Point-of-care electroencephalography for prediction of postoperative delirium in older adults undergoing elective surgery: protocol for a prospective cohort study
Bibliographic record
Abstract
ABSTRACT Background Postoperative delirium (POD) is a complication of surgery in older adults associated with adverse outcomes. Current screening methods demonstrate poor interrater reliability, and conventional electroencephalography (EEG)-based screening requires intensive setup. Point-of-care (POC) EEG technology offers a rapid and objective alternative that may capture neurophysiological signatures of delirium risk. When combined with baseline and perioperative variables, POC EEG may enable prediction of POD before clinical manifestation. In this study, we aim to develop a POD prediction model using POC EEG as well as explore secondary outcomes such as longer-term cognitive impairment and postoperative pain. Methods This is a prospective cohort study enrolling older adults (≥60 years) undergoing elective non-cranial inpatient surgery at two academic hospitals. The target cohort size is 150 participants, determined by an events-per-parameter approach. All participants undergo baseline cognitive testing and pain assessment using the Montreal Cognitive Assessment (MoCA) and Numeric Rating Scale. The primary outcome is POD, while secondary outcomes include follow-up MoCA scores and postoperative pain scores. POD is assessed immediately after surgery and every 12 hours during the admission with the 4AT tool. Perioperative EEG is acquired using the Ceribell EEG system (Ceribell, Inc.) across standardized preoperative, intraoperative, and postoperative phases. EEG features such as spectral power, alpha/delta ratio, and burst suppression ratio are analyzed in relation to outcomes. Predictive models will be developed using regularized logistic regression with nested feature sets and model performance will be evaluated. Discussion This study evaluates whether POC EEG can accurately predict POD in older adults undergoing elective surgery, as well as longer-term cognitive impairment and postoperative pain. This approach could enable early identification of high-risk patients and facilitate targeted preventive strategies. By generating a validated risk model, multimodal exploratory analyses, and openly available datasets, this work aims to advance the practical management of perioperative outcomes. Registration: None. Strengths and Limitations Prospective, multicenter design with standardized perioperative EEG acquisition. Use of an FDA-cleared, rapid-deployment EEG device ensures clinical feasibility. Integration of EEG, cognitive, and biomarker data enables multimodal prediction. Modest single-institution sample size may limit generalizability. Limited-channel EEG montage may reduce spatial resolution and signal detail.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.031 | 0.023 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.019 | 0.005 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".