
Language: English
Subfield: Pulmonary and Respiratory Medicine
Field: Medicine
Domain: Health Sciences
Open Access status: diamond
OpenAlex ID: W4410879661
