[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127938-en":3,"doc-seo-127938-105":31,"detail-sidebar-cat-0-en-105":92},{"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":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127938,687207024478,"Liam","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Progression to refractory status epilepticus - A machine learning analysis by means of classification and regression tree analysis","Background and Objectives: This study identifies predictors of progression to refractory status epilepticus (RSE) using machine learning. Methods: Consecutive patients aged ≥14 years with status epilepticus registered over nine years at Modena Academic Hospital were analyzed. Logistic regression and classification and regression tree (CART) were used to build a predictive model. Results: 705 patients were included; 33% progressed to RSE, which independently increased 30-day mortality. Key predictors included impaired consciousness, acute symptomatic hypoxic etiology, and periodic EEG patterns; the decision tree achieved 79.4% classification success and 94.1% accuracy for non-RSE cases.","Epilepsy & Behavior 161 (2024) 110005  \nContents lists available at ScienceDirect Epilepsy & Behavior  \njournal [homepage:](homepage: www.elsevier.com/locate/yebeh)[ www.elsevier.com/locate/yebeh](homepage: www.elsevier.com/locate/yebeh)  \n| Perspective\u003Cbr>Progression to refractory status epilepticus: A machine learning analysis by means of classification and regression tree analysis |  |  |\n| --- | --- | --- |\n| Stefano Melettia,b,*, Giada Giovanninia,f, Simona Lattanzid, Arian Zabolie, Niccol`o Orlandi a,b, Gianni Turcatoc, Francesco Brigoe\u003Cbr>a Neurophysiology Unit and Epilepsy Centre, Azienda Ospedaliera-Universitaria di Modena, Italy b Dept of Biomedical, Metabolic, and Neural Sciences, University of Modena and Reggio-Emilia, Italy c Hospital of Santorso (AULSS-7), Department of Internal Medicine, Santorso, Italy\u003Cbr>d Marche Polytechnic University, Neurological Clinic, Department of Experimental and Clinical Medicine, Ancona, Italy e Hospital of Merano-Meran (SABES-ASDAA), Department of Emergency Medicine, Merano-Meran, Italy f University of Modena and Reggio-Emilia, PhD Programm in Clinical and Experimental Medicine, Modena, Italy |  |  |\n| A R T I C L E I N F O | A B S T R A C T\u003Cbr>Background and Objectives: to identify predictors of progression to refractory status epilepticus (RSE) using a machine learning technique.\u003Cbr>Methods: Consecutive patients aged ≥ 14 years with SE registered in a 9-years period at Modena Academic Hospital were included in the analysis. We evaluated the risk of progression to RSE using logistic regression and a machine learning analysis by means of classification and regression tree analysis (CART) to develop a predictive model of progression to RSE.\u003Cbr>Results: 705 patients with SE were included in the study; of those, 33 %(233/705) evolved to RSE. The progression to RSE was an independent risk factor for 30-day mortality, with an OR adjusted for previously identified possible univariate confounders of 4.086 (CI 95 % 2.390–6.985; p \u003C 0.001). According to CART the most important variable predicting evolution to RSE was the impaired consciousness before treatment, followed by acute symptomatic hypoxic etiology and periodic EEG patterns. The decision tree identified 14 nodes with a risk of evolution to RSE ranging from 1.5 % to 90.8 %. The overall percentage of success in classifying patients of the decision tree was 79.4 %; the percentage of accurate prediction was high, 94.1 %, for those patients not progressing to RSE and moderate, 49.8 %, for patients evolving to RSE.\u003Cbr>Conclusions: Decision-tree analysis provided a meaningful risk stratification based on few variables that are easily obtained at SE first evaluation: consciousness before treatment, etiology, and severe EEG patterns. CART models must be viewed as potential new method for the stratification RSE at single subject level deserving further exploration and validation. |  |\n| This paper is based on a presentation given atthe 9th London-Inssbruck Colloquium on Status Epilepticus and Acute Seizures, in London in April 2024 |  |  |\n| Keywords: Machine learning Prediction\u003Cbr>Prognosis\u003Cbr>Refractory status epilepticus Super-refractory status epilepticus |  |  |\n\n1. Introduction  \nStatus epilepticus (SE) is a medical and neurological emergency that is currently defined by the International League Against Epilepsy (ILAE) as “a condition resulting either from the failure of the mechanisms responsible for seizure termination or from the initiation of mechanisms, which lead to abnormally, prolonged seizures” [1]. According to this definition, its age-and sex-adjusted incidence of a first SE episode is 36.1 (95 % confidence interval [CIs] 26.2–48.5) per 100 000 adults per year [2].  \nA prompt diagnosis and rapid and accurate treatment are mandatory to reduce the risk of negative long-term consequences, including high morbidity and mortality [3–5]. Usually, the pharmacological management of SE follows as stepwise approach, with benzodiazepines","cbCaiisn8BBfzESF","https://ap.wps.com/l/cbCaiisn8BBfzESF","pdf",1362413,3,1,7,"English","en",105,"# Introduction\n## Definition and clinical urgency of status epilepticus\n## Need for reliable predictors and prediction models\n# Methods\n## Study design, setting, and patients\n## Predictive modeling approach\n# Results\n## Patient cohort and RSE progression\n## CART decision tree performance and key predictors\n# Conclusions\n## Clinical risk stratification using few variables","[{\"question\":\"What was the study’s main goal regarding refractory status epilepticus?\",\"answer\":\"To identify predictors of progression to refractory status epilepticus (RSE) and develop a predictive model using machine learning (logistic regression and CART).\"},{\"question\":\"How were patients selected and what data were used for modeling?\",\"answer\":\"Consecutive patients aged ≥14 years with status epilepticus registered at Modena Academic Hospital over a nine-year period were included, and predictors were evaluated through logistic regression and CART.\"},{\"question\":\"Which variables were most important for predicting progression to RSE in the decision tree?\",\"answer\":\"The most important predictor was impaired consciousness before treatment, followed by acute symptomatic hypoxic etiology and periodic EEG patterns.\"}]","Progression to refractory status epilepticus - A machine learning analysis by means of classification and regression tree analysis | PDF",1785943103,18,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"progression-to-refractory-status-epilepticus-a-machine-learning-analysis-by-means-of-classification-and-regression-tree-analysis","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/progression-to-refractory-status-epilepticus-a-machine-learning-analysis-by-means-of-classification-and-regression-tree-analysis/127938/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-29","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the study’s main goal regarding refractory status epilepticus?","Question",{"text":76,"@type":77},"To identify predictors of progression to refractory status epilepticus (RSE) and develop a predictive model using machine learning (logistic regression and CART).","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were patients selected and what data were used for modeling?",{"text":81,"@type":77},"Consecutive patients aged ≥14 years with status epilepticus registered at Modena Academic Hospital over a nine-year period were included, and predictors were evaluated through logistic regression and CART.",{"name":83,"@type":74,"acceptedAnswer":84},"Which variables were most important for predicting progression to RSE in the decision tree?",{"text":85,"@type":77},"The most important predictor was impaired consciousness before treatment, followed by acute symptomatic hypoxic etiology and periodic EEG patterns.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":22,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]