Morphological Features of the Vertebrobasilar System Predict Ischemic Stroke Risk in Spontaneous Vertebral Artery Dissection.

IF 2.4 3区 医学 Q2 CARDIAC & CARDIOVASCULAR SYSTEMS Journal of Cardiovascular Translational Research Pub Date : 2024-12-01 Epub Date: 2024-07-09 DOI:10.1007/s12265-024-10534-6
Jiajia Bao, Mateng Bai, Muke Zhou, Jinghuan Fang, Yanbo Li, Jian Guo, Li He
{"title":"Morphological Features of the Vertebrobasilar System Predict Ischemic Stroke Risk in Spontaneous Vertebral Artery Dissection.","authors":"Jiajia Bao, Mateng Bai, Muke Zhou, Jinghuan Fang, Yanbo Li, Jian Guo, Li He","doi":"10.1007/s12265-024-10534-6","DOIUrl":null,"url":null,"abstract":"<p><p>The vertebral artery's morphological characteristics are crucial in spontaneous vertebral artery dissection (sVAD). We aimed to investigate morphologic features related to ischemic stroke (IS) and develop a novel prediction model. Out of 126 patients, 93 were finally analyzed. We constructed 3D models and morphological analyses. Patients were randomly classified into training and validation cohorts (3:1 ratio). Variables selected by LASSO - including five morphological features and five clinical characteristics - were used to develop prediction model in the training cohort. The model exhibited a high area under the curve (AUC) of 0.944 (95%CI, 0.862-0.984), with internal validation confirming its consistency (AUC = 0.818, 95%CI, 0.597-0.948). Decision curve analysis (DCA) indicated clinical usefulness. Morphological features significantly contribute to risk stratification in sVAD patients. Our novel developed model, combining interdisciplinary parameters, is clinically useful for predicting IS risk. Further validation and in-depth research into the hemodynamics related to sVAD are necessary.</p>","PeriodicalId":15224,"journal":{"name":"Journal of Cardiovascular Translational Research","volume":" ","pages":"1365-1376"},"PeriodicalIF":2.4000,"publicationDate":"2024-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC11634921/pdf/","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Journal of Cardiovascular Translational Research","FirstCategoryId":"3","ListUrlMain":"https://doi.org/10.1007/s12265-024-10534-6","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"2024/7/9 0:00:00","PubModel":"Epub","JCR":"Q2","JCRName":"CARDIAC & CARDIOVASCULAR SYSTEMS","Score":null,"Total":0}
引用次数: 0

Abstract

The vertebral artery's morphological characteristics are crucial in spontaneous vertebral artery dissection (sVAD). We aimed to investigate morphologic features related to ischemic stroke (IS) and develop a novel prediction model. Out of 126 patients, 93 were finally analyzed. We constructed 3D models and morphological analyses. Patients were randomly classified into training and validation cohorts (3:1 ratio). Variables selected by LASSO - including five morphological features and five clinical characteristics - were used to develop prediction model in the training cohort. The model exhibited a high area under the curve (AUC) of 0.944 (95%CI, 0.862-0.984), with internal validation confirming its consistency (AUC = 0.818, 95%CI, 0.597-0.948). Decision curve analysis (DCA) indicated clinical usefulness. Morphological features significantly contribute to risk stratification in sVAD patients. Our novel developed model, combining interdisciplinary parameters, is clinically useful for predicting IS risk. Further validation and in-depth research into the hemodynamics related to sVAD are necessary.

Abstract Image

查看原文
分享 分享
微信好友 朋友圈 QQ好友 复制链接
本刊更多论文
椎基底动脉系统的形态特征可预测自发性椎动脉夹层的缺血性中风风险
椎动脉的形态特征在自发性椎动脉夹层(sVAD)中至关重要。我们旨在研究与缺血性卒中(IS)相关的形态特征,并建立一个新的预测模型。在 126 例患者中,最终分析了 93 例。我们构建了三维模型并进行了形态学分析。患者被随机分为训练组和验证组(比例为 3:1)。通过 LASSO 筛选出的变量(包括五个形态特征和五个临床特征)被用于在训练组中建立预测模型。该模型的曲线下面积(AUC)高达 0.944(95%CI,0.862-0.984),内部验证证实了其一致性(AUC = 0.818,95%CI,0.597-0.948)。决策曲线分析(DCA)显示了其临床实用性。形态特征有助于对 sVAD 患者进行风险分层。我们新开发的模型结合了多学科参数,在预测 IS 风险方面具有临床实用性。有必要对与 sVAD 相关的血液动力学进行进一步验证和深入研究。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
求助全文
约1分钟内获得全文 去求助
来源期刊
Journal of Cardiovascular Translational Research
Journal of Cardiovascular Translational Research CARDIAC & CARDIOVASCULAR SYSTEMS-MEDICINE, RESEARCH & EXPERIMENTAL
CiteScore
6.10
自引率
2.90%
发文量
148
审稿时长
6-12 weeks
期刊介绍: Journal of Cardiovascular Translational Research (JCTR) is a premier journal in cardiovascular translational research. JCTR is the journal of choice for authors seeking the broadest audience for emerging technologies, therapies and diagnostics, pre-clinical research, and first-in-man clinical trials. JCTR''s intent is to provide a forum for critical evaluation of the novel cardiovascular science, to showcase important and clinically relevant aspects of the new research, as well as to discuss the impediments that may need to be overcome during the translation to patient care.
期刊最新文献
Machine Learning Model for Risk Prediction of Prolonged Intensive Care Unit in Patients Receiving Intra-aortic Balloon Pump Therapy during Coronary Artery Bypass Graft Surgery. NAT10 Modulates Atherosclerosis Progression Mediated by Macrophage Polarization Through Regulating ac4C Modification of TLR9. Associations of Blood Lipid-Related Polygenic Scores, Lifestyle Factors and Their Combined Effects with Risk of Coronary Artery Disease in the UK Biobank Cohort. Prediction of Major Adverse Limb Events in Females with Peripheral Artery Disease using Blood-Based Biomarkers and Clinical Features. Endothelial Cell-Derived Extracellular Vesicles Allow to Differentiate Between Various Endotypes of INOCA: A Multicentre, Prospective, Cohort Study.
×
引用
GB/T 7714-2015
复制
MLA
复制
APA
复制
导出至
BibTeX EndNote RefMan NoteFirst NoteExpress
×
×
提示
您的信息不完整,为了账户安全,请先补充。
现在去补充
×
提示
您因"违规操作"
具体请查看互助需知
我知道了
×
提示
现在去查看 取消
×
提示
确定
0
微信
客服QQ
Book学术公众号 扫码关注我们
反馈
×
意见反馈
请填写您的意见或建议
请填写您的手机或邮箱
已复制链接
已复制链接
快去分享给好友吧!
我知道了
×
扫码分享
扫码分享
Book学术官方微信
Book学术文献互助
Book学术文献互助群
群 号:481959085
Book学术
文献互助 智能选刊 最新文献 互助须知 联系我们:info@booksci.cn
Book学术提供免费学术资源搜索服务,方便国内外学者检索中英文文献。致力于提供最便捷和优质的服务体验。
Copyright © 2023 Book学术 All rights reserved.
ghs 京公网安备 11010802042870号 京ICP备2023020795号-1