Editorial
Brain-focused care in the neonatal intensive care unit: the time has come
Cuidado neurológico na unidade de terapia intensiva neonatal: chegou a hora
Krisa Page Van Meurs
, Sonia Lomeli Bonifacio
Corresponding author
Stanford University School of Medicine, Department of Pediatrics, Division of Neonatal and Developmental Medicine, Palo Alto, California, United States
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"vanmeurs@stanford.edu" ] "referencia" => array:1 [ 0 => array:2 [ "etiqueta" => "<span class="elsevierStyleSup">*</span>" "identificador" => "cor0005" ] ] ] 1 => array:2 [ "nombre" => "Sonia Lomeli" "apellidos" => "Bonifacio" ] ] "afiliaciones" => array:1 [ 0 => array:2 [ "entidad" => "Stanford University School of Medicine, Department of Pediatrics, Division of Neonatal and Developmental Medicine, Palo Alto, California, United States" "identificador" => "aff0005" ] ] "correspondencia" => array:1 [ 0 => array:3 [ "identificador" => "cor0005" "etiqueta" => "⁎" "correspondencia" => "Corresponding author." ] ] ] ] "titulosAlternativos" => array:1 [ "es" => array:1 [ "titulo" => "Cuidado neurológico na unidade de terapia intensiva neonatal: chegou a hora" ] ] "textoCompleto" => "<span class="elsevierStyleSections"><p id="par0005" class="elsevierStylePara elsevierViewall">Survival rates for extremely preterm infants and for critically ill term newborns have improved steadily over the last several decades; however, these same babies continue to experience high rates of adverse neurodevelopmental outcomes with life-long impact. Brain-focused care is a desired evolution in neonatal care after decades of focus on survival and extending the limits of viability. Neonatal neurology and neonatal neurocritical care are growing subspecialties that seek to better address the needs of neonates with, or at risk for, neurological compromise by integrating neonatal intensive care practices with focused neurologic care. The development and application of bedside neuromonitoring has significantly contributed to the enhanced focus and our ability to both monitor and provide care for these vulnerable newborns. Non-invasive neurologic monitoring with techniques such as amplitude-integrated electroencephalography (aEEG) and near-infrared spectroscopy (NIRS) allow screening and assessment at the bedside by neonatal nurses and physicians.</p><p id="par0010" class="elsevierStylePara elsevierViewall">In this issue of the <span class="elsevierStyleItalic">Jornal de Pediatria</span>, Variane et al.<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">1</span></a> described a prospective cohort study of 23 preterm infants less than 31 weeks gestation and 17 term newborns with hypoxic ischemic encephalopathy (HIE). Subjects were monitored with aEEG with assessment of background activity, sleep–wake cycling (SWC), and presence of seizures on days 1, 2, and 3 of life. In the preterm group, abnormal background pattern and absence of SWC were the aEEG findings associated with death or severe abnormalities on cranial ultrasound. Abnormal background pattern was defined as discontinuous low-voltage, burst suppression, continuous low voltage, or flat tracing. In the term HIE group, seizures and longer time to normal background tracing were the aEEG features associated with death and MRI abnormalities.</p><p id="par0015" class="elsevierStylePara elsevierViewall">The research findings presented by Variane et al.<a class="elsevierStyleCrossRef" href="#bib0105"><span class="elsevierStyleSup">1</span></a> add to a growing body of evidence supporting the use of aEEG in the neonatal intensive care unit. aEEG was first developed as a tool to assess the depth of anesthesia during surgery, providing real-time assessment of brain activity during exposure to anesthetic agents. aEEG monitoring devices now display both a limited channel EEG as well as a time-compressed aEEG trace allowing evaluation of background activity, displaying changes in background activity over time, and screening for seizures. The first background classification system, developed by Hellström-Westas, was based on pattern recognition to distinguish between five categories: continuous normal voltage, discontinuous normal voltage, burst suppression, continuous low voltage, and flat tracing.<a class="elsevierStyleCrossRef" href="#bib0110"><span class="elsevierStyleSup">2</span></a> Another classification method was developed by al Naqueeb based on simple voltage criteria.<a class="elsevierStyleCrossRef" href="#bib0115"><span class="elsevierStyleSup">3</span></a> A more consistent interpretation was found with the simple voltage criteria than with pattern recognition in one study<a class="elsevierStyleCrossRef" href="#bib0120"><span class="elsevierStyleSup">4</span></a>; however, the pattern recognition classification system remains widely used. aEEG has been shown to have good agreement with EEG background classification when studied in term newborns with HIE,<a class="elsevierStyleCrossRef" href="#bib0125"><span class="elsevierStyleSup">5</span></a> but no similar comparison has been performed in preterm infants. Term infants with neonatal encephalopathy were one of the first diagnostic groups to be studied with aEEG. Numerous early aEEG studies performed prior to use of therapeutic hypothermia determined that abnormal background patterns are a predictor of outcome in neonates with HIE. Accordingly, abnormal aEEG background pattern at less than 6<span class="elsevierStyleHsp" style=""></span>h of age was used as an eligibility criteria in several trials of therapeutic hypothermia for HIE.<a class="elsevierStyleCrossRefs" href="#bib0130"><span class="elsevierStyleSup">6,7</span></a> Thoresen et al. performed an important study of continuous aEEG for 72<span class="elsevierStyleHsp" style=""></span>h in term infants with HIE in cooled (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>43) and non-cooled (<span class="elsevierStyleItalic">n</span><span class="elsevierStyleHsp" style=""></span>=<span class="elsevierStyleHsp" style=""></span>31) newborns.<a class="elsevierStyleCrossRef" href="#bib0140"><span class="elsevierStyleSup">8</span></a> Recovery time to normal background pattern was found to be the best aEEG predictor of abnormal outcome at 18 months of age. In this analysis, normal background pattern included both continuous normal voltage and discontinuous normal voltage. Infants with a good outcome treated with normothermia had normal tracings by 24<span class="elsevierStyleHsp" style=""></span>h, whereas those treated with hypothermia had normal tracings by 48<span class="elsevierStyleHsp" style=""></span>h. Massaro confirmed the high positive predictive value of abnormal aEEG background for adverse outcome at hospital discharge.<a class="elsevierStyleCrossRef" href="#bib0145"><span class="elsevierStyleSup">9</span></a> SWC were present at the time of rewarming in 58% and all had a favorable outcome, while no babies with adverse outcome had SWC at the time of rewarming. A meta-analysis of eight studies in term infants with HIE concluded that aEEG had an overall sensitivity of 91% (95% CI: 87–95) and specificity of 88% (95% CI: 84–92) to predict poor outcome.<a class="elsevierStyleCrossRef" href="#bib0150"><span class="elsevierStyleSup">10</span></a> A recent meta-analysis of 31 aEEG studies concluded that burst suppression, continuous low voltage, and flat tracing are the aEEG background patterns that most accurately predict long term neurodevelopmental sequelae.<a class="elsevierStyleCrossRef" href="#bib0155"><span class="elsevierStyleSup">11</span></a></p><p id="par0020" class="elsevierStylePara elsevierViewall">Due to the high risk of neurodevelopmental impairment in extremely preterm infants, methods to assess the risk have been sought. The etiology of preterm brain injury is assumed to be multifactorial, including events in the peri-partum period as well as acquired white matter injury, inflammation, and infections that may occur during hospitalization. Useful assessments have included clinical risk scores, neuroimaging, and early brain function. As neonatal brain function can be readily assessed using aEEG, it has been intensely investigated as a prognostic tool. Background pattern, SWC, and seizures have been used to prognosticate with several studies showing a good correlation with outcome.<a class="elsevierStyleCrossRefs" href="#bib0160"><span class="elsevierStyleSup">12,13</span></a> A scoring system to objectively assess developmental maturation at increasing gestational and postnatal ages was developed by Burdjalov et al.<a class="elsevierStyleCrossRef" href="#bib0170"><span class="elsevierStyleSup">14</span></a> Their scoring system uses measures of continuity, presence of cyclic changes, degree of voltage amplitude depression, and bandwidth. The cycling score appeared to have the highest correlation with post-conceptual age and was felt to be the single best sign of cerebral maturity. A recent meta-analysis of the prognostic accuracy of early (within 7 days of life) aEEG or EEG to predict neurodevelopmental outcome at 1–10 years of age concluded that these measures have the potential to predict later neurodevelopmental outcome; however, there was substantial heterogeneity among studies with differing prognostic variables and outcomes.<a class="elsevierStyleCrossRef" href="#bib0175"><span class="elsevierStyleSup">15</span></a> They concluded that high-quality studies are needed to confirm these findings.</p><p id="par0025" class="elsevierStylePara elsevierViewall">aEEG has several advantages over continuous EEG (cEEG) but it does not replace it as the gold standard for seizure diagnosis or for the evaluation of the EEG background brain activity. Due to the limited number of channels that are recorded (usually left and right parietal or central leads), aEEG is easy to apply without the need of an EEG technician. Favorable characteristics of aEEG include the following: aEEG is often available in clinical settings where full conventional EEG is not readily available; aEEG's lead application is easy to learn and is not a time-consuming procedure; aEEG can be used to monitor for long periods of time without burdening neurophysiologists, aEEG recording devices are easy to use and have a small bedside footprint; aEEG can be incorporated into the software of conventional EEG devices, allowing for simultaneous recording and display of the aEEG compressed trace as well as the full video-EEG; aEEG is easy to interpret with a pattern based classification system that parallels the classification of conventional EEG, but does not require extensive training in neurophysiology; and finally, the prognostic ability of aEEG may be superior to more subjective evaluations such as the neonatal neurologic exam. aEEG does have important limitations especially when being used to diagnose seizures. Due to the manner in which the signal is recorded and how the compressed aEEG trace is created, some seizures can be missed. First, aEEG only records EEG signal from a limited number of channels/regions of the brain. Seizures that arise in areas away from the recording leads may not be captured and therefore can be missed. In addition, seizures that are brief (<30<span class="elsevierStyleHsp" style=""></span>s) or low amplitude may be difficult to identify on the compressed trace. Using only the compressed aEEG trace to identify seizures results in low sensitivity and specificity for seizure recognition; therefore, both the compressed and raw traces should be evaluated.<a class="elsevierStyleCrossRef" href="#bib0180"><span class="elsevierStyleSup">16</span></a> Newer aEEG devices have incorporated seizure detection software to assist bedside clinicians in identifying seizures. Based on a survey of US neonatologists performed in 2012, 55% of neonatologists reported using aEEG in their practice. HIE and suspected seizures were the most common indications for use, and aEEG was primarily interpreted by neonatologists (87%).<a class="elsevierStyleCrossRef" href="#bib0185"><span class="elsevierStyleSup">17</span></a> Pediatric neurologists acknowledge the important role aEEG plays in the NICU to identify seizures and assess brain function. It lessens the demand for conventional video EEG, which is more costly, requiring specialized EEG technicians to perform the recordings and neurophysiologists for interpretation. Glass et al. encourage pediatric neurologists to learn aEEG interpretation in order to improve communication and care coordination at the bedside.<a class="elsevierStyleCrossRef" href="#bib0190"><span class="elsevierStyleSup">18</span></a></p><p id="par0030" class="elsevierStylePara elsevierViewall">Brain-focused care is now possible in NICUs equipped with neuromonitoring techniques such as aEEG and NIRS. The inevitable and critical question is whether the use of these neuromonitoring techniques will improve long-term neurodevelopmental outcomes. The wider use of aEEG has the potential to increase seizure identification, decrease seizure burden, and potentially minimize exposure to anticonvulsant medications by accurately identifying patients with electrographic seizures. Two recent investigations have dealt with the question of whether aggressive treatment of neonatal seizures decreases brain injury.<a class="elsevierStyleCrossRefs" href="#bib0195"><span class="elsevierStyleSup">19,20</span></a> Van Rooij et al. found a significant relationship between seizure duration and MRI severity scores, supporting the assumption that seizures worsen existing brain injury.<a class="elsevierStyleCrossRef" href="#bib0195"><span class="elsevierStyleSup">19</span></a> A study by Srinvasakumar et al. compared newborns treated for electrographic seizures to those treated for clinical seizures alone; seizure burden, MRI findings, and neurodevelopmental outcome were improved in the cohort with treatment of electrographic seizures.<a class="elsevierStyleCrossRef" href="#bib0200"><span class="elsevierStyleSup">20</span></a> We eagerly anticipate additional clinical studies using neuromonitoring techniques such as aEEG and NIRS that will provide us the evidence on how these technologies may be best used to optimize intensive care practices and lessen brain injury.</p><span id="sec0005" class="elsevierStyleSection elsevierViewall"><span class="elsevierStyleSectionTitle" id="sect0005">Conflicts of interest</span><p id="par0035" class="elsevierStylePara elsevierViewall">The authors declare no conflicts of interest.</p></span></span>" "textoCompletoSecciones" => array:1 [ "secciones" => array:2 [ 0 => array:2 [ "identificador" => "sec0005" "titulo" => "Conflicts of interest" ] 1 => array:1 [ "titulo" => "References" ] ] ] "pdfFichero" => "main.pdf" "tienePdf" => true "NotaPie" => array:2 [ 0 => array:2 [ "etiqueta" => "☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0005">Please cite this article as: Van Meurs KP, Bonifacio SL. Brain-focused care in the neonatal intensive care unit: the time has come. J Pediatr (Rio J). 2017;93:439–41.</p>" ] 1 => array:2 [ "etiqueta" => "☆☆" "nota" => "<p class="elsevierStyleNotepara" id="npar0010">See paper by Variane et al. in pages 460–6.</p>" ] ] "bibliografia" => array:2 [ "titulo" => "References" "seccion" => array:1 [ 0 => array:2 [ "identificador" => "bibs0005" "bibliografiaReferencia" => array:20 [ 0 => array:3 [ "identificador" => "bib0105" "etiqueta" => "1" "referencia" => array:1 [ 0 => array:2 [ "contribucion" => array:1 [ 0 => array:2 [ "titulo" => "Early amplitude-integrated electroencephalography for monitoring neonates at high risk for brain injury" "autores" => array:1 [ 0 => array:2 [ "etal" => true "autores" => array:6 [ 0 => "G.F. Variane" 1 => "M. Magalhães" 2 => "R. Gasperine" 3 => "H.C. Alves" 4 => "T.L. Scoppetta" 5 => "R.J. 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| Year/Month | Html | Total | |
|---|---|---|---|
| 2024 November | 1 | 5 | 6 |
| 2024 October | 45 | 20 | 65 |
| 2024 September | 40 | 20 | 60 |
| 2024 August | 40 | 26 | 66 |
| 2024 July | 32 | 32 | 64 |
| 2024 June | 20 | 14 | 34 |
| 2024 May | 13 | 10 | 23 |
| 2024 April | 22 | 22 | 44 |
| 2024 March | 23 | 22 | 45 |
| 2024 February | 22 | 26 | 48 |
| 2024 January | 19 | 26 | 45 |
| 2023 December | 14 | 25 | 39 |
| 2023 November | 12 | 29 | 41 |
| 2023 October | 22 | 23 | 45 |
| 2023 September | 19 | 32 | 51 |
| 2023 August | 16 | 15 | 31 |
| 2023 July | 19 | 10 | 29 |
| 2023 June | 12 | 13 | 25 |
| 2023 May | 15 | 16 | 31 |
| 2023 April | 17 | 6 | 23 |
| 2023 March | 27 | 20 | 47 |
| 2023 February | 33 | 15 | 48 |
| 2023 January | 18 | 19 | 37 |
| 2022 December | 37 | 19 | 56 |
| 2022 November | 19 | 26 | 45 |
| 2022 October | 49 | 36 | 85 |
| 2022 September | 23 | 34 | 57 |
| 2022 August | 26 | 34 | 60 |
| 2022 July | 26 | 30 | 56 |
| 2022 June | 24 | 29 | 53 |
| 2022 May | 24 | 23 | 47 |
| 2022 April | 47 | 28 | 75 |
| 2022 March | 25 | 31 | 56 |
| 2022 February | 16 | 10 | 26 |
| 2022 January | 10 | 22 | 32 |
| 2021 December | 12 | 14 | 26 |
| 2021 November | 8 | 15 | 23 |
| 2021 October | 12 | 21 | 33 |
| 2021 September | 8 | 14 | 22 |
| 2021 August | 7 | 15 | 22 |
| 2021 July | 2 | 8 | 10 |
| 2021 June | 9 | 1 | 10 |
| 2021 May | 9 | 15 | 24 |
| 2021 April | 14 | 21 | 35 |
| 2021 March | 9 | 5 | 14 |
| 2021 February | 7 | 3 | 10 |
| 2021 January | 9 | 13 | 22 |
| 2020 December | 8 | 11 | 19 |
| 2020 November | 13 | 14 | 27 |
| 2020 October | 6 | 11 | 17 |
| 2020 September | 9 | 8 | 17 |
| 2020 August | 1 | 2 | 3 |
| 2020 July | 5 | 6 | 11 |
| 2020 June | 9 | 3 | 12 |
| 2020 May | 4 | 2 | 6 |
| 2020 April | 5 | 14 | 19 |
| 2020 March | 4 | 3 | 7 |
| 2020 February | 11 | 11 | 22 |
| 2020 January | 12 | 17 | 29 |
| 2019 December | 7 | 3 | 10 |
| 2019 November | 4 | 4 | 8 |
| 2019 October | 8 | 10 | 18 |
| 2019 September | 7 | 7 | 14 |
| 2019 August | 6 | 9 | 15 |
| 2019 July | 12 | 9 | 21 |
| 2019 June | 7 | 14 | 21 |
| 2019 May | 13 | 8 | 21 |
| 2019 April | 10 | 7 | 17 |
| 2019 March | 4 | 3 | 7 |
| 2019 February | 3 | 3 | 6 |
| 2019 January | 8 | 5 | 13 |
| 2018 December | 4 | 7 | 11 |
| 2018 November | 7 | 5 | 12 |
| 2018 October | 12 | 8 | 20 |
| 2018 September | 8 | 6 | 14 |
| 2018 August | 6 | 11 | 17 |
| 2018 July | 5 | 2 | 7 |
| 2018 June | 8 | 3 | 11 |
| 2018 May | 25 | 6 | 31 |
| 2018 April | 13 | 0 | 13 |
| 2018 March | 24 | 6 | 30 |
| 2018 February | 8 | 5 | 13 |
| 2018 January | 10 | 5 | 15 |
| 2017 December | 8 | 3 | 11 |
| 2017 November | 17 | 5 | 22 |
| 2017 October | 43 | 16 | 59 |
| 2017 September | 12 | 9 | 21 |
| 2017 August | 0 | 2 | 2 |
| 2017 July | 0 | 2 | 2 |
| 2017 June | 1 | 2 | 3 |
| 2017 May | 0 | 6 | 6 |
| 2017 April | 0 | 12 | 12 |
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