Prediction tools and risk stratification in epilepsy surgery
Objective This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (Intern...
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| Veröffentlicht in: | Epilepsia (Copenhagen) 2024-02, Vol.65 (2), p.414-421 |
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| creator | Hadady, Levente Sperling, Michael R. Alcala‐Zermeno, Juan Luis French, Jacqueline A. Dugan, Patricia Jehi, Lara Fabó, Dániel Klivényi, Péter Rubboli, Guido Beniczky, Sándor |
| description | Objective
This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2).
Methods
We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3–4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%–70%), and low (ESGS = 2, SFS = 0–1, ESNs |
| doi_str_mv | 10.1111/epi.17851 |
| format | Article |
| fullrecord | <record><control><sourceid>proquest_cross</sourceid><recordid>TN_cdi_proquest_miscellaneous_2902968760</recordid><sourceformat>XML</sourceformat><sourcesystem>PC</sourcesystem><sourcerecordid>2902968760</sourcerecordid><originalsourceid>FETCH-LOGICAL-c3481-cea2010e1253d8bbfcb394f1bb3bee2e7de772a11007041688adec663d33c24a3</originalsourceid><addsrcrecordid>eNp1kD9PwzAQxS0EoqUw8AVQJBYY0vrs_HEkFlQVqFSJDjBbjnNBLmlS7EQo3x7TFAYkbrnhfnrv3iPkEugU_MxwZ6aQihiOyBhiJkKAJD0mY0qBh1ks6IicObehlKZJyk_JiAuaUB7DmNytLRZGt6apg7ZpKheougisce-Ba61qTWm02l9NHXifCneuD1xn39D25-SkVJXDi8OekNeHxcv8KVw9Py7n96tQ80hAqFExChSBxbwQeV7qnGdRCXnOc0SGaYFpyhSA_49GkAihCtRJwgvONYsUn5CbQXdnm48OXSu3xmmsKlVj0znJMsqyRKQ-04Rc_0E3TWdr_52nvL_IADJP3Q6Uto1zFku5s2arbC-Byu9KpY8q95V69uqg2OVbLH7Jnw49MBuAT99O_7-SXKyXg-QXo0B_Mg</addsrcrecordid><sourcetype>Aggregation Database</sourcetype><iscdi>true</iscdi><recordtype>article</recordtype><pqid>2925389119</pqid></control><display><type>article</type><title>Prediction tools and risk stratification in epilepsy surgery</title><source>MEDLINE</source><source>Access via Wiley Online Library</source><creator>Hadady, Levente ; Sperling, Michael R. ; Alcala‐Zermeno, Juan Luis ; French, Jacqueline A. ; Dugan, Patricia ; Jehi, Lara ; Fabó, Dániel ; Klivényi, Péter ; Rubboli, Guido ; Beniczky, Sándor</creator><creatorcontrib>Hadady, Levente ; Sperling, Michael R. ; Alcala‐Zermeno, Juan Luis ; French, Jacqueline A. ; Dugan, Patricia ; Jehi, Lara ; Fabó, Dániel ; Klivényi, Péter ; Rubboli, Guido ; Beniczky, Sándor</creatorcontrib><description>Objective
This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2).
Methods
We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3–4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%–70%), and low (ESGS = 2, SFS = 0–1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures.
Results
The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05).
Significance
ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.</description><identifier>ISSN: 0013-9580</identifier><identifier>EISSN: 1528-1167</identifier><identifier>DOI: 10.1111/epi.17851</identifier><identifier>PMID: 38060351</identifier><language>eng</language><publisher>United States: Wiley Subscription Services, Inc</publisher><subject>Convulsions & seizures ; EEG ; Epilepsy ; Epilepsy - diagnosis ; Epilepsy - surgery ; Humans ; Nomograms ; prediction tools ; Risk Assessment ; risk stratification ; Seizures ; Seizures - surgery ; Surgery ; Treatment Outcome</subject><ispartof>Epilepsia (Copenhagen), 2024-02, Vol.65 (2), p.414-421</ispartof><rights>2023 International League Against Epilepsy.</rights><rights>Copyright © 2024 International League Against Epilepsy</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><cites>FETCH-LOGICAL-c3481-cea2010e1253d8bbfcb394f1bb3bee2e7de772a11007041688adec663d33c24a3</cites><orcidid>0000-0003-0708-6006 ; 0000-0001-5141-5351 ; 0000-0003-3143-965X ; 0000-0002-5309-2514 ; 0000-0002-6035-6581 ; 0000-0003-2242-8027 ; 0000-0001-6199-1870 ; 0000-0002-8041-6377</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://onlinelibrary.wiley.com/doi/pdf/10.1111%2Fepi.17851$$EPDF$$P50$$Gwiley$$H</linktopdf><linktohtml>$$Uhttps://onlinelibrary.wiley.com/doi/full/10.1111%2Fepi.17851$$EHTML$$P50$$Gwiley$$H</linktohtml><link.rule.ids>315,781,785,1418,27929,27930,45579,45580</link.rule.ids><backlink>$$Uhttps://www.ncbi.nlm.nih.gov/pubmed/38060351$$D View this record in MEDLINE/PubMed$$Hfree_for_read</backlink></links><search><creatorcontrib>Hadady, Levente</creatorcontrib><creatorcontrib>Sperling, Michael R.</creatorcontrib><creatorcontrib>Alcala‐Zermeno, Juan Luis</creatorcontrib><creatorcontrib>French, Jacqueline A.</creatorcontrib><creatorcontrib>Dugan, Patricia</creatorcontrib><creatorcontrib>Jehi, Lara</creatorcontrib><creatorcontrib>Fabó, Dániel</creatorcontrib><creatorcontrib>Klivényi, Péter</creatorcontrib><creatorcontrib>Rubboli, Guido</creatorcontrib><creatorcontrib>Beniczky, Sándor</creatorcontrib><title>Prediction tools and risk stratification in epilepsy surgery</title><title>Epilepsia (Copenhagen)</title><addtitle>Epilepsia</addtitle><description>Objective
This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2).
Methods
We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3–4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%–70%), and low (ESGS = 2, SFS = 0–1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures.
Results
The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05).
Significance
ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.</description><subject>Convulsions & seizures</subject><subject>EEG</subject><subject>Epilepsy</subject><subject>Epilepsy - diagnosis</subject><subject>Epilepsy - surgery</subject><subject>Humans</subject><subject>Nomograms</subject><subject>prediction tools</subject><subject>Risk Assessment</subject><subject>risk stratification</subject><subject>Seizures</subject><subject>Seizures - surgery</subject><subject>Surgery</subject><subject>Treatment Outcome</subject><issn>0013-9580</issn><issn>1528-1167</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2024</creationdate><recordtype>article</recordtype><sourceid>EIF</sourceid><recordid>eNp1kD9PwzAQxS0EoqUw8AVQJBYY0vrs_HEkFlQVqFSJDjBbjnNBLmlS7EQo3x7TFAYkbrnhfnrv3iPkEugU_MxwZ6aQihiOyBhiJkKAJD0mY0qBh1ks6IicObehlKZJyk_JiAuaUB7DmNytLRZGt6apg7ZpKheougisce-Ba61qTWm02l9NHXifCneuD1xn39D25-SkVJXDi8OekNeHxcv8KVw9Py7n96tQ80hAqFExChSBxbwQeV7qnGdRCXnOc0SGaYFpyhSA_49GkAihCtRJwgvONYsUn5CbQXdnm48OXSu3xmmsKlVj0znJMsqyRKQ-04Rc_0E3TWdr_52nvL_IADJP3Q6Uto1zFku5s2arbC-Byu9KpY8q95V69uqg2OVbLH7Jnw49MBuAT99O_7-SXKyXg-QXo0B_Mg</recordid><startdate>202402</startdate><enddate>202402</enddate><creator>Hadady, Levente</creator><creator>Sperling, Michael R.</creator><creator>Alcala‐Zermeno, Juan Luis</creator><creator>French, Jacqueline A.</creator><creator>Dugan, Patricia</creator><creator>Jehi, Lara</creator><creator>Fabó, Dániel</creator><creator>Klivényi, Péter</creator><creator>Rubboli, Guido</creator><creator>Beniczky, Sándor</creator><general>Wiley Subscription Services, Inc</general><scope>CGR</scope><scope>CUY</scope><scope>CVF</scope><scope>ECM</scope><scope>EIF</scope><scope>NPM</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7TK</scope><scope>7X8</scope><orcidid>https://orcid.org/0000-0003-0708-6006</orcidid><orcidid>https://orcid.org/0000-0001-5141-5351</orcidid><orcidid>https://orcid.org/0000-0003-3143-965X</orcidid><orcidid>https://orcid.org/0000-0002-5309-2514</orcidid><orcidid>https://orcid.org/0000-0002-6035-6581</orcidid><orcidid>https://orcid.org/0000-0003-2242-8027</orcidid><orcidid>https://orcid.org/0000-0001-6199-1870</orcidid><orcidid>https://orcid.org/0000-0002-8041-6377</orcidid></search><sort><creationdate>202402</creationdate><title>Prediction tools and risk stratification in epilepsy surgery</title><author>Hadady, Levente ; Sperling, Michael R. ; Alcala‐Zermeno, Juan Luis ; French, Jacqueline A. ; Dugan, Patricia ; Jehi, Lara ; Fabó, Dániel ; Klivényi, Péter ; Rubboli, Guido ; Beniczky, Sándor</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c3481-cea2010e1253d8bbfcb394f1bb3bee2e7de772a11007041688adec663d33c24a3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2024</creationdate><topic>Convulsions & seizures</topic><topic>EEG</topic><topic>Epilepsy</topic><topic>Epilepsy - diagnosis</topic><topic>Epilepsy - surgery</topic><topic>Humans</topic><topic>Nomograms</topic><topic>prediction tools</topic><topic>Risk Assessment</topic><topic>risk stratification</topic><topic>Seizures</topic><topic>Seizures - surgery</topic><topic>Surgery</topic><topic>Treatment Outcome</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Hadady, Levente</creatorcontrib><creatorcontrib>Sperling, Michael R.</creatorcontrib><creatorcontrib>Alcala‐Zermeno, Juan Luis</creatorcontrib><creatorcontrib>French, Jacqueline A.</creatorcontrib><creatorcontrib>Dugan, Patricia</creatorcontrib><creatorcontrib>Jehi, Lara</creatorcontrib><creatorcontrib>Fabó, Dániel</creatorcontrib><creatorcontrib>Klivényi, Péter</creatorcontrib><creatorcontrib>Rubboli, Guido</creatorcontrib><creatorcontrib>Beniczky, Sándor</creatorcontrib><collection>Medline</collection><collection>MEDLINE</collection><collection>MEDLINE (Ovid)</collection><collection>MEDLINE</collection><collection>MEDLINE</collection><collection>PubMed</collection><collection>CrossRef</collection><collection>Neurosciences Abstracts</collection><collection>MEDLINE - Academic</collection><jtitle>Epilepsia (Copenhagen)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Hadady, Levente</au><au>Sperling, Michael R.</au><au>Alcala‐Zermeno, Juan Luis</au><au>French, Jacqueline A.</au><au>Dugan, Patricia</au><au>Jehi, Lara</au><au>Fabó, Dániel</au><au>Klivényi, Péter</au><au>Rubboli, Guido</au><au>Beniczky, Sándor</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Prediction tools and risk stratification in epilepsy surgery</atitle><jtitle>Epilepsia (Copenhagen)</jtitle><addtitle>Epilepsia</addtitle><date>2024-02</date><risdate>2024</risdate><volume>65</volume><issue>2</issue><spage>414</spage><epage>421</epage><pages>414-421</pages><issn>0013-9580</issn><eissn>1528-1167</eissn><abstract>Objective
This study was undertaken to conduct external validation of previously published epilepsy surgery prediction tools using a large independent multicenter dataset and to assess whether these tools can stratify patients for being operated on and for becoming free of disabling seizures (International League Against Epilepsy stage 1 and 2).
Methods
We analyzed a dataset of 1562 patients, not used for tool development. We applied two scales: Epilepsy Surgery Grading Scale (ESGS) and Seizure Freedom Score (SFS); and two versions of Epilepsy Surgery Nomogram (ESN): the original version and the modified version, which included electroencephalographic data. For the ESNs, we used calibration curves and concordance indexes. We stratified the patients into three tiers for assessing the chances of attaining freedom from disabling seizures after surgery: high (ESGS = 1, SFS = 3–4, ESNs > 70%), moderate (ESGS = 2, SFS = 2, ESNs = 40%–70%), and low (ESGS = 2, SFS = 0–1, ESNs < 40%). We compared the three tiers as stratified by these tools, concerning the proportion of patients who were operated on, and for the proportion of patients who became free of disabling seizures.
Results
The concordance indexes for the various versions of the nomograms were between .56 and .69. Both scales (ESGS, SFS) and nomograms accurately stratified the patients for becoming free of disabling seizures, with significant differences among the three tiers (p < .05). In addition, ESGS and the modified ESN accurately stratified the patients for having been offered surgery, with significant difference among the three tiers (p < .05).
Significance
ESGS and the modified ESN (at thresholds of 40% and 70%) stratify patients undergoing presurgical evaluation into three tiers, with high, moderate, and low chance for favorable outcome, with significant differences between the groups concerning having surgery and becoming free of disabling seizures. Stratifying patients for epilepsy surgery has the potential to help select the optimal candidates in underprivileged areas and better allocate resources in developed countries.</abstract><cop>United States</cop><pub>Wiley Subscription Services, Inc</pub><pmid>38060351</pmid><doi>10.1111/epi.17851</doi><tpages>8</tpages><orcidid>https://orcid.org/0000-0003-0708-6006</orcidid><orcidid>https://orcid.org/0000-0001-5141-5351</orcidid><orcidid>https://orcid.org/0000-0003-3143-965X</orcidid><orcidid>https://orcid.org/0000-0002-5309-2514</orcidid><orcidid>https://orcid.org/0000-0002-6035-6581</orcidid><orcidid>https://orcid.org/0000-0003-2242-8027</orcidid><orcidid>https://orcid.org/0000-0001-6199-1870</orcidid><orcidid>https://orcid.org/0000-0002-8041-6377</orcidid><oa>free_for_read</oa></addata></record> |
| fulltext | fulltext |
| identifier | ISSN: 0013-9580 |
| ispartof | Epilepsia (Copenhagen), 2024-02, Vol.65 (2), p.414-421 |
| issn | 0013-9580 1528-1167 |
| language | eng |
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| source | MEDLINE; Access via Wiley Online Library |
| subjects | Convulsions & seizures EEG Epilepsy Epilepsy - diagnosis Epilepsy - surgery Humans Nomograms prediction tools Risk Assessment risk stratification Seizures Seizures - surgery Surgery Treatment Outcome |
| title | Prediction tools and risk stratification in epilepsy surgery |
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