Forecasting seizure risk in adults with focal epilepsy: a development and validation study

被引:110
作者
Proix, Timothee [1 ,2 ]
Truccolo, Wilson [2 ]
Leguia, Marc G. [3 ,4 ]
Tcheng, Thomas K. [5 ]
King-Stephens, David [6 ]
Rao, Vikram R. [7 ,8 ]
Baud, Maxime O. [3 ,4 ,9 ]
机构
[1] Univ Geneva, Fac Med, Dept Basic Neurosci, Geneva, Switzerland
[2] Brown Univ, Carney Inst Brain Sci, Dept Neurosci, Providence, RI 02912 USA
[3] Univ Bern, Univ Hosp, Inselspital Bern, Sleep Wake Epilepsy Ctr,NeuroTec, CH-3010 Bern, Switzerland
[4] Univ Bern, Univ Hosp, Inselspital Bern, Ctr Expt Neurol,Dept Neurol, CH-3010 Bern, Switzerland
[5] NeuroPace, Mountain View, CA USA
[6] Calif Pacific Med Ctr, San Francisco, CA USA
[7] Univ Calif San Francisco, Dept Neurol, San Francisco, CA USA
[8] Univ Calif San Francisco, Weill Inst Neurosci, San Francisco, CA 94143 USA
[9] Wyss Ctr Bio & Neuroengn, Geneva, Switzerland
基金
瑞士国家科学基金会;
关键词
LONG-TERM; PREDICTION; BRIER;
D O I
10.1016/S1474-4422(20)30396-3
中图分类号
R74 [神经病学与精神病学];
学科分类号
摘要
Background People with epilepsy are burdened with the apparent unpredictability of seizures. In the past decade, converging evidence from studies using chronic EEG (cEEG) revealed that epileptic brain activity shows robust cycles, operating over hours (circadian) and days (multidien). We hypothesised that these cycles can be leveraged to estimate future seizure probability, and we tested the feasibility of forecasting seizures days in advance. Methods We did a feasibility study in distinct development and validation cohorts, involving retrospective analysis of cEEG data recorded with an implanted device in adults (age >= 18 years) with drug-resistant focal epilepsy followed at 35 centres across the USA between Jan 19, 2004, and May 18, 2018. Patients were required to have had 20 or snore electrographic seizures (development cohort) or self-reported seizures (validation cohort). In all patients, the device recorded interictal epileptiform activity (IEA; >= 6 months of continuous hourly data), the fluctuations in which helped estimate varying seizure risk. Point process statistical models trained on initial portions of each patient's cEEG data (both cohorts) generated forecasts of seizure probability that were tested on subsequent unseen seizure data and evaluated against surrogate time-series. The primary outcome was the percentage of patients with forecasts showing improvement over chance (IoC). Findings We screened 72 and 256 patients, and included 18 and 157 patients in the development and validation cohorts, respectively. Models incorporating information about multidien IEA cycles alone generated daily seizure forecasts for the next calendar day with IoC in 15 (83%) patients in the development cohort and 103 (66%) patients in the validation cohort. The forecasting horizon could be extended up to 3 days while maintaining IoC in two (11%) of 18 patients and 61 (39%) of 157 patients. Forecasts with a shorter horizon of 1 h, possible only for electrographic seizures in the development cohort, showed IoC in all 18 (100%) patients. Interpretation This study shows that seizure probability can be forecasted days in advance by leveraging multidien IEA cycles recorded with an implanted device. This study will serve as a basis for prospective clinical trials to establish how people with epilepsy might benefit from seizure forecasting over long horizons. Copyright (C) 2020 Elsevier Ltd. All rights reserved.
引用
收藏
页码:127 / 135
页数:9
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