Neonatal resuscitation using a supraglottic airway device for improved mortality and morbidity outcomes in a low-income country: Study protocol for a randomized trial.

Background Intrapartum-related death is the third leading cause of under-5 mortality. Effective ventilation during neonatal resuscitation has the potential to reduce 40% of these deaths. Face-mask ventilation performed by midwives is globally the most common method of resuscitating neonates. It requires considerable operator skills and continuous training because of its complexity. The i-gel® is a cuffless supraglottic airway which is easy to insert and provides an efficient seal that prevents air leakage; it has the potential to enhance performance in neonatal resuscitation. A pilot study in Uganda demonstrated that midwives could safely resuscitate newborns with the i-gel® after a short training session. The aim of the present trial is to investigate whether the use of a cuffless supraglottic airway device compared with face-mask ventilation during neonatal resuscitation can reduce mortality and morbidity in asphyxiated neonates. Methods A randomized phase III open-label superiority controlled clinical trial will be conducted at Mulago Hospital, Kampala, Uganda, in asphyxiated neonates in the delivery units. Prior to the intervention, health staff performing resuscitation will receive training in accordance with the Helping Babies Breathe curriculum with a special module for training on supraglottic airway insertion. A total of 1150 to 1240 babies (depending on cluster size) that need positive pressure ventilation and that have an expected gestational age of more than 34 weeks and an expected birth weight of more than 2000 g will be ventilated by daily unmasked randomization with a supraglottic airway device (i-gel®) (intervention group) or with a face mask (control group). The primary outcome will be a composite outcome of 7-day mortality and admission to neonatal intensive care unit (NICU) with neonatal encephalopathy. Discussion Although indications for the beneficial effect of a supraglottic airway device in the context of neonatal resuscitation exist, so far no large studies powered to assess mortality and morbidity have been carried out. We hypothesize that effective ventilation will be easier to achieve with a supraglottic airway device than with a face mask, decreasing early neonatal mortality and brain injury from neonatal encephalopathy. The findings of this trial will be important for low and middle-resource settings where the majority of intrapartum-related events occur.publishedVersio

Establishment of reference values for plasma neurofilament light based on healthy individuals aged 5-90 years

The recent development of assays that accurately quantify neurofilament light, a neuronal cytoskeleton protein, in plasma has generated a vast literature supporting that it is a sensitive, dynamic, and robust biomarker of neuroaxonal damage. As a result, efforts are now made to introduce plasma neurofilament light into clinical routine practice, making it an easily accessible complement to its cerebrospinal fluid counterpart. An increasing literature supports the use of plasma neurofilament light in differentiating neurodegenerative diseases from their non-neurodegenerative mimics and suggests it is a valuable biomarker for the evaluation of the effect of putative disease-modifying treatments (e.g. in multiple sclerosis). More contexts of use will likely emerge over the coming years. However, to assist clinical interpretation of laboratory test values, it is crucial to establish normal reference intervals. In this study, we sought to derive reliable cut-offs by pooling quantified plasma neurofilament light in neurologically healthy participants (5-90 years) from eight cohorts. A strong relationship between age and plasma neurofilament light prompted us to define the following age-partitioned reference limits (upper 95th percentile in each age category): 5-17 years = 7 pg/mL; 18-50 years = 10 pg/mL; 51-60 years = 15 pg/mL; 61-70 years = 20 pg/mL; 70 + years = 35 pg/mL. The established reference limits across the lifespan will aid the introduction of plasma neurofilament light into clinical routine, and thereby contribute to diagnostics and disease-monitoring in neurological practice

Cerebrospinal fluid markers before and after shunting in patients with secondary and idiopathic normal pressure hydrocephalus

<p>Abstract</p> <p>Background</p> <p>The aim of this study was to explore biochemical changes in the cerebrospinal fluid (CSF) induced by shunt surgery and the relationship between these changes and clinical improvement.</p> <p>Methods</p> <p>We measured clinical symptoms and analysed lumbar CSF for protein content, neurodegeneration and neurotransmission markers in patients with secondary (SNPH, n = 17) and idiopathic NPH (INPH, n = 18) before and 3 months after shunt surgery. Patients were divided into groups according to whether or not there was improvement in clinical symptoms after surgery.</p> <p>Results</p> <p>Preoperatively, the only pathological findings were elevated neurofilament protein (NFL), significantly more so in the SNPH patients than in the INPH patients, and elevated albumin content. Higher levels of NFL correlated with worse gait, balance, wakefulness and neuropsychological performance. Preoperatively, no differences were seen in any of the CSF biomarkers between patients that improved after surgery and those that did not improve. Postoperatively, a greater improvement in gait and balance performance correlated with a more pronounced reduction in NFL. Levels of albumin, albumin ratio, neuropeptide Y, vasoactive intestinal peptide and ganglioside GD3 increased significantly after shunting in both groups. In addition, Gamma amino butyric acid increased significantly in SNPH and tau in INPH.</p> <p>Conclusion</p> <p>We conclude that a number of biochemical changes occur after shunt surgery, but there are no marked differences between the SNPH and INPH patients. The results indicate that NFL may be a marker that can predict a surgically reversible state in NPH.</p

Neonatal Seizure Management - Is the Timing of Treatment Critical?

Objective: To assess the impact of the time to treatment of the first electrographic seizure on subsequent seizure burden and describe overall seizure management in a large neonatal cohort. Study design: Newborns (36-44 weeks of gestation) requiring electroencephalographic (EEG) monitoring recruited to 2 multicenter European studies were included. Infants who received antiseizure medication exclusively after electrographic seizure onset were grouped based on the time to treatment of the first seizure: antiseizure medication within 1 hour, between 1 and 2 hours, and after 2 hours. Outcomes measured were seizure burden, maximum seizure burden, status epilepticus, number of seizures, and antiseizure medication dose over the first 24 hours after seizure onset. Results: Out of 472 newborns recruited, 154 (32.6%) had confirmed electrographic seizures. Sixty-nine infants received antiseizure medication exclusively after the onset of electrographic seizure, including 21 infants within 1 hour of seizure onset, 15 between 1 and 2 hours after seizure onset, and 33 at >2 hours after seizure onset. Significantly lower seizure burden and fewer seizures were noted in the infants treated with antiseizure medication within 1 hour of seizure onset (P =.029 and.035, respectively). Overall, 258 of 472 infants (54.7%) received antiseizure medication during the study period, of whom 40 without electrographic seizures received treatment exclusively during EEG monitoring and 11 with electrographic seizures received no treatment. Conclusions: Treatment of neonatal seizures may be time-critical, but more research is needed to confirm this. Improvements in neonatal seizure diagnosis and treatment are also needed

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial

Background: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR).Methods: This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (non algorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780.Findings: Between Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25.0%) of 128 neonates in the algorithm group and 38 (29.2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81.3% (95% CI 66.7-93.3) in the algorithm group and 89.5% (78.4-97.5) in the non-algorithm group; specificity was 84.4% (95% CI 76.9-91.0) in the algorithm group and 89.1% (82.5-94.7) in the non-algorithm group; and the false detection rate was 36.6% (95% CI 22.7-52.1) in the algorithm group and 22.7% (11.6-35.9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the non algorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66.0%; 95% CI 53.8-77.3] of 268 h vs 177 [45.3%; 34.5-58.3] of 391 h; difference 20.8% [3.6-37.1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37.5% [95% CI 25.0 to 56.3] vs 31.6% [21.1 to 47.4]; difference 5.9% [-14.0 to 26.3]).Interpretation ANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required

A machine-learning algorithm for neonatal seizure recognition: a multicentre, randomised, controlled trial

BACKGROUND: Despite the availability of continuous conventional electroencephalography (cEEG), accurate diagnosis of neonatal seizures is challenging in clinical practice. Algorithms for decision support in the recognition of neonatal seizures could improve detection. We aimed to assess the diagnostic accuracy of an automated seizure detection algorithm called Algorithm for Neonatal Seizure Recognition (ANSeR). METHODS: This multicentre, randomised, two-arm, parallel, controlled trial was done in eight neonatal centres across Ireland, the Netherlands, Sweden, and the UK. Neonates with a corrected gestational age between 36 and 44 weeks with, or at significant risk of, seizures requiring EEG monitoring, received cEEG plus ANSeR linked to the EEG monitor displaying a seizure probability trend in real time (algorithm group) or cEEG monitoring alone (non-algorithm group). The primary outcome was diagnostic accuracy (sensitivity, specificity, and false detection rate) of health-care professionals to identify neonates with electrographic seizures and seizure hours with and without the support of the ANSeR algorithm. Neonates with data on the outcome of interest were included in the analysis. This study is registered with ClinicalTrials.gov, NCT02431780. FINDINGS: Between Feb 13, 2015, and Feb 7, 2017, 132 neonates were randomly assigned to the algorithm group and 132 to the non-algorithm group. Six neonates were excluded (four from the algorithm group and two from the non-algorithm group). Electrographic seizures were present in 32 (25·0%) of 128 neonates in the algorithm group and 38 (29·2%) of 130 neonates in the non-algorithm group. For recognition of neonates with electrographic seizures, sensitivity was 81·3% (95% CI 66·7-93·3) in the algorithm group and 89·5% (78·4-97·5) in the non-algorithm group; specificity was 84·4% (95% CI 76·9-91·0) in the algorithm group and 89·1% (82·5-94·7) in the non-algorithm group; and the false detection rate was 36·6% (95% CI 22·7-52·1) in the algorithm group and 22·7% (11·6-35·9) in the non-algorithm group. We identified 659 h in which seizures occurred (seizure hours): 268 h in the algorithm versus 391 h in the non-algorithm group. The percentage of seizure hours correctly identified was higher in the algorithm group than in the non-algorithm group (177 [66·0%; 95% CI 53·8-77·3] of 268 h vs 177 [45·3%; 34·5-58·3] of 391 h; difference 20·8% [3·6-37·1]). No significant differences were seen in the percentage of neonates with seizures given at least one inappropriate antiseizure medication (37·5% [95% CI 25·0 to 56·3] vs 31·6% [21·1 to 47·4]; difference 5·9% [-14·0 to 26·3]). INTERPRETATION: ANSeR, a machine-learning algorithm, is safe and able to accurately detect neonatal seizures. Although the algorithm did not enhance identification of individual neonates with seizures beyond conventional EEG, recognition of seizure hours was improved with use of ANSeR. The benefit might be greater in less experienced centres, but further study is required. FUNDING: Wellcome Trust, Science Foundation Ireland, and Nihon Kohden