Neonatal donation:are newborns too young to be recognized?

Neonatal organ and tissue donation is not common practice in the Netherlands. At the same time, there is a transplant waiting list for small size-matched organs and tissues. Multiple factors may contribute to low neonatal donation rates, including a lack of awareness of this option. This study provides insight into potential neonatal organ and tissue donors and reports on how many donors were actually reported to the procurement organization. We performed a retrospective analysis of the mortality database and medical records of two largest neonatal intensive care units (NICUs) in the Netherlands. This study reviewed records of neonates with a gestational age >37 weeks and weight >3000g who died in the period from January 1, 2005 through December 31, 2016. During the study period, 259 term-born neonates died in the two NICUs. In total, 132 neonates with general contraindications for donation were excluded. The medical records of 127 neonates were examined for donation suitability. We identified five neonates with documented brain death who were not recognized as potential organ and/or tissue donors. Of the remaining neonates, 27 were found suitable for tissue donation. One potential tissue donor had been reported to the procurement organization. In three cases, the possibility of donation was brought up by parents. Conclusion: A low proportion (2%) of neonates who died in the NICUs were found suitable for organ donation, and a higher proportion (12%) were found suitable for tissue donation. We suggest that increased awareness concerning the possibility of neonatal donation would likely increase the identification of potential neonatal donors

Технология извлечения структур знаний с использованием аппарата расширенных семантических сетей

В статье рассматривается задача извлечения из текстов естественного языка структур знаний: информационных объектов («именованных сущностей»), их свойств, связей и фактов участия в действиях. Для этих целей разработан инструментарий: язык представления знаний (расширенные семантические сети – РСС) и их обработки (язык преобразования структур – ДЕКЛ). На этой основе созданы технологии, которые обладают следующими особенностями. Из текстов извлекаются не отдельные объекты (именованные сущности), а структуры знаний, представляющие связи объектов и их участие в действиях и событиях. Для извлечения структур знаний разработан уникальный семантико-ориентированный лингвистический процессор (ЛП), осуществляющий глубинный анализ текстов ЕЯ и выявляющий десятки типов объектов вместе с их структурами. Процессор ЛП управляется лингвистическими знаниями, представляющими собой декларативные структуры и обеспечивающие быструю настройку ЛП на предметную область и язык. Основой лингвистических знаний являются правила, обладающие высокой степенью избирательности при выявлении объектов («сущностей»), средствами устранения коллизий при их применении. Это позволяет минимизировать шумы и потери.У статті розглядається задача знайдення у текстах природної мови структур знань: інформаційних об’єктів («іменованих сутностей»), їх якостей зв’язків і фактів участі у діях. Для цих цілей розроблений інструментарій: мова представлення знань (розширені семантичні мережі – РСМ) та їх обробки (мова перетворення структур – ДЕКЛ). На цій основі створені технології, що мають наступні особливості. З тестів виділяються не окремі об’єкти (іменовані сутності), а структури знань, що представляють зв’язки об’єктів та їх участь у діях та подіях. З метою виділення структур знань розроблений винятковий семантико-орієнтований лінгвістичний процесор (ЛП), що здійснює глибинний аналіз текстів ЕЯ та виявляє десятки типів об’єктів разом з їх структурами. Процесор ЛП керується лінгвістичними знаннями, які представляють собою декларативні структури та забезпечують швидке настроювання ЛП на предметну сферу та мову. Основою лінгвістичних знань є правила, що мають високий ступінь вибірковості при виявленні об’єктів («сутностей»), засобами усунення колізій при їхньому використанні. Це дозволяє мінімізувати шуми та втрати.The paper is devoted to the extracting of knowledge structures from the natural language texts, i.e. information objects (“Named Entities”), their features, relationships, and participation in the actions and events. For this purpose, the language used for knowledge representation (extended semantic networks/ESN) and tools for processing (language for structure conversion LSC) are considered. On this base, the new technologies are proposed. These technologies have the following features: extraction from the texts of knowledge structures that represent the links of named entities and their participation in actions and events. For the knowledge extraction the unique semantic-oriented language processor (LP) are designed. Processor LP provides the deep analysis of NL-texts and revealing set of objects together with their structures. Processor LP is controlled by the linguistic knowledge, which are declarative structures (on ESN) and which provides the quick tuning of LP on subject area and language, both Russian and English

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

Impact of patent ductus arteriosus and subsequent therapy with indomethacin on cerebral oxygenation in preterm infants

OBJECTIVES. A hemodynamically important patent ductus arteriosus is a common problem in the first week of life in the preterm infant. Although patent ductus arteriosus induces alterations in organ perfusion, scarce information is available of the impact of patent ductus arteriosus and its subsequent treatment on the oxygen supply and oxygen extraction of the brain. We investigated the impact of patent ductus arteriosus and its treatment with indomethacin on regional cerebral oxygen saturation and fractional tissue oxygen extraction by using near-infrared spectroscopy. PATIENTS AND METHODS. Twenty infants with patent ductus arteriosus (gestational age: RESULTS. Mean arterial blood pressure and regional cerebral oxygen saturation were significantly lower and fractional tissue oxygen extraction significantly higher compared with the control infants during patent ductus arteriosus (mean arterial blood pressure: 33 +/- 5 vs 38 +/- 6 mm Hg; regional cerebral oxygen saturation: 62% +/- 9% vs 72% +/- 10%; fractional tissue oxygen extraction: 0.34 +/- 0.1 vs 0.25 +/- 0.1, respectively). Regional cerebral oxygen saturation and fractional tissue oxygen extraction were lower and higher, respectively, up to 24 hours after the start of indomethacin but normalized to control values afterward. Indomethacin had no additional negative effect on cerebral oxygenation. CONCLUSIONS. A hemodynamically significant patent ductus arteriosus has a negative effect on cerebral oxygenation in the premature infant. Subsequent and adequate treatment of a patent ductus arteriosus may prevent diminished cerebral perfusion and subsequent decreased oxygen delivery, which reduces the change of damage to the vulnerable immature brain

The Use of Amplitude Integrated Electroencephalography for Assessing Neonatal Neurologic Injury

Amplitude-integrated electroencephalography (aEEG) plays an important role in integrated care of the full-term infant with neonatal encephalopathy. The three main features that are provided with aEEG are the background pattern on admission and the rate of recovery seen during the first 24 to 48 hours after birth, the presence of most electrographic discharges, and the effect of antiepileptic drugs

A Comparison of the Thompson Encephalopathy Score and Amplitude-Integrated Electroencephalography in Infants with Perinatal Asphyxia and Therapeutic Hypothermia

BACKGROUND: In previous studies clinical signs or amplitude-integrated electroencephalography (aEEG)-based signs of encephalopathy were used to select infants with perinatal asphyxia for treatment with hypothermia. AIM: The objective of this study was to compare Thompson encephalopathy scores and aEEG, and relate both to outcome. SUBJECTS AND METHODS: Thompson scores, aEEG, and outcome were compared in 122 infants with perinatal asphyxia and therapeutic hypothermia. Of these 122 infants, 41 died and 7 had an adverse neurodevelopmental outcome. A receiver operating characteristics (ROC) analysis was also performed. RESULTS: Thompson scores were higher in infants with more abnormal aEEG background patterns (ANOVA, p < 0.001). The ROC analysis demonstrated that a Thompson score of 11 or higher or an aEEG background pattern of continuous low voltage or worse was associated with an adverse outcome (AUC 0.84 for both). CONCLUSIONS: High Thompson scores and a suppressed aEEG background pattern are associated with an adverse outcome after perinatal asphyxia and therapeutic hypothermia. Further studies are needed to identify the best technique with which to select patients for therapeutic hypothermia