- Email: [email protected]
S27. Outcome prediction in postanoxic coma with deep learning
e152
Abstracts / Clinical Neurophysiology 129 (2018) e142–e212
N20 amplitude, clinical examination, EEG data, neuron-specific enolase and outcome determined upon hospital discharge by means of Cerebral performance category (CPC). The patients with CPC 1–3 were defined as good outcome, and those with CPC 4–5 as poor outcome. Results: We identified 76 postanoxic patients with N20 present. Fifty-three (69.7%) had poor outcome (CPC 4–5). The mean N20 amplitude in these patients was 1.90 lV (SD 2.01 range 0.16–7.9). Twenty-three patients (30.2%) had good outcome (CPC 1–3) and the mean N20 amplitude in these patients was 3.43 lV (SD 2.73 range 0.93–11.3). The ROC curve shows 0.86 lV as the cut-value for a better specificity with higher sensitivity. Any patient with amplitude lowest than 0.86 lV had good outcome (specificity 100%, sensitivity 50.9%, positive predictive value 100%, negative predictive value 46.9%). Conclusion: In our postanoxic patients very low N20 amplitudes appear to be highly predictive of poor outcome. Our results corroborate published data. doi:10.1016/j.clinph.2018.04.385
S26. Description and prognostic value of EEG in patients in coma after recovered cardiorespiratory arrest—Raidili Mateo-Montero, Guillermo Martin-Palomeque, Carlos Valera, Alicia GómezAnsede, Antonio Pedrera-Mazarro, Liliana González-Rodriguez, Ignacio Regidor (Spain)
Introduction: Postanoxic coma after cardiac arrest is one of the most serious acute cerebral conditions and a frequent cause of admission to critical care units, wherefore is necessary the use of accurate methods for diagnosis and prognosis. In addition to the classic neurologic examination, EEG is increasingly emerging as an important tool to assess cerebral functions in a non-invasive way because is a sensitive method for evaluating patients with anoxicischaemic encephalopathy due to cardiorrespiratory arrest (CRA) and provides accurate prognostic information in the early phase of coma however, its specificity is affected by the action of sedative drugs and the variability inter-observers. The EEG shows several patterns that may be useful for the prognosis and possible progression of patients with post-CRA coma. The aim of this study is to describe the morphology and analyze the prognostic value of the EEG patterns and reactivity to stimuli in post-CRA coma patients. Methods: 45 patients with CRA were analyzed. The variables established in the study included age, sex, pathological history, time of CRA, cause of CRA, therapeutic hypothermia, EEG (pattern and reactivity) and clinical development. All EEG studies were done without sedation drugs at least for 8 h and 24 h after therapeutic hypothermia (TH). Results: The initial EEG and the control EEGs in the patients were analyzed and an attempt was made to establish a correlation between the various EEG patterns and the clinical evolution of the patients. Surprisingly, the EEG reactivity was the parameter that, in isolation, presented greater specificity in our series. Conclusion: The EEG provides early information on brain function and prognosis in the context of a multimodal assessment of coma patients with recovered CRA. The standardization in the nomenclature of the EEG patterns following the terminology of the ACNS helps the easy recognition of these patterns and their correlation with the prognosis. It is essential to evaluate reactivity in this patients, given that this seems to be the most relevant data when estimating a prognosis. doi:10.1016/j.clinph.2018.04.386
S27. Outcome prediction in postanoxic coma with deep learning—Jeannette Hofmeijer, Barry J. Ruijter, Albertus Beishuizen, Frank H. Bosch, Michel J. van Putten, Marleen C. TjepkemaCloostermans * (Netherlands) ⇑
Presenting author.
Introduction: Visual assessment of the electroencephalogram (EEG) by experienced clinical neurophysiologists allows reliable outcome prediction in up to half of all comatose patients after cardiac arrest. We hypothesize that deep neural networks can achieve similar or better performance, while being objective and consistent. Methods: In a prospective cohort study, continuous EEG recordings from comatose patients after cardiac arrest were collected from the intensive care units of two large teaching hospitals. Functional outcome at six months was assessed using the Cerebral Performance Category scale (CPC), dichotomized as good (CPC 1–2) or poor (CPC 3–5). Five-minute artifact-free EEG epochs at 12 and 24 h after cardiac arrest were partitioned into 10 s epochs. We trained a convolutional neural network, using the raw EEG epochs and outcome labels as inputs to predict outcome using data from 80% of the patients. Validation was performed in the remaining 20%. The probability of recovery to good neurological outcome was quantified for each individual patient. Analyses of diagnostic accuracy included receiver operating characteristics and calculation of predictive values at 12 and 24 h. Results: Four hundred and fifty-six patients were included, resulting in 306 and 439 EEGs epochs at 12 and 24 h, respectively. Outcome prediction was most accurate at 12 h, with an area under the ROC curve (AUC) of 0.89 versus 0.81 at 24 h. Poor outcome could be predicted at 12 h with a sensitivity of 62% (95% confidence interval (CI): 45–78%) at false positive rate (FPR) of 0% (CI: 0–14%); good outcome could be predicted at 12 h with a sensitivity of 50% (CI: 29– 71%) at a FPR of 5% (CI: 1–18%). Conclusion: Deep learning of raw EEG signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual assessment by trained EEG expert. Our approach offers the potential for objective and real-time insight in the prognosis of neurological outcome on a continuous scale, and can provide low-cost expertise at the bedside. doi:10.1016/j.clinph.2018.04.387
S28. Survival in patients admitted in an intensive care unit according to electroencephalographic patterns—Mónica B. Perassolo *, Rita L. Aguirre (Argentina) ⇑
Presenting author.
Introduction: The American Society of Neurophysiology implemented a standardized classification (ACNS Standardized) for the evaluation of cerebral electrical activity in critical patients. The interpretation of electroencephalogram (EEG) findings and clinical correlation is still in debate. AIM: To compare survival according to EEG periodic and not periodic pattern in patients hospitalized in an intensive care unit. Methods: Retrospective cohort of patients over 16 years, in coma state, hospitalized consecutively at the intensive care unit (ICU) in a tertiary care hospital in Buenos Aires, Argentina, from 2014 to 2017. We included all patients with at least one 60 min EEG performed at the ICU, registered according to the International System 10/20. We excluded cases with extra-axial compression injuries. We used the ACNS Standardized guidelines (v.2012), Glasgow Outcome Scale (GOS) and Charlson’s comorbidity index (CCI). We
Abstracts / Clinical Neurophysiology 129 (2018) e142–e212
N20 amplitude, clinical examination, EEG data, neuron-specific enolase and outcome determined upon hospital discharge by means of Cerebral performance category (CPC). The patients with CPC 1–3 were defined as good outcome, and those with CPC 4–5 as poor outcome. Results: We identified 76 postanoxic patients with N20 present. Fifty-three (69.7%) had poor outcome (CPC 4–5). The mean N20 amplitude in these patients was 1.90 lV (SD 2.01 range 0.16–7.9). Twenty-three patients (30.2%) had good outcome (CPC 1–3) and the mean N20 amplitude in these patients was 3.43 lV (SD 2.73 range 0.93–11.3). The ROC curve shows 0.86 lV as the cut-value for a better specificity with higher sensitivity. Any patient with amplitude lowest than 0.86 lV had good outcome (specificity 100%, sensitivity 50.9%, positive predictive value 100%, negative predictive value 46.9%). Conclusion: In our postanoxic patients very low N20 amplitudes appear to be highly predictive of poor outcome. Our results corroborate published data. doi:10.1016/j.clinph.2018.04.385
S26. Description and prognostic value of EEG in patients in coma after recovered cardiorespiratory arrest—Raidili Mateo-Montero, Guillermo Martin-Palomeque, Carlos Valera, Alicia GómezAnsede, Antonio Pedrera-Mazarro, Liliana González-Rodriguez, Ignacio Regidor (Spain)
Introduction: Postanoxic coma after cardiac arrest is one of the most serious acute cerebral conditions and a frequent cause of admission to critical care units, wherefore is necessary the use of accurate methods for diagnosis and prognosis. In addition to the classic neurologic examination, EEG is increasingly emerging as an important tool to assess cerebral functions in a non-invasive way because is a sensitive method for evaluating patients with anoxicischaemic encephalopathy due to cardiorrespiratory arrest (CRA) and provides accurate prognostic information in the early phase of coma however, its specificity is affected by the action of sedative drugs and the variability inter-observers. The EEG shows several patterns that may be useful for the prognosis and possible progression of patients with post-CRA coma. The aim of this study is to describe the morphology and analyze the prognostic value of the EEG patterns and reactivity to stimuli in post-CRA coma patients. Methods: 45 patients with CRA were analyzed. The variables established in the study included age, sex, pathological history, time of CRA, cause of CRA, therapeutic hypothermia, EEG (pattern and reactivity) and clinical development. All EEG studies were done without sedation drugs at least for 8 h and 24 h after therapeutic hypothermia (TH). Results: The initial EEG and the control EEGs in the patients were analyzed and an attempt was made to establish a correlation between the various EEG patterns and the clinical evolution of the patients. Surprisingly, the EEG reactivity was the parameter that, in isolation, presented greater specificity in our series. Conclusion: The EEG provides early information on brain function and prognosis in the context of a multimodal assessment of coma patients with recovered CRA. The standardization in the nomenclature of the EEG patterns following the terminology of the ACNS helps the easy recognition of these patterns and their correlation with the prognosis. It is essential to evaluate reactivity in this patients, given that this seems to be the most relevant data when estimating a prognosis. doi:10.1016/j.clinph.2018.04.386
S27. Outcome prediction in postanoxic coma with deep learning—Jeannette Hofmeijer, Barry J. Ruijter, Albertus Beishuizen, Frank H. Bosch, Michel J. van Putten, Marleen C. TjepkemaCloostermans * (Netherlands) ⇑
Presenting author.
Introduction: Visual assessment of the electroencephalogram (EEG) by experienced clinical neurophysiologists allows reliable outcome prediction in up to half of all comatose patients after cardiac arrest. We hypothesize that deep neural networks can achieve similar or better performance, while being objective and consistent. Methods: In a prospective cohort study, continuous EEG recordings from comatose patients after cardiac arrest were collected from the intensive care units of two large teaching hospitals. Functional outcome at six months was assessed using the Cerebral Performance Category scale (CPC), dichotomized as good (CPC 1–2) or poor (CPC 3–5). Five-minute artifact-free EEG epochs at 12 and 24 h after cardiac arrest were partitioned into 10 s epochs. We trained a convolutional neural network, using the raw EEG epochs and outcome labels as inputs to predict outcome using data from 80% of the patients. Validation was performed in the remaining 20%. The probability of recovery to good neurological outcome was quantified for each individual patient. Analyses of diagnostic accuracy included receiver operating characteristics and calculation of predictive values at 12 and 24 h. Results: Four hundred and fifty-six patients were included, resulting in 306 and 439 EEGs epochs at 12 and 24 h, respectively. Outcome prediction was most accurate at 12 h, with an area under the ROC curve (AUC) of 0.89 versus 0.81 at 24 h. Poor outcome could be predicted at 12 h with a sensitivity of 62% (95% confidence interval (CI): 45–78%) at false positive rate (FPR) of 0% (CI: 0–14%); good outcome could be predicted at 12 h with a sensitivity of 50% (CI: 29– 71%) at a FPR of 5% (CI: 1–18%). Conclusion: Deep learning of raw EEG signals outperforms any previously reported outcome predictor of coma after cardiac arrest, including visual assessment by trained EEG expert. Our approach offers the potential for objective and real-time insight in the prognosis of neurological outcome on a continuous scale, and can provide low-cost expertise at the bedside. doi:10.1016/j.clinph.2018.04.387
S28. Survival in patients admitted in an intensive care unit according to electroencephalographic patterns—Mónica B. Perassolo *, Rita L. Aguirre (Argentina) ⇑
Presenting author.
Introduction: The American Society of Neurophysiology implemented a standardized classification (ACNS Standardized) for the evaluation of cerebral electrical activity in critical patients. The interpretation of electroencephalogram (EEG) findings and clinical correlation is still in debate. AIM: To compare survival according to EEG periodic and not periodic pattern in patients hospitalized in an intensive care unit. Methods: Retrospective cohort of patients over 16 years, in coma state, hospitalized consecutively at the intensive care unit (ICU) in a tertiary care hospital in Buenos Aires, Argentina, from 2014 to 2017. We included all patients with at least one 60 min EEG performed at the ICU, registered according to the International System 10/20. We excluded cases with extra-axial compression injuries. We used the ACNS Standardized guidelines (v.2012), Glasgow Outcome Scale (GOS) and Charlson’s comorbidity index (CCI). We