Evidence map›Paper›PMID 36709247›Full record

ArticleBMC neurology2023

Rupture discrimination of multiple small (< 7 mm) intracranial aneurysms based on machine learning-based cluster analysis.

Xin Tong, Xin Feng, Fei Peng, Hao Niu, Xin Zhang, Xifeng Li, Yuanli Zhao, Aihua Liu, Chuanzhi Duan

Open access · goldAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in BMC neurology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
5citing papers in PubMed, 1 pooled it
1.4field-weighted citation impact, top 18% of its field
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

5 citing papers in PubMed, 1 synthesis or guideline pooled it, 7 citations in OpenAlex.

  1. Systematic Review of Radiomics and Artificial Intelligence in Intracranial Aneurysm Management.Journal of neuroimaging : official journal of the American Society of Neuroimaging
    Pooled it
  2. Article
  3. Article
  4. Article
  5. Observational
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

9 authors at 5 institutions in 1 country.

Xin Tong *Department of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, 119 Fanyang Road, Beijing, 100070, China.
Xin Feng *National Key Clinical Specialty, Department of Neurosurgery, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Fei PengDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, 119 Fanyang Road, Beijing, 100070, China.
Hao NiuDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, 119 Fanyang Road, Beijing, 100070, China.
Xin ZhangNational Key Clinical Specialty, Department of Neurosurgery, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Xifeng LiNational Key Clinical Specialty, Department of Neurosurgery, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China.
Yuanli ZhaoDepartment of Neurosurgery, Peking University International Hospital, Beijing, China.
Aihua LiuDepartment of Neurosurgery, Beijing Neurosurgical Institute and Beijing Tiantan Hospital, Capital Medical University, China National Clinical Research Center for Neurological Diseases, 119 Fanyang Road, Beijing, 100070, China. liuaihuadoctor@163.com.
Chuanzhi DuanNational Key Clinical Specialty, Department of Neurosurgery, Engineering Technology Research Center of Education Ministry of China, Guangdong Provincial Key Laboratory On Brain Function Repair and Regeneration, Neurosurgery Institute, Zhujiang Hospital, Southern Medical University, Guangzhou, China. doctor_duan@126.com.
Capital Medical University · CNSouthern Medical University · CNBeijing Institute of Neurosurgery · CNPeking University International Hospital · CNZhujiang Hospital · CN

Funding

Beijing Municipal Administration of Hospitals' Ascent Plan DFL20190501Beijing Natural Science Foundation 19L2013, 22G10396BTH Coordinated Development-Beijing Science and Technology Planning Project Z181100009618035Natural Science Foundation of Shenyang City 81771233,82171290Research and Promotion Program of Appropriate Techniques for Intervention of Chinese High-risk Stroke People GN-2020R0007
6 · The paper itself

Abstract

backgroundSmall multiple intracranial aneurysms (SMIAs) are known to be more prone to rupture than are single aneurysms. However, specific recommendations for patients with small MIAs are not included in the guidelines of the American Heart Association and American Stroke Association. In this study, we aimed to evaluate the feasibility of machine learning-based cluster analysis for discriminating the risk of rupture of SMIAs.

methodsThis multi-institutional cross-sectional study included 1,427 SMIAs from 660 patients. Hierarchical cluster analysis guided patient classification based on patient-level characteristics. Based on the clusters and morphological features, machine learning models were constructed and compared to screen the optimal model for discriminating aneurysm rupture.

resultsThree clusters with markedly different features were identified. Cluster 1 (n = 45) had the highest risk of subarachnoid hemorrhage (SAH) (75.6%) and was characterized by a higher prevalence of familiar IAs. Cluster 2 (n = 110) had a moderate risk of SAH (38.2%) and was characterized by the highest rate of SAH history and highest number of vascular risk factors. Cluster 3 (n = 505) had a relatively mild risk of SAH (17.6%) and was characterized by a lower prevalence of SAH history and lower number of vascular risk factors. Lasso regression analysis showed that compared with cluster 3, clusters 1 (odds ratio [OR], 7.391; 95% confidence interval [CI], 4.074-13.150) and 2 (OR, 3.014; 95% CI, 1.827-4.970) were at a higher risk of aneurysm rupture. In terms of performance, the area under the curve of the model was 0.828 (95% CI, 0.770-0.833).

conclusionsAn unsupervised machine learning-based algorithm successfully identified three distinct clusters with different SAH risk in patients with SMIAs. Based on the morphological factors and identified clusters, our proposed model has good discrimination ability for SMIA ruptures.

Indexed as

Aneurysm, RupturedIntracranial AneurysmSubarachnoid HemorrhageCluster AnalysisCross-Sectional StudiesHumansMachine LearningRisk FactorsCluster analysisMachine learningRisk discriminationSmall multiple intracranial aneurysms

Identifiers

PMID36709247
PMCPMC9883873
OpenAlexW4318323734

What OpenQuestion holds

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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.