Evidence map›Paper›PMID 42416511›Full record

ReviewJournal of anesthesia and translational medicine2026

Electroencephalographic phenotypes of postoperative delirium in the perioperative period.

Sanawaer Tuerhong, Linlin Han, Xu Ku, Ruili Ding, Xin Huang, Xiangdong Chen

Abstract readReview
In one paragraph

Review in Journal of anesthesia and translational medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Review
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

6 authors.

Sanawaer TuerhongDepartment of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Linlin HanDepartment of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Xu KuDepartment of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Ruili DingDepartment of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Xin HuangDepartment of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.
Xiangdong ChenDepartment of Anesthesiology, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan 430022, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Postoperative delirium (POD) is a common and severe complication among older surgical patients. Perioperative electroencephalography (EEG) monitoring provides a noninvasive, real-time window into cerebral neural activity. Traditionally, EEG has been simplified into single indices, such as the bispectral index (BIS), to guide anesthetic depth titration. However, recent large-scale clinical trials have demonstrated that avoiding EEG suppression based solely on such indices does not reduce the incidence of POD. This discrepancy partly arises because conventional indices are primarily derived from frontal EEG signals and fail to fully exploit the multidimensional information distributed across the whole brain. This review highlights that high-dimensional features embedded in raw EEG-including spectral characteristics, oscillatory dynamics, brain network connectivity, and aperiodic components-serve as key biomarkers for identifying individual neural vulnerability and POD risk. We systematically summarize spatiotemporal EEG phenotypes associated with POD pathophysiology, encompassing age-related baseline features, intraoperative high-risk patterns (such as burst suppression), and abnormal emergence trajectories indicative of failed brain network reorganization. Accumulating evidence indicates that aberrations in these EEG features are closely associated with POD occurrence and adverse clinical outcomes. Therefore, future EEG interpretation must move beyond single "depth" indices toward a mechanism-driven assessment that integrates high-dimensional EEG features with individual baseline characteristics, thereby providing novel strategies for the precise prevention of POD.

Indexed as

Depth of anesthesiaElectroencephalogramNeural oscillationsPerioperative neurocognitive disordersPostoperative delirium

Identifiers

PMID42416511
PMCPMC13338944

What Socratic holds

Textmetadata
LicenceCC BY-NC-ND
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Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.