{'fraction_non_alphanumeric': 0.030432918988426916, 'fraction_numerical': 0.011144449207029576, 'mean_word_length': 5.53781512605042, 'pattern_counts': {'":': 0, '<': 0, '<?xml version=': 0, '>': 0, 'https://': 0, 'lorem ipsum': 0, 'www.': 0, 'xml': 0}, 'pii_count': 0, 'substrings_counts': 0, 'word_list_counts': {'cursed_substrings.json': 0, 'profanity_word_list.json': 0, 'sexual_word_list.json': 0, 'zh_pornsignals.json': 0}}
Neurophysiologic predictors of individual risk for post-operative delirium after elective surgery.

BACKGROUND
Post-surgical delirium is associated with increased morbidity, lasting cognitive decline, and loss of functional independence. Within a conceptual framework that delirium is triggered by stressors when vulnerabilities exist in cerebral connectivity and plasticity, we previously suggested that neurophysiologic measures might identify individuals at risk for post-surgical delirium. Here we demonstrate the feasibility of the approach and provide preliminary experimental evidence of the predictive value of such neurophysiologic measures for the risk of delirium in older persons undergoing elective surgery.


METHODS
Electroencephalography (EEG) and transcranial magnetic stimulation (TMS) were collected from 23 patients prior to elective surgery. Resting-state EEG spectral power ratio (SPR) served as a measure of integrity of neural circuits. TMS-EEG metrics of plasticity (TMS-plasticity) were used as indicators of brain capacity to respond to stressors. Presence or absence of delirium was assessed using the confusion assessment method (CAM). We included individuals with no baseline clinically relevant cognitive impairment (MoCA scores ≥21) in order to focus on subclinical neurophysiological measures.


RESULTS
In patients with no baseline cognitive impairment (N = 20, age = 72 ± 6), 3 developed post-surgical delirium (MoCA = 24 ± 2.6) and 17 did not (controls; MoCA = 25 ± 2.4). Patients who developed delirium had pre-surgical resting-state EEG power ratios outside the 95% confidence interval of controls, and 2/3 had TMS-plasticity measures outside the 95% CI of controls.


CONCLUSIONS
Consistent with our proposed conceptual framework, this pilot study suggests that non-invasive and scalable neurophysiologic measures can identify individuals at risk of post-operative delirium. Specifically, abnormalities in resting-state EEG spectral power or TMS-plasticity may indicate sub-clinical risk for post-surgery delirium. Extension and confirmation of these findings in a larger sample is needed to assess the clinical utility of the proposed neurophysiologic markers, and to identify specific connectivity and plasticity targets for therapeutic interventions that might minimize the risk of delirium.