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Developing predictive nomogram models using quantitative electroencephalography for brain function in type a aortic

Ya-Peng Wang1,2, Yi Jiang1, Lin Mi3

  • 1Department of Cardio-Thoracic Surgery, Nanjing Drum Tower Hospital, Chinese Academy of Medical Science & Peking Union Medical College, Nanjing, Jiangsu, China.

International Journal of Surgery (London, England)
|January 27, 2025
PubMed
Summary
This summary is machine-generated.

Quantitative electroencephalography (QEEG) effectively predicts neurological dysfunction after Type A aortic dissection (TAAD) surgery. Combining QEEG with clinical factors like bypass time improves early detection of brain impairment, aiding better patient outcomes.

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Area of Science:

  • Neuroscience
  • Cardiovascular Surgery
  • Medical Technology

Background:

  • Type A aortic dissection (TAAD) poses significant risks for neurological dysfunction and mortality in cardiac surgery.
  • Advances in surgical techniques have not fully mitigated these risks.
  • This study explores quantitative electroencephalography (QEEG) for perioperative neurological outcome prediction in TAAD patients.

Purpose of the Study:

  • To investigate the utility of QEEG in monitoring and predicting neurological outcomes during the perioperative period for TAAD patients.
  • To develop predictive models for adverse outcomes (AO) and transient neurological dysfunction (TND) using QEEG metrics and clinical variables.

Main Methods:

  • A prospective observational study involving TAAD patients from February 2022 to January 2023.
  • QEEG parameters, including dynamic amplitude-integrated electroencephalography (aEEG) grade, aEEG, and relative band power (RBP), were monitored preoperatively, intraoperatively, and postoperatively.
  • Predictive nomogram models were developed using QEEG metrics and clinical data.

Main Results:

  • Multivariable analysis identified pre-mental status, cardiopulmonary bypass time, and dynamic aEEG grade as independent risk factors for adverse outcomes (AO).
  • The AO predictive model demonstrated high discriminative ability (AUC=0.888).
  • Cardiopulmonary bypass time, Post-RBP Alpha%, and dynamic aEEG grade were identified as independent risk factors for transient neurological dysfunction (TND), with the TND model showing an AUC of 0.893.

Conclusions:

  • Perioperative QEEG monitoring, combined with clinical indicators, enables the development of effective predictive models for AO and TND after TAAD surgery.
  • These models facilitate early detection of postoperative brain function impairment.
  • The findings support the use of QEEG for postoperative brain function monitoring in TAAD patients, potentially improving clinical outcomes.