Monitoring burst suppression in critically ill patients: Multi-centric evaluation of a novel method

Abstract

Objective To develop a computational method to detect and quantify burst suppression patterns (BSP) in the EEGs of critical care patients. A multi-center validation study was performed to assess the detection performance of the method. Methods The fully automatic method scans the EEG for discontinuous patterns and shows detected BSP and quantitative information on a trending display in real-time. The method is designed to work without setting any patient specific parameters and to be insensitive to EEG artifacts and periodic patterns. For validation a total of 3982 h of EEG from 88 patients were analyzed from three centers. Each EEG was annotated by two reviewers to assess the detection performance and the inter-rater agreement. Results Average inter-rater agreement between pairs of reviewers was κ = 0.69. On average 22% of the review segments included BSP. An average sensitivity of 90% and a specificity of 84% were measured on the consensus annotations of two reviewers. More than 95% of the periodic patterns in the EEGs were correctly suppressed. Conclusion A fully automatic method to detect burst suppression patterns was assessed in a multi-center study. The method showed high sensitivity and specificity. Significance Clinically applicable burst suppression detection method validated in a large multi-center study.

Automated Long-Term EEG Review: Fast and Precise Analysis in Critical Care Patients

“…We observed substantial IRA for burst suppression patterns with sensitivities ranging from 93 to 100% (mean 97%) and specificities from 74 to 80% (mean 77%). Kappa values of IRA were almost identical in a previous study conducted by our group, whereas sensitivity was lower and specificity slightly higher ( 29 ). Furthermore, MRA showed almost perfect agreement for burst suppression in the present study.…”

Association Between Induced Burst Suppression and Clinical Outcomes in Patients With Refractory Status Epilepticus

“…Computational algorithms to quantify burst suppression need an extensive preprocessing before analysis, and commercially available software is prone to over-reading or under-reading and needs human proofreading for quality assessment. 26 For this study, we chose an adaptation of a previously published pragmatic approach 27 to quantitatively assess burst suppression, which represents also a feasible approach in clinical practice directly at the patient's bedside: all EEGs during the period of IVADs were visually reanalyzed to quantify burst suppression by 2 trained EEG readers (U.F. and A.L.J.)…”

Burst-suppression eeg pattern is not always a predictor of poor prognosis in children

“…В многочисленных исследованиях [16][17][18][19] показано, что регистрация ПВП у взрослых пациентов в отсутствие медикаментозной седации является прогностически неблагоприятным электрографическим признаком. В большинстве таких работ информативность ЭЭГ оценивали у больных с аноксиишемическим поражением головного мозга, однако выделение ПВП в качестве отдельного фактора прогнозирования исхода привело к тому, что его стали ассоциировать с неблагоприятным прогнозом выживания и восстановления сознания у всех пациентов [8,20].…”

“…Для точного прогнозирования следует проводить длительное мониторирование ЭЭГ с оценкой продолжительности вспышек и процентного содержания в записи участков с подавленной биоэлектрической активностью. Предложенные для снижения трудоемкости анализа автоматизированные системы сбора и обработки данных ЭЭГ, которые рассчитывают цифровой индекс ПВП, должны быть валидизированы на большом количестве наблюдений; кроме того, необходимо оценить их прогностическую ценность для разных заболеваний, приводящих к угнетению бодрствования [20].…”