Age and ageing · 2025
Prediction of postoperative delirium in older adults from preoperative cognition and occipital alpha power from resting-state electroencephalogram.
СтатьяНаблюдательное исследованиеНазвание и аннотация: Перевод в очереди
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Ning Matthew H, Rodionov Andrei, Ross Jessica M, Ozdemir Recep A, Burch Maja, Lian Shu J, Alsop David, Cavallari Michele, Dickerson Bradford, Fong Tamara, Jones Richard N, Libermann Towia, Marcantonio Edward, Santarnecchi Emiliano, Schmitt Eva M, Touroutoglou Alexandra, Travison Thomas, Acker Leah, Smith Melody R, Sun Haoqi, Westover M Brandon, Pascual-Leone Álvaro, Berger Miles, Inouye Sharon K, Shafi Mouhsin
Аннотация
BACKGROUND: Postoperative delirium is the most common complication following surgery amongst older adults, and has been consistently associated with increased mortality and morbidity, cognitive decline, loss of independence and increased health-care costs. 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.
METHODS: Preoperative resting-state electroencephalograms (EEGs) and the Montreal Cognitive Assessment were collected from a prospective observational cohort of 85 older adults (12 cases of delirium) undergoing elective surgery. Four machine learning models were tested and the model with the highest f1-score was subsequently validated in an independent, prospective cohort of 51 older adults (6 cases of delirium) undergoing elective surgery.
RESULTS: Occipital alpha powers have higher f1-score (0.57 ± 0.07) than frontal alpha powers (0.47 ± 0.07), EEG spectral slowing (0.48 ± 0.08), or modelling of EEG power spectral density into periodic and aperiodic components (0.44 ± 0.09) in the training cohort. Occipital alpha powers plus cognitive scores were able to predict postoperative delirium with area under the receiver operating characteristic curve (AUC) (0.94, 95% CI: [0.86-0.99]), sensitivity (0.83, 95% CI: [0.50-1.00]) and specificity (0.91, 95% CI: [0.82-0.98]) in the validation cohort, and outperformed models incorporating occipital alpha powers alone or cognitive scores alone.
CONCLUSIONS: Whilst the sample size is small and findings require confirmation in larger studies, our results suggest that the thalamocortical circuit exhibits different EEG patterns under stressors, with occipital alpha powers potentially reflecting baseline vulnerabilities.
METHODS: Preoperative resting-state electroencephalograms (EEGs) and the Montreal Cognitive Assessment were collected from a prospective observational cohort of 85 older adults (12 cases of delirium) undergoing elective surgery. Four machine learning models were tested and the model with the highest f1-score was subsequently validated in an independent, prospective cohort of 51 older adults (6 cases of delirium) undergoing elective surgery.
RESULTS: Occipital alpha powers have higher f1-score (0.57 ± 0.07) than frontal alpha powers (0.47 ± 0.07), EEG spectral slowing (0.48 ± 0.08), or modelling of EEG power spectral density into periodic and aperiodic components (0.44 ± 0.09) in the training cohort. Occipital alpha powers plus cognitive scores were able to predict postoperative delirium with area under the receiver operating characteristic curve (AUC) (0.94, 95% CI: [0.86-0.99]), sensitivity (0.83, 95% CI: [0.50-1.00]) and specificity (0.91, 95% CI: [0.82-0.98]) in the validation cohort, and outperformed models incorporating occipital alpha powers alone or cognitive scores alone.
CONCLUSIONS: Whilst the sample size is small and findings require confirmation in larger studies, our results suggest that the thalamocortical circuit exhibits different EEG patterns under stressors, with occipital alpha powers potentially reflecting baseline vulnerabilities.