Evidence map›Paper›PMID 40276889›Full record

Observational studyKorean journal of anesthesiology2025

Predicting neurological complications post clipping surgery in unruptured intracranial aneurysms using the NEURO score: a multi-center retrospective cohort study.

Yeon Ju Kim, Ah Ran Oh, Soo Jeong, Jungchan Park, Min-Ju Kim, Seong-Yoon Kim, Wonhyoung Park, Jae Sung Ahn, Chan-Sik Kim, Ji-Hoon Sim and 3 more

Abstract readMulticenter StudyObservational Study
In one paragraph

Observational study in Korean journal of anesthesiology, 2025. 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. Advancing risk prediction in intracranial aneurysm clipping.Korean journal of anesthesiology · 2025
    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

13 authors.

Yeon Ju KimDepartment of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Ah Ran OhDepartment of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Soo JeongDepartment of Neurology, Hanyang University College of Medicine, Seoul, Korea.
Jungchan ParkDepartment of Anesthesiology and Pain Medicine, Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Korea.
Min-Ju KimDepartment of Clinical Epidemiology and Biostatistics, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Seong-Yoon KimKorea Electronics Technology Institute, Seongnam, Korea.
Wonhyoung ParkDepartment of Neurological Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Jae Sung AhnDepartment of Neurological Surgery, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Chan-Sik KimDepartment of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Ji-Hoon SimDepartment of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Yong-Seok ParkDepartment of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Seungil HaDepartment of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.
Joung Uk KimDepartment of Anesthesiology and Pain Medicine, Asan Medical Center, University of Ulsan College of Medicine, Seoul, Korea.

Funding

Ministry of Science and ICT RS-2022-00165755National Research Foundation of Korea
6 · The paper itself

Abstract

backgroundPredicting fatal neurological complications after clipping surgery for unruptured intracranial aneurysms (UIAs) is crucial; however, existing scoring systems are limited by narrow consideration of factors. We aimed to develop and validate a comprehensive risk stratification scoring system that incorporates patient-, aneurysm-, and operation-specific variables for predicting postoperative neurological complications in UIA surgeries.

methodsThis multi-center retrospective cohort study was conducted from September 2018 to October 2023. Patients undergoing clipping surgery for UIAs were divided into development and validation sets based on the treating institution. A predictive score for postoperative neurological complications was developed from a multivariate logistic regression analysis. The score, named NEURO, that incorporates variables like previous neurological disease, categorized aneurysm location and size, categorized operation time, and transfusion was validated externally.

resultsThe study included 2847 patients, with 1547 and 1300 in the development and validation sets, based on the institution of surgery, respectively. The incidence of neurological complications was 5.7% (88/1547) and 5.6% (73/1300) in the development and validation sets, respectively. The NEURO score showed good predictive ability with C-statistics of 0.720 (95% CI [0.667-0.776]) in the development set and 0.693 (95% CI [0.631-0.754]) in the validation set, demonstrating good calibration across the predicted probability range.

conclusionsThe NEURO score, integrating multiple perioperative variables, may effectively predict the risk of neurological complications post UIA clipping surgery, aiding in identifying high-risk patients. This tool could enhance clinical decision-making and patient management in neurosurgical practice.

Indexed as

Intracranial AneurysmNervous System DiseasesNeurosurgical ProceduresPostoperative ComplicationsAdultAgedCohort StudiesFemaleHumansMaleMiddle AgedPredictive Value of TestsRetrospective StudiesRisk AssessmentCerebrovascular disordersIntracranial aneurysmMulticenter studyNeurosurgeryPostoperative complicationsRisk assessment

Identifiers

PMID40276889
PMCPMC12489578

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Registered trials

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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.