Ruba Al-Ramadhani, Ann Hyslop, Avery R. Caraway, Edward J. Novotny, Adam P. Ostendorf, Krista L. Eschbach, Allyson L. Alexander, Lily C. Wong-Kisiel, Dewi F. Depositario-Cabacar, Chima O. Oluigbo, Cemal Karakas, Samir R. Karia, Priyamvada Tatachar, Jeffrey Bolton, Pilar D. Pichon, Daniel W. Shrey, Erin Fedak Romanowski, Nancy A. McNamara, Ernesto Gonzalez-Giraldo, Kurtis Auguste, Danilo Bernardo, Rani K. Singh, Pradeep K. Javarayee, Jenny J. Lin, Jason C. Coryell, Shilpa B. Reddy, Abhinaya Ganesh, Michael A. Ciliberto, Debopam Samanta, Kristen H. Arredondo, Ahmad Marashly, Zachary M. Grinspan, Dallas Armstrong, Taylor J. Abel, Janelle Wagner, Derryl J. Miller, Fernando N. Galan, Michael Scott Perry
{"title":"影响小儿癫痫手术从开始手术评估到最终干预持续时间的因素","authors":"Ruba Al-Ramadhani, Ann Hyslop, Avery R. Caraway, Edward J. Novotny, Adam P. Ostendorf, Krista L. Eschbach, Allyson L. Alexander, Lily C. Wong-Kisiel, Dewi F. Depositario-Cabacar, Chima O. Oluigbo, Cemal Karakas, Samir R. Karia, Priyamvada Tatachar, Jeffrey Bolton, Pilar D. Pichon, Daniel W. Shrey, Erin Fedak Romanowski, Nancy A. McNamara, Ernesto Gonzalez-Giraldo, Kurtis Auguste, Danilo Bernardo, Rani K. Singh, Pradeep K. Javarayee, Jenny J. Lin, Jason C. Coryell, Shilpa B. Reddy, Abhinaya Ganesh, Michael A. Ciliberto, Debopam Samanta, Kristen H. Arredondo, Ahmad Marashly, Zachary M. Grinspan, Dallas Armstrong, Taylor J. Abel, Janelle Wagner, Derryl J. Miller, Fernando N. Galan, Michael Scott Perry","doi":"10.1002/cns3.70027","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Rationale</h3>\n \n <p>Longer duration of epilepsy before surgery is a predictor of poor outcome. While referral delays of surgical candidates are well documented, factors causing delay during the presurgical evaluation remain unclear and may vary depending on institutional characteristics. By benchmarking the duration of presurgical evaluation across multiple centers and identifying patient and evaluation characteristics contributing to duration, we can ascertain best practices and address modifiable contributors to reduce delays.</p>\n </section>\n \n <section>\n \n <h3> Methods</h3>\n \n <p>We queried the Pediatric Epilepsy Research Consortium Surgery Database, a prospective, observational multicenter study enrolling children 0–18 years at 27 US pediatric epilepsy centers, for all patients undergoing initial presurgical evaluation for drug-resistant epilepsy (DRE). We included patients with completed evaluations and data on duration from initiation of presurgical evaluation to final surgical decision. We compared patient characteristics and evaluation components between those with long duration evaluations (> 75% quartile) and those with short evaluations (< 25% quartile). Akaike information criteria selection identified variables associated with longer duration. From these, we developed a logistic prediction model for evaluation duration, using a random 80/20 training/testing split of the entire cohort. The model was tested among institutions with ≥ 10 patients in the cohort to assess its accuracy in predicting long durations. Linear models for each site assessed each variable's impact on duration. Variables with < 10% of the patient population at each site were excluded. Beta values were compared to identify intra- and inter-institution variability and to delineate institutions with the shortest added duration for each variable.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>Of 2318 patients undergoing surgical evaluation, 1655 (71%) from 23 sites had complete data. Median evaluation duration was 8 weeks (interquartile range 3–22); 453 (27%) were short-duration evaluations and 414 (25%) were long-duration evaluations. Multiple patient and evaluation characteristics were associated with duration (Table 1). Table 6 provides the average duration each variable contributes to evaluation by site, highlighting the shortest durations compared with other groups.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>Duration of presurgical evaluation for DRE can be accurately modeled using multiple patient characteristics and testing strategies commonly employed in epilepsy surgery evaluations. This predictive model can not only estimate evaluation duration but also identify opportunities to improve systemic efficiency. Institution-level modeling identifies specific program strengths, providing an opportunity to learn from successful processes. Subsequent research will focus on institutional process mapping to better understand systemic practices that lead to improved efficiencies, then sharing these processes across the consortium to shorten evaluation durations.</p>\n </section>\n </div>","PeriodicalId":72232,"journal":{"name":"Annals of the Child Neurology Society","volume":"3 3","pages":"188-200"},"PeriodicalIF":0.0000,"publicationDate":"2025-08-06","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cns3.70027","citationCount":"0","resultStr":"{\"title\":\"Factors Influencing the Duration From the Initiation of Surgical Evaluation to Final Intervention in Pediatric Epilepsy Surgery\",\"authors\":\"Ruba Al-Ramadhani, Ann Hyslop, Avery R. Caraway, Edward J. Novotny, Adam P. Ostendorf, Krista L. Eschbach, Allyson L. Alexander, Lily C. Wong-Kisiel, Dewi F. Depositario-Cabacar, Chima O. Oluigbo, Cemal Karakas, Samir R. Karia, Priyamvada Tatachar, Jeffrey Bolton, Pilar D. Pichon, Daniel W. Shrey, Erin Fedak Romanowski, Nancy A. McNamara, Ernesto Gonzalez-Giraldo, Kurtis Auguste, Danilo Bernardo, Rani K. Singh, Pradeep K. Javarayee, Jenny J. Lin, Jason C. Coryell, Shilpa B. Reddy, Abhinaya Ganesh, Michael A. Ciliberto, Debopam Samanta, Kristen H. Arredondo, Ahmad Marashly, Zachary M. Grinspan, Dallas Armstrong, Taylor J. Abel, Janelle Wagner, Derryl J. Miller, Fernando N. Galan, Michael Scott Perry\",\"doi\":\"10.1002/cns3.70027\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"<div>\\n \\n \\n <section>\\n \\n <h3> Rationale</h3>\\n \\n <p>Longer duration of epilepsy before surgery is a predictor of poor outcome. While referral delays of surgical candidates are well documented, factors causing delay during the presurgical evaluation remain unclear and may vary depending on institutional characteristics. By benchmarking the duration of presurgical evaluation across multiple centers and identifying patient and evaluation characteristics contributing to duration, we can ascertain best practices and address modifiable contributors to reduce delays.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Methods</h3>\\n \\n <p>We queried the Pediatric Epilepsy Research Consortium Surgery Database, a prospective, observational multicenter study enrolling children 0–18 years at 27 US pediatric epilepsy centers, for all patients undergoing initial presurgical evaluation for drug-resistant epilepsy (DRE). We included patients with completed evaluations and data on duration from initiation of presurgical evaluation to final surgical decision. We compared patient characteristics and evaluation components between those with long duration evaluations (> 75% quartile) and those with short evaluations (< 25% quartile). Akaike information criteria selection identified variables associated with longer duration. From these, we developed a logistic prediction model for evaluation duration, using a random 80/20 training/testing split of the entire cohort. The model was tested among institutions with ≥ 10 patients in the cohort to assess its accuracy in predicting long durations. Linear models for each site assessed each variable's impact on duration. Variables with < 10% of the patient population at each site were excluded. Beta values were compared to identify intra- and inter-institution variability and to delineate institutions with the shortest added duration for each variable.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Results</h3>\\n \\n <p>Of 2318 patients undergoing surgical evaluation, 1655 (71%) from 23 sites had complete data. Median evaluation duration was 8 weeks (interquartile range 3–22); 453 (27%) were short-duration evaluations and 414 (25%) were long-duration evaluations. Multiple patient and evaluation characteristics were associated with duration (Table 1). Table 6 provides the average duration each variable contributes to evaluation by site, highlighting the shortest durations compared with other groups.</p>\\n </section>\\n \\n <section>\\n \\n <h3> Conclusions</h3>\\n \\n <p>Duration of presurgical evaluation for DRE can be accurately modeled using multiple patient characteristics and testing strategies commonly employed in epilepsy surgery evaluations. This predictive model can not only estimate evaluation duration but also identify opportunities to improve systemic efficiency. Institution-level modeling identifies specific program strengths, providing an opportunity to learn from successful processes. Subsequent research will focus on institutional process mapping to better understand systemic practices that lead to improved efficiencies, then sharing these processes across the consortium to shorten evaluation durations.</p>\\n </section>\\n </div>\",\"PeriodicalId\":72232,\"journal\":{\"name\":\"Annals of the Child Neurology Society\",\"volume\":\"3 3\",\"pages\":\"188-200\"},\"PeriodicalIF\":0.0000,\"publicationDate\":\"2025-08-06\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"https://onlinelibrary.wiley.com/doi/epdf/10.1002/cns3.70027\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"Annals of the Child Neurology Society\",\"FirstCategoryId\":\"1085\",\"ListUrlMain\":\"https://onlinelibrary.wiley.com/doi/10.1002/cns3.70027\",\"RegionNum\":0,\"RegionCategory\":null,\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"\",\"JCRName\":\"\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"Annals of the Child Neurology Society","FirstCategoryId":"1085","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1002/cns3.70027","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"","JCRName":"","Score":null,"Total":0}
Factors Influencing the Duration From the Initiation of Surgical Evaluation to Final Intervention in Pediatric Epilepsy Surgery
Rationale
Longer duration of epilepsy before surgery is a predictor of poor outcome. While referral delays of surgical candidates are well documented, factors causing delay during the presurgical evaluation remain unclear and may vary depending on institutional characteristics. By benchmarking the duration of presurgical evaluation across multiple centers and identifying patient and evaluation characteristics contributing to duration, we can ascertain best practices and address modifiable contributors to reduce delays.
Methods
We queried the Pediatric Epilepsy Research Consortium Surgery Database, a prospective, observational multicenter study enrolling children 0–18 years at 27 US pediatric epilepsy centers, for all patients undergoing initial presurgical evaluation for drug-resistant epilepsy (DRE). We included patients with completed evaluations and data on duration from initiation of presurgical evaluation to final surgical decision. We compared patient characteristics and evaluation components between those with long duration evaluations (> 75% quartile) and those with short evaluations (< 25% quartile). Akaike information criteria selection identified variables associated with longer duration. From these, we developed a logistic prediction model for evaluation duration, using a random 80/20 training/testing split of the entire cohort. The model was tested among institutions with ≥ 10 patients in the cohort to assess its accuracy in predicting long durations. Linear models for each site assessed each variable's impact on duration. Variables with < 10% of the patient population at each site were excluded. Beta values were compared to identify intra- and inter-institution variability and to delineate institutions with the shortest added duration for each variable.
Results
Of 2318 patients undergoing surgical evaluation, 1655 (71%) from 23 sites had complete data. Median evaluation duration was 8 weeks (interquartile range 3–22); 453 (27%) were short-duration evaluations and 414 (25%) were long-duration evaluations. Multiple patient and evaluation characteristics were associated with duration (Table 1). Table 6 provides the average duration each variable contributes to evaluation by site, highlighting the shortest durations compared with other groups.
Conclusions
Duration of presurgical evaluation for DRE can be accurately modeled using multiple patient characteristics and testing strategies commonly employed in epilepsy surgery evaluations. This predictive model can not only estimate evaluation duration but also identify opportunities to improve systemic efficiency. Institution-level modeling identifies specific program strengths, providing an opportunity to learn from successful processes. Subsequent research will focus on institutional process mapping to better understand systemic practices that lead to improved efficiencies, then sharing these processes across the consortium to shorten evaluation durations.