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

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
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引用次数: 0

摘要

手术前癫痫持续时间较长是预后不良的一个预测因素。虽然手术候选人的转诊延迟有很好的记录,但在术前评估期间导致延迟的因素仍然不清楚,并且可能因机构特征而异。通过对多个中心的手术前评估的持续时间进行基准测试,并确定影响持续时间的患者和评估特征,我们可以确定最佳实践并解决可修改的因素,以减少延误。方法:我们查询了儿童癫痫研究联盟手术数据库,这是一项前瞻性、观察性多中心研究,纳入了27个美国儿童癫痫中心的0-18岁儿童,所有患者接受了耐药性癫痫(DRE)的初步术前评估。我们纳入了完成评估的患者以及从术前评估开始到最终手术决定的持续时间数据。我们比较了长时间评估组(>; 75%四分位数)和短时间评估组(<; 25%四分位数)的患者特征和评估成分。赤池信息标准选择确定的变量与较长的持续时间有关。由此,我们开发了评估持续时间的逻辑预测模型,使用整个队列的随机80/20训练/测试分割。该模型在队列中有≥10例患者的机构中进行测试,以评估其预测长期持续时间的准确性。每个地点的线性模型评估了每个变量对持续时间的影响。排除每个部位患者人数占10%的变量。对Beta值进行比较,以确定机构内部和机构间的可变性,并描绘每个变量的增加持续时间最短的机构。结果在2318例接受手术评估的患者中,来自23个部位的1655例(71%)资料完整。中位评估持续时间为8周(四分位数范围为3-22);453例(27%)为短期评估,414例(25%)为长期评估。多个患者和评估特征与持续时间相关(表1)。表6提供了每个变量对站点评估的平均持续时间,突出显示了与其他组相比最短的持续时间。结论采用多种患者特征和癫痫手术评估常用的测试策略,可以准确地模拟DRE术前评估的持续时间。该预测模型不仅可以估计评估持续时间,还可以识别提高系统效率的机会。机构级建模确定了特定的项目优势,提供了从成功过程中学习的机会。随后的研究将集中在制度流程映射上,以更好地理解导致效率提高的系统实践,然后在整个联盟中共享这些过程,以缩短评估持续时间。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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.

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