Evidence map›Paper›PMID 39790454›Full record

ArticlePeerJ2025

A novel nomogram based on the patient's clinical data and CT signs to predict poor outcomes in AIS patients.

Jingyao Yang, Fangfang Deng, Qian Zhang, Zhuyin Zhang, Qinghua Luo, Yeyu Xiao

Abstract read
In one paragraph

Article in PeerJ, 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. 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

6 authors.

Jingyao YangGuangzhou University of Chinese Medicine, Guangzhou, China.
Fangfang DengDepartment of Medical Imaging, Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou, China.
Qian ZhangDepartment of Medical Imaging, Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou, China.
Zhuyin ZhangDepartment of Medical Imaging, Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou, China.
Qinghua LuoDepartment of Medical Imaging, Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou, China.
Yeyu XiaoDepartment of Medical Imaging, Guangzhou Hospital of Integrated Traditional and Western Medicine, Guangzhou, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: The 2019 American Heart Association/American Stroke Association (AHA/ASA) guidelines strongly advise using non-contrast CT (NCCT) of the head as a mandatory test for all patients with suspected acute ischemic stroke (AIS) due to CT's advantages of affordability and speed of imaging. Therefore, our objective was to combine patient clinical data with head CT signs to create a nomogram to predict poor outcomes in AIS patients. Methods: A retrospective analysis was conducted on 161 patients with acute ischemic stroke who underwent mechanical thrombectomy at the Guangzhou Hospital of Integrated Traditional and Western Medicine from January 2019 to June 2023. All patients were randomly assigned to either the training cohort ( Results: Multivariate logistic regression analysis showed that hyperdense middle cerebral artery sign (HMCAS) (OR 9.113; 95% CI [1.945-42.708]; Conclusion: This study introduces a novel practical nomogram based on HMCAS, ASPECTS > 6, NIHSS score, age, and WBC that can well predict the probability of poor outcomes after MT in patients with AIS.

Indexed as

Ischemic StrokeNomogramsTomography, X-Ray ComputedAgedFemaleHumansMaleMiddle AgedPrognosisRetrospective StudiesROC CurveThrombectomyTreatment OutcomeAcute ischemic strokeNomogramPoor outcomesThrombectomy

Identifiers

PMID39790454
PMCPMC11716008

What OpenQuestion holds

Textmetadata
LicenceCC BY
Read underepoch 390

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 OpenQuestion graph.