A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends
Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early...
Ausführliche Beschreibung
Gespeichert in:
| Autor*in: |
Kang, Jennifer H. [verfasserIn] Sherill, G. Clay [verfasserIn] Sinha, Saurabh R. [verfasserIn] Swisher, Christa B. [verfasserIn] |
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| Format: |
E-Artikel |
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| Sprache: |
Englisch |
| Erschienen: |
2019 |
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| Schlagwörter: |
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| Übergeordnetes Werk: |
Enthalten in: Neurocritical care - New York, NY : Springer, 2004, 31(2019), 2 vom: 20. Feb., Seite 312-320 |
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| Übergeordnetes Werk: |
volume:31 ; year:2019 ; number:2 ; day:20 ; month:02 ; pages:312-320 |
| Links: |
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| DOI / URN: |
10.1007/s12028-019-00673-z |
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| Katalog-ID: |
SPR023818271 |
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| 520 | |a Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. | ||
| 650 | 4 | |a Quantitative EEG |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a qEEG |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Seizures |7 (dpeaa)DE-He213 | |
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| 650 | 4 | |a EEG |7 (dpeaa)DE-He213 | |
| 650 | 4 | |a Non-convulsive seizures |7 (dpeaa)DE-He213 | |
| 700 | 1 | |a Sherill, G. Clay |e verfasserin |4 aut | |
| 700 | 1 | |a Sinha, Saurabh R. |e verfasserin |4 aut | |
| 700 | 1 | |a Swisher, Christa B. |e verfasserin |4 aut | |
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10.1007/s12028-019-00673-z doi (DE-627)SPR023818271 (SPR)s12028-019-00673-z-e DE-627 ger DE-627 rakwb eng 610 ASE 44.90 bkl Kang, Jennifer H. verfasserin aut A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. Quantitative EEG (dpeaa)DE-He213 qEEG (dpeaa)DE-He213 Seizures (dpeaa)DE-He213 ICU (dpeaa)DE-He213 EEG (dpeaa)DE-He213 Non-convulsive seizures (dpeaa)DE-He213 Sherill, G. Clay verfasserin aut Sinha, Saurabh R. verfasserin aut Swisher, Christa B. verfasserin aut Enthalten in Neurocritical care New York, NY : Springer, 2004 31(2019), 2 vom: 20. Feb., Seite 312-320 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:31 year:2019 number:2 day:20 month:02 pages:312-320 https://dx.doi.org/10.1007/s12028-019-00673-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 31 2019 2 20 02 312-320 |
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10.1007/s12028-019-00673-z doi (DE-627)SPR023818271 (SPR)s12028-019-00673-z-e DE-627 ger DE-627 rakwb eng 610 ASE 44.90 bkl Kang, Jennifer H. verfasserin aut A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. Quantitative EEG (dpeaa)DE-He213 qEEG (dpeaa)DE-He213 Seizures (dpeaa)DE-He213 ICU (dpeaa)DE-He213 EEG (dpeaa)DE-He213 Non-convulsive seizures (dpeaa)DE-He213 Sherill, G. Clay verfasserin aut Sinha, Saurabh R. verfasserin aut Swisher, Christa B. verfasserin aut Enthalten in Neurocritical care New York, NY : Springer, 2004 31(2019), 2 vom: 20. Feb., Seite 312-320 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:31 year:2019 number:2 day:20 month:02 pages:312-320 https://dx.doi.org/10.1007/s12028-019-00673-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 31 2019 2 20 02 312-320 |
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10.1007/s12028-019-00673-z doi (DE-627)SPR023818271 (SPR)s12028-019-00673-z-e DE-627 ger DE-627 rakwb eng 610 ASE 44.90 bkl Kang, Jennifer H. verfasserin aut A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. Quantitative EEG (dpeaa)DE-He213 qEEG (dpeaa)DE-He213 Seizures (dpeaa)DE-He213 ICU (dpeaa)DE-He213 EEG (dpeaa)DE-He213 Non-convulsive seizures (dpeaa)DE-He213 Sherill, G. Clay verfasserin aut Sinha, Saurabh R. verfasserin aut Swisher, Christa B. verfasserin aut Enthalten in Neurocritical care New York, NY : Springer, 2004 31(2019), 2 vom: 20. Feb., Seite 312-320 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:31 year:2019 number:2 day:20 month:02 pages:312-320 https://dx.doi.org/10.1007/s12028-019-00673-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 31 2019 2 20 02 312-320 |
| allfieldsGer |
10.1007/s12028-019-00673-z doi (DE-627)SPR023818271 (SPR)s12028-019-00673-z-e DE-627 ger DE-627 rakwb eng 610 ASE 44.90 bkl Kang, Jennifer H. verfasserin aut A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. Quantitative EEG (dpeaa)DE-He213 qEEG (dpeaa)DE-He213 Seizures (dpeaa)DE-He213 ICU (dpeaa)DE-He213 EEG (dpeaa)DE-He213 Non-convulsive seizures (dpeaa)DE-He213 Sherill, G. Clay verfasserin aut Sinha, Saurabh R. verfasserin aut Swisher, Christa B. verfasserin aut Enthalten in Neurocritical care New York, NY : Springer, 2004 31(2019), 2 vom: 20. Feb., Seite 312-320 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:31 year:2019 number:2 day:20 month:02 pages:312-320 https://dx.doi.org/10.1007/s12028-019-00673-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 31 2019 2 20 02 312-320 |
| allfieldsSound |
10.1007/s12028-019-00673-z doi (DE-627)SPR023818271 (SPR)s12028-019-00673-z-e DE-627 ger DE-627 rakwb eng 610 ASE 44.90 bkl Kang, Jennifer H. verfasserin aut A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends 2019 Text txt rdacontent Computermedien c rdamedia Online-Ressource cr rdacarrier Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. Quantitative EEG (dpeaa)DE-He213 qEEG (dpeaa)DE-He213 Seizures (dpeaa)DE-He213 ICU (dpeaa)DE-He213 EEG (dpeaa)DE-He213 Non-convulsive seizures (dpeaa)DE-He213 Sherill, G. Clay verfasserin aut Sinha, Saurabh R. verfasserin aut Swisher, Christa B. verfasserin aut Enthalten in Neurocritical care New York, NY : Springer, 2004 31(2019), 2 vom: 20. Feb., Seite 312-320 (DE-627)478509855 (DE-600)2176033-0 1556-0961 nnns volume:31 year:2019 number:2 day:20 month:02 pages:312-320 https://dx.doi.org/10.1007/s12028-019-00673-z lizenzpflichtig Volltext GBV_USEFLAG_A SYSFLAG_A GBV_SPRINGER SSG-OLC-PHA GBV_ILN_11 GBV_ILN_20 GBV_ILN_22 GBV_ILN_23 GBV_ILN_24 GBV_ILN_31 GBV_ILN_32 GBV_ILN_39 GBV_ILN_40 GBV_ILN_60 GBV_ILN_62 GBV_ILN_63 GBV_ILN_65 GBV_ILN_69 GBV_ILN_70 GBV_ILN_73 GBV_ILN_74 GBV_ILN_90 GBV_ILN_95 GBV_ILN_100 GBV_ILN_101 GBV_ILN_105 GBV_ILN_110 GBV_ILN_120 GBV_ILN_138 GBV_ILN_150 GBV_ILN_151 GBV_ILN_161 GBV_ILN_170 GBV_ILN_171 GBV_ILN_187 GBV_ILN_213 GBV_ILN_224 GBV_ILN_230 GBV_ILN_250 GBV_ILN_281 GBV_ILN_285 GBV_ILN_293 GBV_ILN_370 GBV_ILN_602 GBV_ILN_636 GBV_ILN_702 GBV_ILN_2001 GBV_ILN_2003 GBV_ILN_2004 GBV_ILN_2005 GBV_ILN_2006 GBV_ILN_2007 GBV_ILN_2008 GBV_ILN_2009 GBV_ILN_2010 GBV_ILN_2011 GBV_ILN_2014 GBV_ILN_2015 GBV_ILN_2020 GBV_ILN_2021 GBV_ILN_2025 GBV_ILN_2026 GBV_ILN_2027 GBV_ILN_2031 GBV_ILN_2034 GBV_ILN_2037 GBV_ILN_2038 GBV_ILN_2039 GBV_ILN_2044 GBV_ILN_2048 GBV_ILN_2049 GBV_ILN_2050 GBV_ILN_2055 GBV_ILN_2057 GBV_ILN_2059 GBV_ILN_2061 GBV_ILN_2064 GBV_ILN_2065 GBV_ILN_2068 GBV_ILN_2070 GBV_ILN_2086 GBV_ILN_2088 GBV_ILN_2093 GBV_ILN_2106 GBV_ILN_2107 GBV_ILN_2108 GBV_ILN_2110 GBV_ILN_2111 GBV_ILN_2112 GBV_ILN_2113 GBV_ILN_2116 GBV_ILN_2118 GBV_ILN_2119 GBV_ILN_2122 GBV_ILN_2129 GBV_ILN_2143 GBV_ILN_2144 GBV_ILN_2147 GBV_ILN_2148 GBV_ILN_2152 GBV_ILN_2153 GBV_ILN_2188 GBV_ILN_2190 GBV_ILN_2232 GBV_ILN_2336 GBV_ILN_2446 GBV_ILN_2470 GBV_ILN_2472 GBV_ILN_2507 GBV_ILN_2522 GBV_ILN_2548 GBV_ILN_4035 GBV_ILN_4037 GBV_ILN_4046 GBV_ILN_4112 GBV_ILN_4125 GBV_ILN_4242 GBV_ILN_4246 GBV_ILN_4249 GBV_ILN_4251 GBV_ILN_4305 GBV_ILN_4306 GBV_ILN_4307 GBV_ILN_4313 GBV_ILN_4322 GBV_ILN_4323 GBV_ILN_4324 GBV_ILN_4325 GBV_ILN_4326 GBV_ILN_4333 GBV_ILN_4334 GBV_ILN_4335 GBV_ILN_4336 GBV_ILN_4338 GBV_ILN_4393 GBV_ILN_4700 44.90 ASE AR 31 2019 2 20 02 312-320 |
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English |
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Enthalten in Neurocritical care 31(2019), 2 vom: 20. Feb., Seite 312-320 volume:31 year:2019 number:2 day:20 month:02 pages:312-320 |
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Enthalten in Neurocritical care 31(2019), 2 vom: 20. Feb., Seite 312-320 volume:31 year:2019 number:2 day:20 month:02 pages:312-320 |
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Quantitative EEG qEEG Seizures ICU EEG Non-convulsive seizures |
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Neurocritical care |
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Kang, Jennifer H. @@aut@@ Sherill, G. Clay @@aut@@ Sinha, Saurabh R. @@aut@@ Swisher, Christa B. @@aut@@ |
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2019-02-20T00:00:00Z |
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<?xml version="1.0" encoding="UTF-8"?><collection xmlns="http://www.loc.gov/MARC21/slim"><record><leader>01000caa a22002652 4500</leader><controlfield tag="001">SPR023818271</controlfield><controlfield tag="003">DE-627</controlfield><controlfield tag="005">20230519153300.0</controlfield><controlfield tag="007">cr uuu---uuuuu</controlfield><controlfield tag="008">201006s2019 xx |||||o 00| ||eng c</controlfield><datafield tag="024" ind1="7" ind2=" "><subfield code="a">10.1007/s12028-019-00673-z</subfield><subfield code="2">doi</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(DE-627)SPR023818271</subfield></datafield><datafield tag="035" ind1=" " ind2=" "><subfield code="a">(SPR)s12028-019-00673-z-e</subfield></datafield><datafield tag="040" ind1=" " ind2=" "><subfield code="a">DE-627</subfield><subfield code="b">ger</subfield><subfield code="c">DE-627</subfield><subfield code="e">rakwb</subfield></datafield><datafield tag="041" ind1=" " ind2=" "><subfield code="a">eng</subfield></datafield><datafield tag="082" ind1="0" ind2="4"><subfield code="a">610</subfield><subfield code="q">ASE</subfield></datafield><datafield tag="084" ind1=" " ind2=" "><subfield code="a">44.90</subfield><subfield code="2">bkl</subfield></datafield><datafield tag="100" ind1="1" ind2=" "><subfield code="a">Kang, Jennifer H.</subfield><subfield code="e">verfasserin</subfield><subfield code="4">aut</subfield></datafield><datafield tag="245" ind1="1" ind2="2"><subfield code="a">A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends</subfield></datafield><datafield tag="264" ind1=" " ind2="1"><subfield code="c">2019</subfield></datafield><datafield tag="336" ind1=" " ind2=" "><subfield code="a">Text</subfield><subfield code="b">txt</subfield><subfield code="2">rdacontent</subfield></datafield><datafield tag="337" ind1=" " ind2=" "><subfield code="a">Computermedien</subfield><subfield code="b">c</subfield><subfield code="2">rdamedia</subfield></datafield><datafield tag="338" ind1=" " ind2=" "><subfield code="a">Online-Ressource</subfield><subfield code="b">cr</subfield><subfield code="2">rdacarrier</subfield></datafield><datafield tag="520" ind1=" " ind2=" "><subfield code="a">Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS.</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Quantitative EEG</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">qEEG</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Seizures</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">ICU</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">EEG</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="650" ind1=" " ind2="4"><subfield code="a">Non-convulsive seizures</subfield><subfield code="7">(dpeaa)DE-He213</subfield></datafield><datafield tag="700" ind1="1" ind2=" "><subfield code="a">Sherill, G. 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Kang, Jennifer H. |
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Kang, Jennifer H. ddc 610 bkl 44.90 misc Quantitative EEG misc qEEG misc Seizures misc ICU misc EEG misc Non-convulsive seizures A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends |
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610 ASE 44.90 bkl A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends Quantitative EEG (dpeaa)DE-He213 qEEG (dpeaa)DE-He213 Seizures (dpeaa)DE-He213 ICU (dpeaa)DE-He213 EEG (dpeaa)DE-He213 Non-convulsive seizures (dpeaa)DE-He213 |
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trial of real-time electrographic seizure detection by neuro-icu nurses using a panel of quantitative eeg trends |
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A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends |
| abstract |
Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. |
| abstractGer |
Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. |
| abstract_unstemmed |
Background Non-convulsive seizures (NCS) are a common occurrence in the neurologic intensive care unit (Neuro-ICU) and are associated with worse outcomes. Continuous electroencephalogram (cEEG) monitoring is necessary for the detection of NCS; however, delays in interpretation are a barrier to early treatment. Quantitative EEG (qEEG) calculates a time-compressed simplified visual display from raw EEG data. This study aims to evaluate the performance of Neuro-ICU nurses utilizing bedside, real-time qEEG interpretation for detecting recurrent NCS. Methods This is a prospective, single-institution study of patients admitted to the Duke Neuro-ICU between 2016 and 2018 who had NCS identified on traditional cEEG review. The accuracy of recurrent seizure detection on hourly qEEG review by bedside Neuro-ICU nurses was compared to the gold standard of cEEG interpretation by two board-certified neurophysiologists. The nurses first received brief qEEG training, individualized for their specific patient. The bedside qEEG display consisted of rhythmicity spectrogram (left and right hemispheres) and amplitude-integrated EEG (left and right hemispheres) in 1-h epochs. Results Twenty patients were included and 174 1-h qEEG blocks were analyzed. Forty-seven blocks contained seizures (27%). The sensitivity was 85.1% (95% CI 71.1–93.1%), and the specificity was 89.8% (82.8–94.2%) for the detection of seizures for each 1-h block when compared to interpretation of conventional cEEG by two neurophysiologists. The false positive rate was 0.1/h. Hemispheric seizures (> 4 unilateral EEG electrodes) were more likely to be correctly identified by nurses on qEEG than focal seizures (≤ 4 unilateral electrodes) (p = 0.03). Conclusions After tailored training sessions, Neuro-ICU nurses demonstrated a good sensitivity for the interpretation of bedside real-time qEEG for the detection of recurrent NCS with a low false positive rate. qEEG is a promising tool that may be used by non-neurophysiologists and may lead to earlier detection of NCS. |
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| title_short |
A Trial of Real-Time Electrographic Seizure Detection by Neuro-ICU Nurses Using a Panel of Quantitative EEG Trends |
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https://dx.doi.org/10.1007/s12028-019-00673-z |
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Sherill, G. Clay Sinha, Saurabh R. Swisher, Christa B. |
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2024-07-03T21:35:43.690Z |
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| score |
7.400527 |