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It highlights acquired epilepsies after stroke, traumatic brain injury, or CNS infections, where recurrences cluster within 1–2 years and decline sharply. Dynamic tools such as COSY and validated prognostic models provide recalculable near-term estimates, supporting communication, NNT-informed thresholds, and trial recruitment, while addressing remaining barriers including guideline integration gaps, limited external validation, and the “Oedipus effect.”",{"@graph":69,"@context":122},[70,84,105],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/epilepsy-as-a-dynamic-disease-toward-actionable-individualized-seizure-risk-prediction/456183/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/epilepsy-as-a-dynamic-disease-toward-actionable-individualized-seizure-risk-prediction/456183.png","ImageObject",300,407,{"name":92,"@type":93},"Mimi","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-11-04","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":44},"InteractionCounter",{"@type":104},"ViewAction",{"@type":106,"mainEntity":107},"FAQPage",[108,114,118],{"name":109,"@type":110,"acceptedAnswer":111},"Why does the current 10-year risk definition of epilepsy pose clinical challenges?","Question",{"text":112,"@type":113},"Because many decisions rely on shorter time horizons, while static long-term thresholds may not reflect near-term recurrence dynamics.","Answer",{"name":115,"@type":110,"acceptedAnswer":116},"How do dynamic risk tools like COSY improve seizure risk prediction?",{"text":117,"@type":113},"They provide recalculable, near-term estimates that reflect evolving patient status, improving communication and decision-making.",{"name":119,"@type":110,"acceptedAnswer":120},"What limitations and concerns remain when using probabilistic seizure predictions?",{"text":121,"@type":113},"Barriers include limited guideline integration, gaps in external validation, and the “Oedipus effect,”where predictions can change patient behavior and outcomes.","https://schema.org",{"og:url":83,"og:type":124,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":126,"canonical":83},"index,follow",{"doc_id":128,"site_id":62},456183,1791223893,{"code":4,"msg":5,"data":131},{"doc_id":128,"user_id":132,"nickname":92,"user_avatar":133,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":134,"file_id":135,"file_url":136,"file_type":137,"file_size":138,"view_count":44,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":139,"language":140,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":141,"faqs":142,"seo_title":143,"seo_description":67,"update_tm":144,"read_time":145,"preview_page_count":4},2336477974920,"https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d","DOI: 10.1111/epi.18602  \nC OMMENTARY  \nEpilepsy as a dynamic disease: Toward actionable, individualized seizure risk prediction  \nKai Michael Schubert1  | Anthony G. Marson2,3  | Eugen Trinka4,5,6  | Marian Galovic1   \n1Department of Neurology, Clinical Neuroscience Center, University Hospital and University of Zurich, Zurich, Switzerland  \n2Institute of Systems, Molecular and Integrative Biology, Pharmacology, and Therapeutics, University of Liverpool, Liverpool, UK  \n3Walton Centre NHS Foundation Trust, Liverpool, UK  \n4Department of Neurology, Neurocritical Care, and Neurorehabilitation, Christian Doppler University Hospital, member of European Reference Network EpiCARE, and Center for Cognitive Neuroscience, Paracelsus Medical University, Salzburg, Austria 5Neuroscience Institute, Christian Doppler University Hospital, Center for Cognitive Neuroscience, Salzburg, Austria  \n6Karl Landsteiner Institute for Clinical Neurosciences, Salzburg, Austria  \nCorrespondence  \nKai Michael Schubert, Department of Neurology, University Hospital Zurich, Frauenklinikstrasse 26, 8091 Zurich, Switzerland.  \nEmail: kaimichael.schubert@usz.ch  \nAbstract  \nThe current definition of epilepsy allows diagnosis after a single unprovoked seizure if the estimated 10-year recurrence risk is ≥60% . While this framework is grounded in epidemiological evidence, it does not align with the shorter time horizons that guide many clinical and personal decisions. In acquired epilepsies, such as those following stroke, traumatic brain injury, or CNS infections, most recurrences occur within 1–2 years, with risk declining sharply thereafter. This temporal clustering challenges the use of static, long-term risk thresholds in isolation. Dynamic tools, such as the Chance of an Occurrence of a Seizure in the Next Year (COSY) and validated prognostic models (e.g., SeLECT, CAVE, RISE), offer recalculable, near-term estimates that reflect evolving patient status. These metrics can improve communication, inform treatment thresholds through Number Needed to Treat (NNT) calculations, and enhance clinical trial recruitment by targeting periods of highest risk. However, barriers remain, including limited integration into guidelines, gaps in external validation, and the “Oedipus effect,”where probabilistic predictions influence patient behavior, treatment decisions, and research outcomes. Incorporating individualized, time-sensitive risk prediction into clinical frameworks may better align diagnostic definitions with patient needs, reduce overtreatment, and optimize both everyday care and research in epilepsy prevention and management.  \nKEYWORDS  \nacquired epilepsy, COSY, individualized prediction, number needed to treat, prognostic models, seizure recurrence risk  \nIn 2005, the International League Against Epilepsy defined epilepsy conceptually as a “a disorder of the brain characterized by an enduring predisposition to generate epileptic seizures, and by the neurobiologic, cognitive, psychological, and social consequences of this condition.”  \nThis conceptual definition was operationalized in 2014, allowing a diagnosis after a single unprovoked seizure if the 10-year recurrence risk is ≥60%—a threshold based on epidemiological data and comparable to risk after two seizures.1,2 This captures elevated risk after events like  \nThis is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n© 2025 The Author(s). Epilepsia published by Wiley Periodicals LLC on behalf of International League Against Epilepsy.  \nstroke, traumatic brain injury, and central nervous system infections.  \nThe 10-year threshold of 60% recurrence risk is grounded in epidemiological data and may not address shorter time frames that drive day-to-day clinical decisions. In practice, patients and clinicians often focus on near-term risk of recurrence—over days, months,","cbCaij7CmaWUriZt","https://ap.wps.com/l/cbCaij7CmaWUriZt","pdf",3147938,13,"English","# Abstract\n# The Challenge of Diagnosing Epilepsy Based on Future Risk","[{\"question\":\"Why does the current 10-year risk definition of epilepsy pose clinical challenges?\",\"answer\":\"Because many decisions rely on shorter time horizons, while static long-term thresholds may not reflect near-term recurrence dynamics.\"},{\"question\":\"How do dynamic risk tools like COSY improve seizure risk prediction?\",\"answer\":\"They provide recalculable, near-term estimates that reflect evolving patient status, improving communication and decision-making.\"},{\"question\":\"What limitations and concerns remain when using probabilistic seizure predictions?\",\"answer\":\"Barriers include limited guideline integration, gaps in external validation, and the “Oedipus effect,”where predictions can change patient behavior and outcomes.\"}]","Epilepsy as a dynamic disease - Toward actionable, individualized seizure risk prediction | PDF",1790745295,33]