{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,9,24]],"date-time":"2026-09-24T02:51:51Z","timestamp":1790218311889,"version":"4.1.0"},"reference-count":40,"publisher":"Wiley","issue":"2","license":[{"start":{"date-parts":[[2018,4,20]],"date-time":"2018-04-20T00:00:00Z","timestamp":1524182400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/100000002","name":"National Institutes of Health","doi-asserted-by":"publisher","award":["5T32\u2010CA096520\u201007"],"award-info":[{"award-number":["5T32\u2010CA096520\u201007"]}],"id":[{"id":"10.13039\/100000002","id-type":"DOI","asserted-by":"publisher"}]},{"DOI":"10.13039\/100000065","name":"National Institute of Neurological Disorders and Stroke","doi-asserted-by":"publisher","award":["NS044936"],"award-info":[{"award-number":["NS044936"]}],"id":[{"id":"10.13039\/100000065","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Epilepsia Open"],"published-print":{"date-parts":[[2018,6]]},"abstract":"<jats:title>Summary<\/jats:title><jats:sec><jats:title>Objective<\/jats:title><jats:p>A fundamental challenge in treating epilepsy is that changes in observed seizure frequencies do not necessarily reflect changes in underlying seizure risk. Rather, changes in seizure frequency may occur due to probabilistic variation around an underlying seizure risk state caused by normal fluctuations from natural history, leading to seizure unpredictability and potentially suboptimal medication adjustments in epilepsy management. However, no rigorous statistical approach exists to systematically distinguish expected changes in seizure frequency due to natural variability from changes in underlying seizure risk.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>Using data from SeizureTracker.com, a patient\u2010reported seizure diary tool containing over 1.2\u00a0million recorded seizures across 8\u00a0years, a novel epilepsy seizure risk assessment tool (EpiSAT) employing a Bayesian mixed\u2010effects hidden Markov model for zero\u2010inflated count data was developed to estimate changes in underlying seizure risk using patient\u2010reported seizure diary and clinical measurement data. Accuracy for correctly assessing underlying seizure risk was evaluated through a simulation comparison. Implications for the natural history of tuberous sclerosis complex (<jats:styled-content style=\"fixed-case\">TSC<\/jats:styled-content>) were assessed using data from SeizureTracker.com.<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>EpiSAT led to significant improvement in seizure risk assessment compared to traditional approaches relying solely on observed seizure frequencies. Applied to <jats:styled-content style=\"fixed-case\">TSC<\/jats:styled-content>, four underlying seizure risk states were identified. The expected duration of each state was &lt;12\u00a0months, providing a data\u2010driven estimate of the amount of time a person with <jats:styled-content style=\"fixed-case\">TSC<\/jats:styled-content> would be expected to remain at the same seizure risk level according to the natural course of epilepsy.<\/jats:p><\/jats:sec><jats:sec><jats:title>Significance<\/jats:title><jats:p>We propose a novel Bayesian statistical approach for evaluating seizure risk on an individual patient level using patient\u2010reported seizure diaries, which allows for the incorporation of external clinical variables to assess impact on seizure risk. This tool may improve the ability to distinguish true changes in seizure risk from natural variations in seizure frequency in clinical practice. Incorporation of systematic statistical approaches into antiepileptic drug (<jats:styled-content style=\"fixed-case\">AED<\/jats:styled-content>) management may help improve understanding of seizure unpredictability as well as timing of treatment interventions for people with epilepsy.<\/jats:p><\/jats:sec>","DOI":"10.1002\/epi4.12112","type":"journal-article","created":{"date-parts":[[2018,3,23]],"date-time":"2018-03-23T19:02:15Z","timestamp":1521831735000},"page":"236-246","update-policy":"https:\/\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":31,"title":["Epilepsy as a dynamic disease: A Bayesian model for differentiating seizure risk from natural variability"],"prefix":"10.1002","volume":"3","author":[{"given":"Sharon","family":"Chiang","sequence":"first","affiliation":[{"name":"School of Medicine Baylor College of Medicine Houston Texas U.S.A."},{"name":"Department of Statistics Rice University Houston Texas U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Marina","family":"Vannucci","sequence":"additional","affiliation":[{"name":"Department of Statistics Rice University Houston Texas U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8370-2758","authenticated-orcid":false,"given":"Daniel M.","family":"Goldenholz","sequence":"additional","affiliation":[{"name":"Division of Epilepsy Beth Israel Deaconess Medical Center Boston Massachusetts U.S.A."},{"name":"Clinical Epilepsy Section National Institute of Neurological Disorders and Stroke National Institutes of Health Bethesda Maryland U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Robert","family":"Moss","sequence":"additional","affiliation":[{"name":"SeizureTracker.com Alexandria Virginia U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"John M.","family":"Stern","sequence":"additional","affiliation":[{"name":"Department of Neurology University of California Los Angeles Los Angeles California U.S.A."}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2018,4,20]]},"reference":[{"key":"e_1_2_9_2_1","doi-asserted-by":"publisher","DOI":"10.1136\/adc.2011.211995"},{"key":"e_1_2_9_3_1","doi-asserted-by":"publisher","DOI":"10.1111\/j.1528-1167.2009.02474.x"},{"key":"e_1_2_9_4_1","volume-title":"Electroencephalography: basic principles, clinical applications, and related fields","author":"Niedermeyer E","year":"2005"},{"key":"e_1_2_9_5_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.886"},{"key":"e_1_2_9_6_1","doi-asserted-by":"publisher","DOI":"10.1002\/sim.2108"},{"key":"e_1_2_9_7_1","doi-asserted-by":"publisher","DOI":"10.1198\/016214507000000923"},{"key":"e_1_2_9_8_1","doi-asserted-by":"publisher","DOI":"10.1214\/13-AOAS705"},{"key":"e_1_2_9_9_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2013.12.063"},{"key":"e_1_2_9_10_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.neuroimage.2015.10.070"},{"key":"e_1_2_9_11_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10928-012-9248-2"},{"key":"e_1_2_9_12_1","doi-asserted-by":"publisher","DOI":"10.2307\/2532758"},{"key":"e_1_2_9_13_1","doi-asserted-by":"publisher","DOI":"10.1111\/epi.13291"},{"key":"e_1_2_9_14_1","doi-asserted-by":"crossref","unstructured":"CallutJ DupontP.Inducing hidden Markov models to model long\u2010term dependencies. 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