Yunpeng Lin, Xiaoning Liu, Bingcheng Ren, Jiwen Wang, Yang Li, Xiangbo Liu, Yidi Wang, Fushun Xiao, Shiqing Mu
{"title":"机器学习预测颅内动脉瘤分流治疗后血栓栓塞事件：一项多中心回顾性研究。","authors":"Yunpeng Lin, Xiaoning Liu, Bingcheng Ren, Jiwen Wang, Yang Li, Xiangbo Liu, Yidi Wang, Fushun Xiao, Shiqing Mu","doi":"10.1007/s40120-025-00808-9","DOIUrl":null,"url":null,"abstract":"<p><strong>Introduction: </strong>Flow diverters (FD) have gradually become the preferred treatment option for complex and large intracranial aneurysms. Postoperative thromboembolic events (TEEs) are among the most common complications associated with endovascular treatment. However, widely applicable predictive tools for the occurrence of TEEs are currently lacking.</p><p><strong>Methods: </strong>This retrospective study included clinical data from 377 patients (a total of 451 aneurysms) treated with flow diverters at two neurointerventional centers between June 2018 and September 2022. Thirty-nine baseline patient characteristics were included as clinical variables. The primary endpoint was the occurrence of postoperative ischemic events. The dataset was randomly divided into a training set (80%) and a testing set (20%). We performed fivefold cross-validation and applied Lasso regression to the training set to identify the most informative features. Multiple machine learning (ML) algorithms were employed to construct predictive models. Model performance was evaluated on the testing set using the area under the receiver operating characteristic curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and calibration plots. SHapley Additive exPlanations (SHAP) analysis was used to visualize feature contributions and to interpret individual case predictions.</p><p><strong>Results: </strong>Among 377 patients, 21 (5.6%) experienced TEEs. A machine learning model incorporating 10 variables was developed, with the support vector machine (SVM) model demonstrating the best performance-achieving an AUC-ROC of 0.96 and an AUC-PR of 0.88 in validation. The key predictive factors included aneurysm width, low-density lipoprotein (LDL) levels, hypertension, aneurysm location, triglycerides (TG), and diabetes. Additionally, a web-based tool was developed to assist clinicians in applying the model in practice.</p><p><strong>Conclusions: </strong>We developed a machine learning model to predict the risk of TEEs following FD implantation for intracranial aneurysms, and demonstrated its clinical potential through internal validation. This tool can assist neurointerventionalists in estimating the probability of TEE occurrence based on patient clinical data and aneurysm characteristics, enabling the development of personalized treatment strategies.</p>","PeriodicalId":19216,"journal":{"name":"Neurology and Therapy","volume":" ","pages":"2171-2185"},"PeriodicalIF":4.8000,"publicationDate":"2025-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC12450181/pdf/","citationCount":"0","resultStr":"{\"title\":\"Machine Learning for Predicting Thromboembolic Events Following Flow Diverter Treatment of Intracranial Aneurysms: A Multicenter Retrospective Study.\",\"authors\":\"Yunpeng Lin, Xiaoning Liu, Bingcheng Ren, Jiwen Wang, Yang Li, Xiangbo Liu, Yidi Wang, Fushun Xiao, Shiqing Mu\",\"doi\":\"10.1007/s40120-025-00808-9\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<p><strong>Introduction: </strong>Flow diverters (FD) have gradually become the preferred treatment option for complex and large intracranial aneurysms. Postoperative thromboembolic events (TEEs) are among the most common complications associated with endovascular treatment. However, widely applicable predictive tools for the occurrence of TEEs are currently lacking.</p><p><strong>Methods: </strong>This retrospective study included clinical data from 377 patients (a total of 451 aneurysms) treated with flow diverters at two neurointerventional centers between June 2018 and September 2022. Thirty-nine baseline patient characteristics were included as clinical variables. The primary endpoint was the occurrence of postoperative ischemic events. The dataset was randomly divided into a training set (80%) and a testing set (20%). We performed fivefold cross-validation and applied Lasso regression to the training set to identify the most informative features. Multiple machine learning (ML) algorithms were employed to construct predictive models. Model performance was evaluated on the testing set using the area under the receiver operating characteristic curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and calibration plots. SHapley Additive exPlanations (SHAP) analysis was used to visualize feature contributions and to interpret individual case predictions.</p><p><strong>Results: </strong>Among 377 patients, 21 (5.6%) experienced TEEs. A machine learning model incorporating 10 variables was developed, with the support vector machine (SVM) model demonstrating the best performance-achieving an AUC-ROC of 0.96 and an AUC-PR of 0.88 in validation. The key predictive factors included aneurysm width, low-density lipoprotein (LDL) levels, hypertension, aneurysm location, triglycerides (TG), and diabetes. Additionally, a web-based tool was developed to assist clinicians in applying the model in practice.</p><p><strong>Conclusions: </strong>We developed a machine learning model to predict the risk of TEEs following FD implantation for intracranial aneurysms, and demonstrated its clinical potential through internal validation. 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Machine Learning for Predicting Thromboembolic Events Following Flow Diverter Treatment of Intracranial Aneurysms: A Multicenter Retrospective Study.
Introduction: Flow diverters (FD) have gradually become the preferred treatment option for complex and large intracranial aneurysms. Postoperative thromboembolic events (TEEs) are among the most common complications associated with endovascular treatment. However, widely applicable predictive tools for the occurrence of TEEs are currently lacking.
Methods: This retrospective study included clinical data from 377 patients (a total of 451 aneurysms) treated with flow diverters at two neurointerventional centers between June 2018 and September 2022. Thirty-nine baseline patient characteristics were included as clinical variables. The primary endpoint was the occurrence of postoperative ischemic events. The dataset was randomly divided into a training set (80%) and a testing set (20%). We performed fivefold cross-validation and applied Lasso regression to the training set to identify the most informative features. Multiple machine learning (ML) algorithms were employed to construct predictive models. Model performance was evaluated on the testing set using the area under the receiver operating characteristic curve (AUC-ROC), the area under the precision-recall curve (AUC-PR), and calibration plots. SHapley Additive exPlanations (SHAP) analysis was used to visualize feature contributions and to interpret individual case predictions.
Results: Among 377 patients, 21 (5.6%) experienced TEEs. A machine learning model incorporating 10 variables was developed, with the support vector machine (SVM) model demonstrating the best performance-achieving an AUC-ROC of 0.96 and an AUC-PR of 0.88 in validation. The key predictive factors included aneurysm width, low-density lipoprotein (LDL) levels, hypertension, aneurysm location, triglycerides (TG), and diabetes. Additionally, a web-based tool was developed to assist clinicians in applying the model in practice.
Conclusions: We developed a machine learning model to predict the risk of TEEs following FD implantation for intracranial aneurysms, and demonstrated its clinical potential through internal validation. This tool can assist neurointerventionalists in estimating the probability of TEE occurrence based on patient clinical data and aneurysm characteristics, enabling the development of personalized treatment strategies.
期刊介绍：
Aims and Scope
Neurology and Therapy aims to provide reliable and inclusive, rapid publication for all therapy related research for neurological indications, supporting the timely dissemination of research with a global reach, to help advance scientific discovery and support clinical practice.
Neurology and Therapy is an international, open access, peer reviewed, rapid publication journal dedicated to the publication of high-quality clinical (all phases), observational, real-world and health outcomes research around the discovery, development, and use of neurological and psychiatric therapies, (also covering surgery and devices). Studies relating to diagnosis, pharmacoeconomics, public health, quality of life, and patient care, management, and education are also welcomed.
The journal is of interest to a broad audience of healthcare professionals and publishes original research, reviews, case reports, trial designs, communications and letters. The journal is read by a global audience and receives submissions from all over the world. Neurology and Therapy will consider all scientifically sound research be it positive, confirmatory or negative data. Submissions are welcomed whether they relate to an international and/or a country-specific audience, something that is crucially important when researchers are trying to target more specific patient populations. This inclusive approach allows the journal to assist in the dissemination of all scientifically and ethically sound research.
Rapid Publication
The journal’s rapid publication timelines aim for a peer review decision within 2 weeks of submission. If an article is accepted, it will be published online 3-4 weeks from acceptance. These rapid timelines are achieved through the combination of a dedicated in-house editorial team, who closely manage article workflow, and an extensive Editorial and Advisory Board who assist with rapid peer review. This allows the journal to support the rapid dissemination of research, whilst still providing robust peer review. Combined with the journal’s open access model, this allows for the rapid and efficient communication of the latest research and reviews to support scientific discovery and clinical practice.
Open Access
All articles published by Neurology and Therapy are open access.
Personal Service
The journal’s dedicated in-house editorial team offer a personal “concierge service” meaning that authors will always have a personal point of contact able to update them on the status of their manuscript. The editorial team check all manuscripts to ensure that articles conform to the most recent COPE and ICMJE publishing guidelines. This supports the publication of ethically sound and transparent research. We also encourage pre-submission enquiries and are always happy to provide a confidential assessment of manuscripts.
Digital Features and Plain Language Summaries
Neurology and Therapy offers a range of additional features designed to increase the visibility, readership and educational value of the journal’s content. Each article is accompanied by key summary points, giving a time-efficient overview of the content to a wide readership. Articles may be accompanied by plain language summaries to assist readers who have some knowledge of, but not in-depth expertise in, the area to understand the scientific content and overall implications of the article. The journal also provides the option to include various types of digital features including animated abstracts, video abstracts, slide decks, audio slides, instructional videos, infographics, podcasts and animations. All additional features are peer reviewed to the same high standard as the article itself. If you consider that your paper would benefit from the inclusion of a digital feature, please let us know. Our editorial team are able to create high-quality slide decks and infographics in-house, and video abstracts through our partner Research Square, and would be happy to assist in any way we can. For further information about digital features, please contact the journal editor (see ‘Contact the Journal’ for email address), and see the ‘Guidelines for digital features and plain language summaries’ document under ‘Submission guidelines’.
For examples of digital features please visit our showcase page https://springerhealthcare.com/expertise/publishing-digital-features/
Publication Fees
Upon acceptance of an article, authors will be required to pay the mandatory Rapid Service Fee of €5250/$6000/£4300. The journal will consider fee discounts and waivers for developing countries and this is decided on a case-by-case basis.
Peer Review Process
Upon submission, manuscripts are assessed by the editorial team to ensure they fit within the aims and scope of the journal and are also checked for plagiarism. All suitable submissions are then subject to a comprehensive single-blind peer review. Reviewers are selected based on their relevant expertise and publication history in the subject area. The journal has an extensive pool of editorial and advisory board members who have been selected to assist with peer review based on the afore-mentioned criteria.
At least two extensive reviews are required to make the editorial decision, with the exception of some article types such as Commentaries, Editorials and Letters which are generally reviewed by one member of the Editorial Board. Where reviews conflict, an Editorial Board Member will be contacted for further advice and a presiding decision. Manuscripts are then either accepted, rejected or authors are required to make major or minor revisions (both reviewer comments and editorial comments may need to be addressed. Once a revised manuscript is re-submitted, it is assessed along with the responses to reviewer comments and if it has been adequately revised, it will be accepted for publication. Accepted manuscripts are then copyedited and typeset by the production team before online publication. Appeals against decisions following peer review are considered on a case-by-case basis and should be sent to the journal editor, and authors are welcome to make rebuttals against individual reviewer comments, if appropriate.
Preprints
We encourage posting of preprints of primary research manuscripts on preprint servers, authors'' or institutional websites, and open communications between researchers whether on community preprint servers or preprint commenting platforms. Posting of preprints is not considered prior publication and will not jeopardize consideration in our journals.
Please see here for further information on preprint sharing: https://www.springer.com/gp/authors-editors/journal-author/journal-author-helpdesk/submission/1302#c16721550
Copyright
Neurology and Therapy is published under the Creative Commons Attribution-Noncommercial License, which allows users to read, copy, distribute, and make derivative works for non-commercial purposes from the material, as long as the author of the original work is cited. The author assigns the exclusive right to any commercial use of the article to Springer. For more information about the Creative Commons Attribution-Noncommercial License, click here: http://creativecommons.org/licenses/by-nc/4.0.
Contact
For more information about the journal, including pre-submission enquiries, please contact managing editor Lydia Alborn at lydia.alborn@springer.com.
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