Clinical neurophysiology : official journal of the International Federation of Clinical Neurophysiology · 2025
Using high-frequency oscillation to detect acute brain injury in extracorporeal membrane oxygenation supported patients.
СтатьяНазвание и аннотация: Перевод в очереди
Обработка статьи · этапы, ошибки и управление
Загрузка состояния…
Ошибки и ограничения
История · было → стало
Состояние обновляется каждые 5 секунд, пока блок открыт. Процент показывается только там, где известен объём работы. Пауза останавливает продолжение этапа; текущий запрос к источнику может завершиться.
Cao Mingfeng, Khanduja Shivalika, Liu Winnie, Nan Feng Shi, Kang Jin Kook, Husari Khalil S, Ritzl Eva Katharina, Whitman Glenn, Thakor Nitish, Cho Sung-Min
Аннотация
OBJECTIVE: This study aims to utilize HFO analysis to enhance existing SSEP modality and develop it as a bedside diagnostic tool for acute brain injury (ABI) detection in Extracorporeal Membrane Oxygenation (ECMO) patients.
SIGNIFICANCE: Timely diagnosis of ABI in ECMO patients is challenging due to logistical complexities with computed tomography (CT) and magnetic resonance imaging (MRI). Integrating time-frequency analysis into routine SSEP monitoring for early ABI detection can facilitate timely medical decisions.
METHOD: Consecutive SSEP data were collected from Johns Hopkins Intensive Care Units (ICUs), including 31 ECMO and 45 non-ECMO patients from 2016 to 2022. ABIs were determined using CT and MRI as clinically indicated. Using wavelet techniques, two SSEP-HFO components were quantified: HFOL (80-200 Hz) and HFOH (200-600 Hz), which were later fed to a Support Vector Machine (SVM) with a linear kernel.
RESULT: ECMO patients with ABI (N = 22) exhibited suppressed HFOH (Median = -9.09, Interquartile Range (IQR) = [ -13.5; -4.73] dB) compared to patients without (N = 9, Median = -4.39, IQR = [-6.35; -3.28] dB, P = 0.035). The SVM classifier achieved an accuracy of 75 % and a sensitivity of 82 % for detecting ABI, outperforming SSEP-N20.
CONCLUSION: SSEP-HFO can potentially improve early detection of ABI in ECMO patients at the bedside.
SIGNIFICANCE: Timely diagnosis of ABI in ECMO patients is challenging due to logistical complexities with computed tomography (CT) and magnetic resonance imaging (MRI). Integrating time-frequency analysis into routine SSEP monitoring for early ABI detection can facilitate timely medical decisions.
METHOD: Consecutive SSEP data were collected from Johns Hopkins Intensive Care Units (ICUs), including 31 ECMO and 45 non-ECMO patients from 2016 to 2022. ABIs were determined using CT and MRI as clinically indicated. Using wavelet techniques, two SSEP-HFO components were quantified: HFOL (80-200 Hz) and HFOH (200-600 Hz), which were later fed to a Support Vector Machine (SVM) with a linear kernel.
RESULT: ECMO patients with ABI (N = 22) exhibited suppressed HFOH (Median = -9.09, Interquartile Range (IQR) = [ -13.5; -4.73] dB) compared to patients without (N = 9, Median = -4.39, IQR = [-6.35; -3.28] dB, P = 0.035). The SVM classifier achieved an accuracy of 75 % and a sensitivity of 82 % for detecting ABI, outperforming SSEP-N20.
CONCLUSION: SSEP-HFO can potentially improve early detection of ABI in ECMO patients at the bedside.