Characteristics of large patient-reported outcomes: Where can one million seizures get us?
To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers. Zero-inflated negative binomial mixed-effects models were used to evaluate temporal pa...
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| Veröffentlicht in: | Epilepsia open 2018-09, Vol.3 (3), p.364-373 |
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| creator | Ferastraoaru, Victor Goldenholz, Daniel M Chiang, Sharon Moss, Robert Theodore, William H Haut, Sheryl R |
| description | To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.
Zero-inflated negative binomial mixed-effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed-effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero-inflated negative binomial and logistic mixed-effects models, respectively.
A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p 5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: "Overtired or irregular sleep," "Bright or flashing lights," and "Emotional stress" (p |
| doi_str_mv | 10.1002/epi4.12237 |
| format | Article |
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Zero-inflated negative binomial mixed-effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed-effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero-inflated negative binomial and logistic mixed-effects models, respectively.
A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: "Overtired or irregular sleep," "Bright or flashing lights," and "Emotional stress" (p < 0.004).
This study explored a large cohort of patients with self-reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient-recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.</description><identifier>ISSN: 2470-9239</identifier><identifier>EISSN: 2470-9239</identifier><identifier>DOI: 10.1002/epi4.12237</identifier><identifier>PMID: 30187007</identifier><language>eng</language><publisher>United States: John Wiley & Sons, Inc</publisher><subject>Adults ; Alcohol ; Big Data ; Brain cancer ; Circadian rhythm ; Clustering ; Convulsions & seizures ; Diaries ; Epilepsy ; Etiology ; Full‐Length Original Research ; Patients ; Software</subject><ispartof>Epilepsia open, 2018-09, Vol.3 (3), p.364-373</ispartof><rights>2018. This work is published under http://creativecommons.org/licenses/by-nc-nd/4.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><rights>2018 The Authors. published by Wiley Periodicals Inc. on behalf of International League Against Epilepsy.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c3497-3914c82c72e91b214faf5375de2db064ac2824d4ed17e371184b992babf041c53</citedby><cites>FETCH-LOGICAL-c3497-3914c82c72e91b214faf5375de2db064ac2824d4ed17e371184b992babf041c53</cites><orcidid>0000-0002-8370-2758</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6119749/pdf/$$EPDF$$P50$$Gpubmedcentral$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.ncbi.nlm.nih.gov/pmc/articles/PMC6119749/$$EHTML$$P50$$Gpubmedcentral$$Hfree_for_read</linktohtml><link.rule.ids>230,314,723,776,780,860,881,27901,27902,53766,53768</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/30187007$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Ferastraoaru, Victor</creatorcontrib><creatorcontrib>Goldenholz, Daniel M</creatorcontrib><creatorcontrib>Chiang, Sharon</creatorcontrib><creatorcontrib>Moss, Robert</creatorcontrib><creatorcontrib>Theodore, William H</creatorcontrib><creatorcontrib>Haut, Sheryl R</creatorcontrib><title>Characteristics of large patient-reported outcomes: Where can one million seizures get us?</title><title>Epilepsia open</title><addtitle>Epilepsia Open</addtitle><description>To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.
Zero-inflated negative binomial mixed-effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed-effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero-inflated negative binomial and logistic mixed-effects models, respectively.
A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: "Overtired or irregular sleep," "Bright or flashing lights," and "Emotional stress" (p < 0.004).
This study explored a large cohort of patients with self-reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient-recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.</description><subject>Adults</subject><subject>Alcohol</subject><subject>Big Data</subject><subject>Brain cancer</subject><subject>Circadian rhythm</subject><subject>Clustering</subject><subject>Convulsions & seizures</subject><subject>Diaries</subject><subject>Epilepsy</subject><subject>Etiology</subject><subject>Full‐Length Original Research</subject><subject>Patients</subject><subject>Software</subject><issn>2470-9239</issn><issn>2470-9239</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2018</creationdate><recordtype>article</recordtype><sourceid>BENPR</sourceid><recordid>eNpdkU1LxDAQhoMoKurFHyABLyJUM0l203hQZPELBC-K4CWk6XQ30jY1aQX99dZP1FMG8vDyzjyEbAM7AMb4IXZeHgDnQi2RdS4VyzQXevnXvEa2UnpkjIHmAFO2StYEg1wxptbJw2xho3U9Rp967xINFa1tnCPtbO-x7bOIXYg9ljQMvQsNpiN6v8CI1NmWhhZp4-vah5Ym9K9DxETn2NMhnWySlcrWCbe-3g1yd352O7vMrm8urman15kTUqtMaJAu505x1FBwkJWtJkJNSuRlwabSOp5zWUosQaFQALkstOaFLSomwU3EBjn-zO2GosHSjaWjrU0XfWPjiwnWm78_rV-YeXg2UwCtpB4D9r4CYngaMPWm8clhXdsWw5AMHw8tBAgJI7r7D30MQ2zH9QwXPBdKq4kcqf1PysWQUsTqpwww827NvFszH9ZGeOd3_R_025F4A3PFkrM</recordid><startdate>20180901</startdate><enddate>20180901</enddate><creator>Ferastraoaru, Victor</creator><creator>Goldenholz, Daniel M</creator><creator>Chiang, Sharon</creator><creator>Moss, Robert</creator><creator>Theodore, William H</creator><creator>Haut, Sheryl R</creator><general>John Wiley & Sons, Inc</general><general>John Wiley and Sons Inc</general><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>3V.</scope><scope>7X7</scope><scope>7XB</scope><scope>8FI</scope><scope>8FJ</scope><scope>8FK</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>FYUFA</scope><scope>GHDGH</scope><scope>K9.</scope><scope>M0S</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>7X8</scope><scope>5PM</scope><orcidid>https://orcid.org/0000-0002-8370-2758</orcidid></search><sort><creationdate>20180901</creationdate><title>Characteristics of large patient-reported outcomes: Where can one million seizures get us?</title><author>Ferastraoaru, Victor ; Goldenholz, Daniel M ; Chiang, Sharon ; Moss, Robert ; Theodore, William H ; Haut, Sheryl R</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3497-3914c82c72e91b214faf5375de2db064ac2824d4ed17e371184b992babf041c53</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2018</creationdate><topic>Adults</topic><topic>Alcohol</topic><topic>Big Data</topic><topic>Brain cancer</topic><topic>Circadian rhythm</topic><topic>Clustering</topic><topic>Convulsions & seizures</topic><topic>Diaries</topic><topic>Epilepsy</topic><topic>Etiology</topic><topic>Full‐Length Original Research</topic><topic>Patients</topic><topic>Software</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Ferastraoaru, Victor</creatorcontrib><creatorcontrib>Goldenholz, Daniel M</creatorcontrib><creatorcontrib>Chiang, Sharon</creatorcontrib><creatorcontrib>Moss, Robert</creatorcontrib><creatorcontrib>Theodore, William H</creatorcontrib><creatorcontrib>Haut, Sheryl R</creatorcontrib><collection>PubMed</collection><collection>CrossRef</collection><collection>ProQuest Central (Corporate)</collection><collection>Health & Medical Collection</collection><collection>ProQuest Central (purchase pre-March 2016)</collection><collection>Hospital Premium Collection</collection><collection>Hospital Premium Collection (Alumni Edition)</collection><collection>ProQuest Central (Alumni) (purchase pre-March 2016)</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>Health Research Premium Collection</collection><collection>Health Research Premium Collection (Alumni)</collection><collection>ProQuest Health & Medical Complete (Alumni)</collection><collection>Health & Medical Collection (Alumni Edition)</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>MEDLINE - Academic</collection><collection>PubMed Central (Full Participant titles)</collection><jtitle>Epilepsia open</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Ferastraoaru, Victor</au><au>Goldenholz, Daniel M</au><au>Chiang, Sharon</au><au>Moss, Robert</au><au>Theodore, William H</au><au>Haut, Sheryl R</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Characteristics of large patient-reported outcomes: Where can one million seizures get us?</atitle><jtitle>Epilepsia open</jtitle><addtitle>Epilepsia Open</addtitle><date>2018-09-01</date><risdate>2018</risdate><volume>3</volume><issue>3</issue><spage>364</spage><epage>373</epage><pages>364-373</pages><issn>2470-9239</issn><eissn>2470-9239</eissn><abstract>To analyze data from Seizure Tracker, a large electronic seizure diary, including comparison of seizure characteristics among different etiologies, temporal patterns in seizure fluctuations, and specific triggers.
Zero-inflated negative binomial mixed-effects models were used to evaluate temporal patterns of seizure events (during the day or week), as well as group differences in monthly seizure frequency between children and adults and between etiologies. The association of long seizures with seizure triggers was evaluated using a mixed-effects logistic model with subject as the random effect. Incidence rate ratios (IRRs) and odds ratios were reported for analyses involving zero-inflated negative binomial and logistic mixed-effects models, respectively.
A total of 1,037,909 seizures were logged by 10,186 subjects (56.7% children) from December 2007 to January 2016. Children had more frequent seizures than adults did (median monthly seizure frequency 3.5 vs. 2.7, IRR 1.26; p < 0.001). Seizures demonstrated a circadian pattern (higher frequency between 07:00 a.m. and 10:00 a.m. and lower overnight), and seizures were reported differentially across the week (seizure rates higher Monday through Friday than Saturday or Sunday). Longer seizures (>5 or >30 min) had a higher proportion of the following triggers when compared with shorter seizures: "Overtired or irregular sleep," "Bright or flashing lights," and "Emotional stress" (p < 0.004).
This study explored a large cohort of patients with self-reported seizures; strengths and limitations of large seizure diary databases are discussed. The findings in this study are consistent with those of prior work in smaller validated cohorts, suggesting that patient-recorded databases are a valuable resource for epilepsy research, capable of both replication of results and generation of novel hypotheses.</abstract><cop>United States</cop><pub>John Wiley & Sons, Inc</pub><pmid>30187007</pmid><doi>10.1002/epi4.12237</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0002-8370-2758</orcidid><oa>free_for_read</oa></addata></record> |
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| ispartof | Epilepsia open, 2018-09, Vol.3 (3), p.364-373 |
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| language | eng |
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| source | Wiley Online Library Open Access; DOAJ Directory of Open Access Journals; Wiley Online Library Journals Frontfile Complete; Elektronische Zeitschriftenbibliothek - Frei zugängliche E-Journals; PubMed Central |
| subjects | Adults Alcohol Big Data Brain cancer Circadian rhythm Clustering Convulsions & seizures Diaries Epilepsy Etiology Full‐Length Original Research Patients Software |
| title | Characteristics of large patient-reported outcomes: Where can one million seizures get us? |
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