Developing predictive nomogram models using quantitative electroencephalography for brain function in type a aortic dissection: a prospective observational study
Authors: Wang Ya-Peng , Jiang Yi , Mi Lin , Liu Wen-Xue , Xue Yun-Xing , Chen Yang , Luo Xuan , Cheng Yong-Qing , Pan Jun , Qu Jason Zhensheng , Wang Dong-Jin 
Published: January 2025

Background:
Type A aortic dissection (TAAD) remains a significant challenge in cardiac surgery, presenting high risks of adverse outcomes such as permanent neurological dysfunction and mortality despite advances in medical technology and surgical techniques. This study investigates the use of quantitative electroencephalography (QEEG) to monitor and predict neurological outcomes during the perioperative period in TAAD patients.

Methods:
This prospective observational study was conducted at the hospital, involving patients undergoing TAAD surgery from February 2022 to January 2023. QEEG parameters, including the dynamic amplitude-integrated electroencephalography (aEEG) grade, which assesses changes in brain function over time, alongside aEEG and relative band power (RBP), were monitored and analyzed to assess brain function preoperatively, intraoperatively, and within 2 hours postoperatively. A predictive nomogram model was developed using these QEEG metrics along with other clinical variables to forecast neurological outcomes.

Results:
In this study, we analyzed the factors contributing to adverse outcomes (AO) and transient neurological dysfunction (TND) following TAAD surgery. For AO, multivariable analysis identified pre-mental status (odds ratio [OR] = 4.652, 95% confidence interval [CI] = 2.316–10.074, P < 0.001), cardiopulmonary bypass time (OR = 1.014, 95% CI = 1.006–1.023, P = 0.001), and dynamic aEEG grade (OR = 9.926, 95% CI = 4.493–25.268, P < 0.001) as independent risk factors. The AO model showed high discriminative ability with an area under the curve of 0.888 (95% CI = 0.818–0.960) and good calibration (Brier score = 0.0728). For TND, significant preoperative differences included dynamic aEEG grade (P < 0.001) and Log(Post-RBP Alpha%) (6.00 vs. 4.00, P < 0.001). Multivariable analysis identified cardiopulmonary bypass time (OR = 1.014, 95% CI = 1.006–1.023, P = 0.001), Post-RBP Alpha% (OR = 0.263, 95% CI = 0.121–0.532, P < 0.001), and dynamic aEEG grade (OR = 12.444, 95% CI = 5.337–30.814, P < 0.001) as independent risk factors. The TND model had an area under the curve of 0.893 (95% CI = 0.844–0.941) and good calibration (Brier score = 0.125). These findings highlight the role of QEEG in predicting postoperative neurological dysfunction in TAAD patients.

Conclusion:
Through perioperative QEEG monitoring of TAAD patients, combined with clinical indicators such as cardiopulmonary bypass time and preoperative mental status, we developed clinical predictive models for AO and TND after surgery. These models allow for early detection of postoperative brain function impairment, as assessed by QEEG parameters monitored intraoperatively and during the first 2 hours after surgery, a period chosen based on clinical definitions of delayed awakening and supported by the findings of this study. This study provides evidence supporting postoperative brain function monitoring in TAAD patients, with potential clinical implications for improved outcomes.