Prediction of Postoperative Delirium in Older Adults from Preoperative Cognition and Occipital Alpha Power from Resting-State Electroencephalogram
Authors: Ning Matthew , Rodionov Andrei , Ross Jessica M. , Ozdemir Recep A. , Burch Maja , Lian Shu Jing , Alsop David , Cavallari Michele , Dickerson Bradford C. , Fong Tamara G. , Jones Richard N. , Libermann Towia A. , Marcantonio Edward R. , Santarnecchi Emiliano , Schmitt Eva M. , Touroutoglou Alexandra , Travison Thomas G. , Acker Leah , Reese Melody , Sun Haoqi , Westover Brandon , Berger Miles , Pascual-Leone Alvaro , Inouye Sharon K. , Shafi Mouhsin M. ,   the SAGES II Study Group and the INTUIT/PRIME Study Groups
Published: August 2024

Structured AbstractBackgroundPostoperative delirium is the most common complication following surgery among older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, and loss of independence, as well as markedly increased health-care costs. Electroencephalography (EEG) spectral slowing has frequently been observed during episodes of delirium, whereas intraoperative frontal alpha power is associated with postoperative delirium. We sought to identify preoperative predictors that could identify individuals at high risk for postoperative delirium, which could guide clinical decision-making and enable targeted interventions to potentially decrease delirium incidence and postoperative delirium-related complications.MethodsIn this prospective observational study, we used machine learning to evaluate whether baseline (preoperative) cognitive function and resting-state EEG could be used to identify patients at risk for postoperative delirium. Preoperative resting-state EEGs and the Montreal Cognitive Assessment were collected from 85 patients (age = 73 ± 6.4 years, 12 cases of delirium) undergoing elective surgery. The model with the highest f1-score was subsequently validated in an independent, prospective cohort of 51 older adults (age = 68 ± 5.2 years, 6 cases of delirium) undergoing elective surgery.ResultsOccipital alpha powers have higher f1-score than frontal alpha powers and EEG spectral slowing in the training cohort. Occipital alpha powers were able to predict postoperative delirium with AUC, specificity and accuracy all >90%, and sensitivity >80%, in the validation cohort. Notably, models incorporating transformed alpha powers and cognitive scores outperformed models incorporating occipital alpha powers alone or cognitive scores alone.ConclusionsWhile requiring prospective validation in larger cohorts, these results suggest that strong prediction of postoperative delirium may be feasible in clinical settings using simple and widely available clinical tools. Additionally, our results suggested that the thalamocortical circuit exhibits different EEG patterns under different stressors, with occipital alpha powers potentially reflecting baseline vulnerabilities.Clinical TrialsINTUIT: Investigating Neuroinflammation Underlying Postoperative Cognitive Dysfunction (ClinicalTrials.govID:NCT03273335, PI: Miles Berger, Project Start Date: 2017-06-15)Prior Presentation2024 American Delirium Society Annual Conference, presented by Matthew Ning, Ph.D., June 11th, 2024, Sacramento, CA, USA.Preprint ServerURL:https://www.medrxiv.org/content/10.1101/2024.08.15.24312053v1