European Academy of Neurology and European Stroke Organization consensus statement and practical guidance for pre-hospital management of stroke

Background and purposeThe reduction of delay between onset and hospital arrival and adequate pre-hospital care of persons with acute stroke are important for improving the chances of a favourable outcome. The objective is to recommend evidence-based practices for the management of patients with suspected stroke in the pre-hospital setting. MethodsThe GRADE (Grading of Recommendations Assessment, Development and Evaluation) methodology was used to define the key clinical questions. An expert panel then reviewed the literature, established the quality of the evidence, and made recommendations. ResultsDespite very low quality of evidence educational campaigns to increase the awareness of immediately calling emergency medical services are strongly recommended. Moderate quality evidence was found to support strong recommendations for the training of emergency medical personnel in recognizing the symptoms of a stroke and in implementation of a pre-hospital code stroke' including highest priority dispatch, pre-hospital notification and rapid transfer to the closest stroke-ready' centre. Insufficient evidence was found to recommend a pre-hospital stroke scale to predict large vessel occlusion. Despite the very low quality of evidence, restoring normoxia in patients with hypoxia is recommended, and blood pressure lowering drugs and treating hyperglycaemia with insulin should be avoided. There is insufficient evidence to recommend the routine use of mobile stroke units delivering intravenous thrombolysis at the scene. Because only feasibility studies have been reported, no recommendations can be provided for pre-hospital telemedicine during ambulance transport. ConclusionsThese guidelines inform on the contemporary approach to patients with suspected stroke in the pre-hospital setting. Further studies, preferably randomized controlled trials, are required to examine the impact of particular interventions on quality parameters and outcome. Click for the corresponding questions to this CME article

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

Background: Ongoing or recurrent seizure activity without prominent motor features is a common burden in neurological critical care patients and people with epilepsy during ICU stays. Continuous EEG (CEEG) is the gold standard for detecting ongoing ictal EEG patterns and monitoring functional brain activity. However CEEG review is very demanding and time consuming. The purpose of the present multirater, EEG expert reviewer study, is to test and assess the clinical feasibility of an automatic EEG pattern detection method (Neurotrend).Methods: Four board certified EEG reviewers used Neurotrend to annotate 76 CEEG datasets à 6 h (in total 456 h of EEG) for rhythmic and periodic EEG patterns (RPP), unequivocal ictal EEG patterns and burst suppression. All reviewers had a predefined time limit of 5 min (± 2 min) per CEEG dataset and were compared to a predefined gold standard (conventional EEG review with unlimited time). Subanalysis of specific features of RPP was conducted as well. We used Gwet's AC1 and AC2 coefficients to calculate interrater agreement (IRA) and multirater agreement (MRA). Also, we determined individual performance measures for unequivocal ictal EEG patterns and burst suppression. Bonferroni-Holmes correction for multiple testing was applied to all statistical tests.Results: Mean review time was 3.3 min (± 1.9 min) per CEEG dataset. We found substantial IRA for unequivocal ictal EEG patterns (0.61–0.79; mean sensitivity 86.8%; mean specificity 82.2%, p < 0.001) and burst suppression (0.68–0.71; mean sensitivity 96.7%; mean specificity 76.9% p < 0.001). Two reviewers showed substantial IRA for RPP (0.68–0.72), whereas the other two showed moderate agreement (0.45–0.54), compared to the gold standard (p < 0.001). MRA showed almost perfect agreement for burst suppression (0.86) and moderate agreement for RPP (0.54) and unequivocal ictal EEG patterns (0.57).Conclusions: We demonstrated the clinical feasibility of an automatic critical care EEG pattern detection method on two levels: (1) reasonable high agreement compared to the gold standard, (2) reasonable short review times compared to previously reported EEG review times with conventional EEG analysis

Data_Sheet_2_Automated Long-Term EEG Review: Fast and Precise Analysis in Critical Care Patients.DOCX

<p>Background: Ongoing or recurrent seizure activity without prominent motor features is a common burden in neurological critical care patients and people with epilepsy during ICU stays. Continuous EEG (CEEG) is the gold standard for detecting ongoing ictal EEG patterns and monitoring functional brain activity. However CEEG review is very demanding and time consuming. The purpose of the present multirater, EEG expert reviewer study, is to test and assess the clinical feasibility of an automatic EEG pattern detection method (Neurotrend).</p><p>Methods: Four board certified EEG reviewers used Neurotrend to annotate 76 CEEG datasets à 6 h (in total 456 h of EEG) for rhythmic and periodic EEG patterns (RPP), unequivocal ictal EEG patterns and burst suppression. All reviewers had a predefined time limit of 5 min (± 2 min) per CEEG dataset and were compared to a predefined gold standard (conventional EEG review with unlimited time). Subanalysis of specific features of RPP was conducted as well. We used Gwet's AC₁ and AC₂ coefficients to calculate interrater agreement (IRA) and multirater agreement (MRA). Also, we determined individual performance measures for unequivocal ictal EEG patterns and burst suppression. Bonferroni-Holmes correction for multiple testing was applied to all statistical tests.</p><p>Results: Mean review time was 3.3 min (± 1.9 min) per CEEG dataset. We found substantial IRA for unequivocal ictal EEG patterns (0.61–0.79; mean sensitivity 86.8%; mean specificity 82.2%, p < 0.001) and burst suppression (0.68–0.71; mean sensitivity 96.7%; mean specificity 76.9% p < 0.001). Two reviewers showed substantial IRA for RPP (0.68–0.72), whereas the other two showed moderate agreement (0.45–0.54), compared to the gold standard (p < 0.001). MRA showed almost perfect agreement for burst suppression (0.86) and moderate agreement for RPP (0.54) and unequivocal ictal EEG patterns (0.57).</p><p>Conclusions: We demonstrated the clinical feasibility of an automatic critical care EEG pattern detection method on two levels: (1) reasonable high agreement compared to the gold standard, (2) reasonable short review times compared to previously reported EEG review times with conventional EEG analysis.</p