Evidence map›Paper›PMID 39317392›Full record

ArticleAnnals of medicine2024

Optimizing anesthesia management based on early identification of electroencephalogram burst suppression risk in non-cardiac surgery patients: a visualized dynamic nomogram.

Jian Chen, Wanxia Li, Qianping Chen, Zhou Zhou, Chen Chen, Yuping Hu, Yanna Si, Jianjun Zou

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Annals of medicine, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT07031427 (Incidence of Slow Delta EEG Frequencies), which is not on this 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.

NCT07031427 not yet recruitingnot on this mapstarted 2025, after this paper: background citation

Incidence of Slow Delta EEG Frequencies (0.1-1 Hz) Before Burst Suppression: A Prospective Observational Pilot Study

TypeobservationalSponsorInsel Gruppe AG, University Hospital BernRan2025 to 2025Enrolled30ConditionsBurst Suppression, Anesthesia
3 · Its place in the literature

Who cites it

1 citing paper in PubMed.

  1. Article
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

8 authors.

Jian ChenDepartment of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Wanxia LiSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Qianping ChenSchool of Basic Medicine and Clinical Pharmacy, China Pharmaceutical University, Nanjing, China.
Zhou ZhouDepartment of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Chen ChenDepartment of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Yuping HuDepartment of Anesthesiology, The Fourth Affiliated Hospital of Nanjing Medical University, Nanjing, China.
Yanna SiDepartment of Anesthesiology, Perioperative and Pain Medicine, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.
Jianjun ZouDepartment of Pharmacy, Nanjing First Hospital, Nanjing Medical University, Nanjing, China.ORCID 0000-0003-0886-3153

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundBurst suppression (BS) is a specific electroencephalogram (EEG) pattern that may contribute to postoperative delirium and negative outcomes. Few prediction models of BS are available and some factors such as frailty and intraoperative hypotension (IOH) which have been reported to promote the occurrence of BS were not included. Therefore, we look forward to creating a straightforward, precise, and clinically useful prediction model by incorporating new factors, such as frailty and IOH. MATERIALS AND

methodsWe retrospectively collected 540 patients and analyzed the data from 418 patients. Univariate analysis and backward stepwise logistic regression were used to select risk factors to develop a dynamic nomogram model, and then we developed a web calculator to visualize the process of prediction. The performance of the nomogram was evaluated in terms of discrimination, calibration, and clinical utility.

resultsAccording to the receiver operating characteristic (ROC) analysis, the nomogram showed good discriminative ability (AUC = 0.933) and the Hosmer-Lemeshow goodness-of-fit test demonstrated the nomogram had good calibration (

conclusionsIncorporating two distinctive new risk factors, frailty and IOH, we firstly developed a visualized nomogram for accurately predicting BS in non-cardiac surgery patients. The model is expected to guide clinical decision-making and optimize anesthesia management.

Indexed as

ElectroencephalographyHypotensionNomogramsAdultAgedAnesthesiaFemaleFrailtyHumansMaleMiddle AgedRetrospective StudiesRisk AssessmentRisk FactorsROC CurveBurst suppressiondynamic nomogramfrailtyintraoperative hypotensionnon-cardiac surgeryprediction

Identifiers

PMID39317392
PMCPMC11423528

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Registered trials

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 OpenQuestion graph.