[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121885-en":3,"doc-seo-121885-105":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},121885,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage","Cerebral vasospasm (CV) is a feared complication after subarachnoid haemorrhage (SAH) and typically leads to intensive care monitoring, often requiring resource-intensive practices. A multi-center approach was used to build and validate machine-learning models that predict CV requiring verapamil (CVRV). Patients with SAH from UCLA (2013–2022) and a validation cohort from VUMC (2018–2023) were analyzed using 172 ICU variables, with early performance assessed across multiple timepoints.","UCLA  \nUCLA Previously Published Works  \nTitle  \nMachine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage.  \nPermalink  \n[https://escholarship.org/uc/item/12m905sn](https://escholarship.org/uc/item/12m905sn)  \nAuthors  \nZarrin, David  \nSuri, Abhinav McCarthy, Karen et al.  \nPublication Date  \n2024-07-01  \nDOI  \n10.1016/j.ebiom.2024.105206  \nPeer reviewed  \n[eScholarship.org](eScholarship.org) Powered by the California Digital Library  \nUniversity of California  \nArticles   \nMachine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage  \nDavid A. Zarrin,a Abhinav Suri,a Karen McCarthy,b Bilwaj Gaonkar,c Bayard R. Wilson,c Geoffrey P. Colby,c Robert E. Freundlich,b and Eilon Gabeld,∗  \naDavid Geffen School of Medicine at University of California, Los Angeles, USA bDepartment of Anesthesiology, Vanderbilt University Medical Center, USAcDepartment of Neurological Surgery at University of California, Los Angeles Health, USA  \ndDepartment of Anesthesia and Perioperative Medicine at University of California, Los Angeles Health, USA  \nSummary  \nBackground Cerebral vasospasm (CV) is a feared complication which occurs after 20–40% of subarachnoid haemorrhage (SAH) . It is standard practice to admit patients with SAH to intensive care for an extended period of resource-intensive monitoring. We used machine learning to predict CV requiring verapamil (CVRV) in the largest and only multi-center study to date.  \nMethods Patients with SAH admitted to UCLA from 2013 to 2022 and a validation cohort from VUMC from 2018 to 2023 were included. For each patient, 172 unique intensive care unit (ICU) variables were extracted through the primary endpoint, namely first verapamil administration or no verapamil. At each institution, a light gradient boosting machine (LightGBM) was trained using five-fold cross validation to predict the primary endpoint at various hospitalization timepoints.  \nFindings A total of 1750 patients were included from UCLA, 125 receiving verapamil. LightGBM achieved an area under the ROC (AUC) of 0.88 > 1 week in advance and ruled out 8% of non-verapamil patients with zero false negatives. Our models predicted “no CVRV” vs “CVRV within three days” vs “CVRV after three days” with AUCs = 0.88, 0.83, and 0.88, respectively. From VUMC, 1654 patients were included, 75 receiving verapamil. VUMC predictions averaged within 0.01 AUC points of UCLA predictions.  \nInterpretation We present an accurate and early predictor of CVRV using machine learning with multi-center validation. This represents a significant step towards optimized clinical management and resource allocation inpatients with SAH.  \nFunding Robert E. Freundlich is supported by National Center for Advancing Translational Sciences federal grant UL1TR002243 and National Heart, Lung, and Blood Institute federal grant K23HL148640; these funders did not play any role in this study. The National Institutes of Health supports Vanderbilt University Medical Center which indirectly supported these research efforts. Neither this study nor any other authors personally received financial support for the research presented in this manuscript. No support from pharmaceutical companies was received.  \nCopyright Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license ([http://](http://)[ ](http://)[creativecommons.org/licenses/by-nc-nd/4.0/](creativecommons.org/licenses/by-nc-nd/4.0/)).  \nKeywords: Cerebral vasospasm; Verapamil; Machine learning; Prediction  \nIntroduction  \nCerebral vasospasm (CV) is a common angiographic finding following subarachnoid haemorrhage (SAH) and is widely reported as a primary contributor to delayed cerebral ischemia (DCI) and concomitant morbidity and mortality in this population.1 CV manifests with variable severity, being angiographically appreciable in up to 70% of all patients with SAH and  \nclinically symptomatic in 20–40% of patients with SAH.2,3 A smaller subset of up to 20","cbCaictQMEg49sdp","https://ap.wps.com/l/cbCaictQMEg49sdp","pdf",2488072,1,12,"English","en",105,"# Summary\n## Background\n## Methods\n## Findings\n## Interpretation\n# Research in context\n## Evidence before this study\n## Added value of this study\n# Funding and copyright\n# Keywords\n# Introduction","[{\"question\":\"What clinical problem does the study address?\",\"answer\":\"The study targets cerebral vasospasm after subarachnoid haemorrhage, a complication linked to delayed cerebral ischemia and adverse outcomes.\"},{\"question\":\"How were the machine-learning predictions constructed?\",\"answer\":\"The models used 172 intensive care unit variables and trained a LightGBM model with five-fold cross validation to predict the primary endpoint based on first verapamil administration versus no verapamil.\"},{\"question\":\"What were the main performance results?\",\"answer\":\"The UCLA-trained model achieved an AUC of 0.88 more than a week in advance and ruled out verapamil for about 8% of non-verapamil patients with zero false negatives; VUMC predictions closely matched UCLA performance.\"}]","Machine learning predicts cerebral vasospasm in patients with subarachnoid haemorrhage | PDF",1785807484,30,{"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},"machine-learning-predicts-cerebral-vasospasm-in-patients-with-subarachnoid-haemorrhage","",{"@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/machine-learning-predicts-cerebral-vasospasm-in-patients-with-subarachnoid-haemorrhage/121885/",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","2026-08-04",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?","Question",{"text":75,"@type":76},"The study targets cerebral vasospasm after subarachnoid haemorrhage, a complication linked to delayed cerebral ischemia and adverse outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the machine-learning predictions constructed?",{"text":80,"@type":76},"The models used 172 intensive care unit variables and trained a LightGBM model with five-fold cross validation to predict the primary endpoint based on first verapamil administration versus no verapamil.",{"name":82,"@type":73,"acceptedAnswer":83},"What were the main performance results?",{"text":84,"@type":76},"The UCLA-trained model achieved an AUC of 0.88 more than a week in advance and ruled out verapamil for about 8% of non-verapamil patients with zero false negatives; 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