[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118436-en":3,"doc-seo-118436-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},118436,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment - ORIGINAL RESEARCH","Endovascular treatment (EVT) is recommended for intracranial aneurysms, yet poor-grade aneurysmal subarachnoid hemorrhage (aSAH) still shows a high rate of unfavorable functional outcome. This study extracts outcome-related variables from the multicenter PROSAH-MPC registry, splits data into training and validation cohorts (7:3), and builds nine machine learning models plus a stack ensemble. Multivariable logistic regression identifies independent risk factors, and model performance is assessed with AUC-ROC, complemented by SHAP-based feature explanation. LightGBM demonstrates the best validation performance and highlights age as a key driver.","Therapeutics and Clinical Risk Management downloaded from [https://www.dovepress.com/](https://www.dovepress.com/)  \nFor personal use only.  \nTherapeutics and Clinical Risk Management  \nOpen Access Full Text Article ORIGINAL RESEARCH  \nDevelopment and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment  \nSenlin Du 1–4 , *, Yanze Wu 1–4 , *, Jiarong Tao 1 , Lei Shu 1–4 , Tengfeng Yan 1–4 , Bing Xiao 1 , Shigang Lv 1 , Minhua Ye 1 , Yanyan Gong 1 , Xingen Zhu 1–4 , Ping Hu 1 , 5 , Miaojing Wu 1  \n1Department of Neurosurgery, The second Affiliated Hospital, Jiangxi Medical College of Nanchang University, Nanchang, 330006, People’s Republic of China; 2Jiangxi Key Laboratory of Neurological Tumors and Cerebrovascular Diseases, Nanchang, 330006, People’s Republic of China; 3Jiangxi Health Commission Key Laboratory of Neurological Medicine, Nanchang, 330006, People’s Republic of China; 4Institute of Neuroscience, Nanchang University, Nanchang, 330006, People’s Republic of China; 5Department of Neurosurgery, Panzhihua Central Hospital, The second Clinical Medical College of Panzhihua University, Panzhihua, 617067, People’s Republic of China  \n*These authors contributed equally to this work  \nCorrespondence: Miaojing Wu, Department of Neurosurgery, The second Affiliated Hospital, Jiangxi Medical College of Nanchang University, Nanchang, 330006, People’s Republic of China, Email wmj [1987@163.com](1987@163.com)  \n\n| Background: Endovascular treatment (EVT) has been recommended as a superior modality for the treatment of intracranial aneurysm. However, there still exists a worse percentage of poor functional outcome in patients with poor-grade aneurysmal subarachnoid hemorrhage (aSAH) undergoing EVT. Therefore, it is urgently needed to investigate the risk factors and develop a critical decision model in the subtype of such patients.\u003Cbr>Methods: We extracted the target variables from an ongoing registry cohort study, PROSAH-MPC, which was conducted in multiple centers in China. We randomly assigned these patients to training and validation cohorts with a ratio of 7:3 . Univariate and multivariate logistic regressions were performed to find the potential factors, and then nine machine learning models and a stack ensemble model were developed with optimized variables. The performance of these models was evaluated through several indicators, including area under the receiver operating characteristic curve (AUC-ROC) . We further use Shapley Additive Explanations (SHAP) methods for the distribution of feature visualization based on the optimal models.\u003Cbr>Results: A total of 226 eligible patients with poor-grade aSAH undergoing EVT were enrolled, while 89 (39.4%) has a poor 12-month outcome. Age (Adjusted OR [aOR], 1.08; 95% CI: 1.03–1.13; p = 0.002), subarachnoid hemorrhage volume (aOR, 1.02; 95% CI: 1.00–1.05; p = 0.033), World Federation of Neurosurgical Societies grade (WFNS) (aOR, 2.03; 95% CI: 1.05–3.93; p = 0.035), and Hunt-Hess grade (aOR, 2.36; 95% CI: 1.13–4.93; p = 0.022) were identified as the independent risk factors of the poor outcome. Then, the prediction models developed have revealed that LightGBM algorithm has a superior performance with an AUC-ROC value of 0.842 in the validation cohort, while the SHAP results showed that age is the most important risk factor affecting functional outcomes. Conclusion: The LightGBM model holds immense potential in facilitating risk stratification for poor-grade aSAH patients undergoing endovascular treatment who are at risk of adverse outcomes, thereby enhancing clinical decision-making processes.\u003Cbr>Trial Registration: PROSAH-MPC. NCT05738083. Registered 16 November 2022 – Retrospectively registered, [https://clinical](https://clinical)[ ](https://clinical)[trials.gov/study/NCT05738083](trials.gov/study/NCT05738083) .\u003Cbr>Keywords: intracranial aneurysm, subarachnoid hemorrhage, endovascu","cbCaiu5hQKbfBS6Q","https://ap.wps.com/l/cbCaiu5hQKbfBS6Q","pdf",6366890,1,15,"English","en",105,"# Introduction\n## Background and rationale\n# Methods\n## Dataset and cohort assignment\n## Model development and evaluation\n## Explainability using SHAP\n# Results\n## Cohort outcomes\n## Independent risk factors\n## Predictive performance and feature importance\n# Conclusion","[{\"question\":\"What clinical problem does the study address in poor-grade aSAH patients?\",\"answer\":\"It targets the persistently high proportion of poor functional outcomes in patients with poor-grade aneurysmal subarachnoid hemorrhage undergoing endovascular treatment.\"},{\"question\":\"How were the machine learning models developed and validated?\",\"answer\":\"Variables were extracted from the multicenter PROSAH-MPC registry, patients were randomly split into training and validation cohorts in a 7:3 ratio, and logistic regression was used to identify factors before training nine machine learning models and a stack ensemble.\"},{\"question\":\"Which model performed best and which factor was most influential?\",\"answer\":\"LightGBM achieved superior validation performance with an AUC-ROC of 0.842, and SHAP results indicated that age is the most important risk factor affecting functional outcomes.\"}]","Development and Validation of Machine Learning Models for Outcome Prediction in Patients with Poor-Grade Aneurysmal Subarachnoid Hemorrhage Following Endovascular Treatment - ORIGINAL RESEARCH | PDF",1785683605,38,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"development-and-validation-of-machine-learning-models-for-outcome-prediction-in-patients-with-poor-grade-aneurysmal-subarachnoid-hemorrhage-following-endovascular-treatment-original-research","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/development-and-validation-of-machine-learning-models-for-outcome-prediction-in-patients-with-poor-grade-aneurysmal-subarachnoid-hemorrhage-following-endovascular-treatment-original-research/118436/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What clinical problem does the study address in poor-grade aSAH patients?","Question",{"text":75,"@type":76},"It targets the persistently high proportion of poor functional outcomes in patients with poor-grade aneurysmal subarachnoid hemorrhage undergoing endovascular treatment.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine learning models developed and validated?",{"text":80,"@type":76},"Variables were extracted from the multicenter PROSAH-MPC registry, patients were randomly split into training and validation cohorts in a 7:3 ratio, and logistic regression was used to identify factors before training nine machine learning models and a stack ensemble.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and which factor was most influential?",{"text":84,"@type":76},"LightGBM achieved superior validation performance with an AUC-ROC of 0.842, and SHAP results indicated that age is the most important risk factor affecting functional outcomes.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]