Predicting outcome in patients with moderate to severe traumatic brain injury using electroencephalography.

BACKGROUND: Better outcome prediction could assist in reliable quantification and classification of traumatic brain injury (TBI) severity to support clinical decision-making. We developed a multifactorial model combining quantitative electroencephalography (qEEG) measurements and clinically relevant parameters as proof of concept for outcome prediction of patients with moderate to severe TBI. METHODS: Continuous EEG measurements were performed during the first 7 days of ICU admission. Patient outcome at 12 months was dichotomized based on the Extended Glasgow Outcome Score (GOSE) as poor (GOSE 1-2) or good (GOSE 3-8). Twenty-three qEEG features were extracted. Prediction models were created using a Random Forest classifier based on qEEG features, age, and mean arterial blood pressure (MAP) at 24, 48, 72, and 96 h after TBI and combinations of two time intervals. After optimization of the models, we added parameters from the International Mission for Prognosis And Clinical Trial Design (IMPACT) predictor, existing of clinical, CT, and laboratory parameters at admission. Furthermore, we compared our best models to the online IMPACT predictor. RESULTS: Fifty-seven patients with moderate to severe TBI were included and divided into a training set (n = 38) and a validation set (n = 19). Our best model included eight qEEG parameters and MAP at 72 and 96 h after TBI, age, and nine other IMPACT parameters. This model had high predictive ability for poor outcome on both the training set using leave-one-out (area under the receiver operating characteristic curve (AUC) = 0.94, specificity 100%, sensitivity 75%) and validation set (AUC = 0.81, specificity 75%, sensitivity 100%). The IMPACT predictor independently predicted both groups with an AUC of 0.74 (specificity 81%, sensitivity 65%) and 0.84 (sensitivity 88%, specificity 73%), respectively. CONCLUSIONS: Our study shows the potential of multifactorial Random Forest models using qEEG parameters to predict outcome in patients with moderate to severe TBI.

Cerebral Recovery Index: Reliable Help for Prediction of Neurologic Outcome After Cardiac Arrest.

OBJECTIVE: Early electroencephalography measures contribute to outcome prediction of comatose patients after cardiac arrest. We present predictive values of a new cerebral recovery index, based on a combination of quantitative electroencephalography measures, extracted every hour, and combined by the use of a random forest classifier. DESIGN: Prospective observational cohort study. SETTING: Medical ICU of two large teaching hospitals in the Netherlands. PATIENTS: Two hundred eighty-three consecutive comatose patients after cardiac arrest. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: Continuous electroencephalography was recorded during the first 3 days. Outcome at 6 months was dichotomized as good (Cerebral Performance Category 1-2, no or moderate disability) or poor (Cerebral Performance Category 3-5, severe disability, comatose, or death). Nine quantitative electroencephalography measures were extracted. Patients were randomly divided over a training and validation set. Within the training set, a random forest classifier was fitted for each hour after cardiac arrest. Diagnostic accuracy was evaluated in the validation set. The relative contributions of resuscitation parameters and patient characteristics were evaluated. The cerebral recovery index ranges from 0 (prediction of death) to 1 (prediction of full recovery). Poor outcome could be predicted at a threshold of 0.34 without false positives at a sensitivity of 56% at 12 hours after cardiac arrest. At 24 hours, sensitivity of 65% with a false positive rate of 6% was obtained. Good neurologic outcome could be predicted with sensitivities of 63% and 58% at a false positive rate of 6% and 7% at 12 and 24 hours, respectively. Adding patient characteristics was of limited additional predictive value. CONCLUSIONS: A cerebral recovery index based on a combination of intermittently extracted, optimally combined quantitative electroencephalography measures provides unequalled prognostic value for comatose patients after cardiac arrest and enables bedside EEG interpretation of unexperienced readers.

Continuous electroencephalography monitoring for early prediction of neurological outcome in postanoxic patients after cardiac arrest: a prospective cohort study.

OBJECTIVE: To evaluate the value of continuous electroencephalography in early prognostication in patients treated with hypothermia after cardiac arrest. DESIGN: Prospective cohort study. SETTING: Medical intensive care unit. PATIENTS: Sixty patients admitted to the intensive care unit for therapeutic hypothermia after cardiac arrest. INTERVENTION: None. MEASUREMENTS AND MAIN RESULTS: In all patients, continuous electroencephalogram and daily somatosensory evoked potentials were recorded during the first 5 days of admission or until intensive care unit discharge. Neurological outcomes were based on each patient's best achieved Cerebral Performance Category score within 6 months. Twenty-seven of 56 patients (48%) achieved good neurological outcome (Cerebral Performance Category score 1-2).At 12 hrs after resuscitation, 43% of the patients with good neurological outcome showed continuous, diffuse slow electroencephalogram rhythms, whereas this was never observed in patients with poor outcome.The sensitivity for predicting poor neurological outcome of low-voltage and isoelectric electroencephalogram patterns 24 hrs after resuscitation was 40% (95% confidence interval 19%-64%) with a 100% specificity (confidence interval 86%-100%), whereas the sensitivity and specificity of absent somatosensory evoked potential responses during the first 24 hrs were 24% (confidence interval 10%-44%) and 100% (confidence interval: 87%-100%), respectively. The negative predictive value for poor outcome of low-voltage and isoelectric electroencephalogram patterns was 68% (confidence interval 50%-81%) compared to 55% (confidence interval 40%-60%) for bilateral somatosensory evoked potential absence, both with a positive predictive value of 100% (confidence interval 63%-100% and 59%-100% respectively). Burst-suppression patterns after 24 hrs were also associated with poor neurological outcome, but not inevitably so. CONCLUSIONS: In patients treated with hypothermia, electroencephalogram monitoring during the first 24 hrs after resuscitation can contribute to the prediction of both good and poor neurological outcome. Continuous patterns within 12 hrs predicted good outcome. Isoelectric or low-voltage electroencephalograms after 24 hrs predicted poor outcome with a sensitivity almost two times larger than bilateral absent somatosensory evoked potential responses.

Predicting Outcome in Postanoxic Coma: Are Ten EEG Electrodes Enough?

INTRODUCTION: Increasing evidence supports that early EEG recordings reliably contribute to outcome prediction in comatose patients with postanoxic encephalopathy. As postanoxic encephalopathy typically results in generalized EEG abnormalities, spatial resolution of a small number of electrodes is likely sufficient, which will reduce set-up time. Here, the authors compare a reduced and a 21-channel EEG for outcome prediction. METHODS: EEG recordings from 142 prospectively collected patients with postanoxic encephalopathy were reassessed by two independent reviewers using a reduced (10 electrodes) bipolar montage. Classification and prognostic accuracy were compared with the full (21 electrodes) montage. The full montage consensus was considered Gold Standard. RESULTS: Sixty-seven patients (47%) had good outcome. The agreement between the individual reviewers using the reduced montage and the Gold Standard score was good (κ = 0.75-0.79). The interobserver agreement was not affected by reducing the number of electrodes (κ = 0.78 for the reduced montage vs. 0.71 for the full montage). An isoelectric, low-voltage, or burst-suppression with identical bursts pattern at 24 hours invariably predicted poor outcome in both montages, with similar prognostic accuracy. A diffusely slowed or normal EEG pattern at 12 hours was associated with good outcome in both montages. CONCLUSIONS: Reducing the number of electrodes from 21 to 10 does not affect EEG classification or prognostic accuracy in patients with postanoxic coma.

[Agranulocytosis and septic shock after metamizole use]. / Agranulocytose en septische shock na metamizolgebruik.

BACKGROUND: Metamizole is an analgesic, the orally administered form of which was withdrawn in the Netherlands in 1989 due to an unacceptably high incidence of agranulocytosis. However, later studies showed a much lower incidence and since 2013 the use of metamizole has been recommended by the national guideline on postoperative pain. CASE DESCRIPTION: A 58-year-old woman was referred by her general practitioner to our hospital with suspected diverticulitis. Three days previously the patient had returned from a four-week period of rehabilitation at a German spa following hip replacement surgery. She had been using metamizole since the operation. Within hours of admission, the patient developed septic shock and was transferred to the intensive care unit. Laboratory tests revealed severe neutropenia of 0.2 × 10(9)/l. Treatment consisted of filgrastim, piperacillin/tazobactam and haemodynamic support. After five days the patient was sufficiently recovered to return to the ward. CONCLUSION: Metamizole-related agranulocytosis is rare but potentially life-threatening. This condition is expected to occur more frequently as the use of metamizole in the Netherlands increases.