Complex roles of TGF-ß signaling pathways in lung development and bronchopulmonary dysplasia.

As survival of extremely preterm infants continues to improve, there is also an associated increase in bronchopulmonary dysplasia (BPD), one of the most significant complications of preterm birth. BPD development is multifactorial resulting from exposure to multiple antenatal and postnatal stressors. BPD has both short-term health implications and long-term sequelae including increased respiratory, cardiovascular, and neurological morbidity. Transforming growth factor ß (TGF-ß) is an important signaling pathway in lung development, organ injury, and fibrosis and is implicated in the development of BPD. This review provides a detailed account on the role of TGF-ß in antenatal and postnatal lung development, the effect of known risk factors for BPD on the TGF-ß signaling pathway, and how medications currently in use or under development, for the prevention or treatment of BPD, affect TGF-ß signaling.

Respiratory management and outcomes in high-risk preterm infants with development of a population outcome dashboard.

INTRODUCTION: Bronchopulmonary dysplasia (BPD) is associated with adverse long-term respiratory and neurodevelopmental outcomes. No recent studies examined the changing respiratory management and outcomes, particularly severe BPD, across a whole population. PURPOSE: Evaluate the temporal trends in the respiratory management and outcomes of preterm infants born below 32 weeks gestational age and develop an individualised dashboard of the incidence of neonatal outcome. METHODS: Using the National Neonatal Research Database, we determined changes in respiratory management, BPD rates, postdischarge respiratory support and mortality in 83 463 preterm infants in England and Wales from 2010 to 2020. RESULTS: Between 2010 and 2020, antenatal corticosteroids use increased (88%-93%, p<0.0001) and neonatal surfactant use decreased (65%-60%, p<0.0001). Postnatal corticosteroid use increased, especially dexamethasone (4%-6%, p<0.0001). More recently, hydrocortisone and budesonide use increased from 2% in 2017 to 4% and 3%, respectively, in 2020 (p<0.0001). Over the study period, mortality decreased (10.1%-8.5%), with increases in BPD (28%-33%), severe BPD (12%-17%), composite BPD/death (35%-39%) and composite severe BPD/death (21%-24%) (all p<0.0001). Overall, 11 684 infants required postdischarge respiratory support, increasing from 13% to 17% (p<0.0001), with 1843 infants requiring respiratory pressure support at discharge. A population dashboard (https://premoutcome.github.io/) depicting the incidence of mortality and respiratory outcomes, based on gestation, sex and birthweight centile, was developed. CONCLUSION: More preterm infants are surviving with worse respiratory outcomes, particularly severe BPD requiring postdischarge respiratory support. Ultimately, these survivors will develop chronic respiratory diseases requiring greater healthcare resources.

Impact of postnatal dexamethasone timing on preterm mortality and bronchopulmonary dysplasia: a propensity score analysis.

BACKGROUND: Postnatal dexamethasone (PND) is used in high-risk preterm infants after the first week of life to facilitate extubation and prevent bronchopulmonary dysplasia (BPD) but the optimal treatment timing remains unclear. Our objective was to explore the association between the timing of PND commencement and mortality and respiratory outcomes. METHODS: This was a retrospective National Neonatal Research Database study of 84 440 premature infants born <32 weeks gestational age from 2010 to 2020 in England and Wales. Propensity score weighting analysis was used to explore the impact of PND commenced at three time-points (2-3 weeks (PND2/3), 4-5 weeks (PND4/5) and after 5 weeks (PND6+) chronological age) on the primary composite outcome of death before neonatal discharge and/or severe BPD (defined as respiratory pressure support at 36 weeks) alongside other secondary respiratory outcomes. RESULTS: 3469 infants received PND. Compared with PND2/3, infants receiving PND6+ were more likely to die and/or develop severe BPD (OR 1.68, 95% CI 1.28-2.21), extubate at later postmenstrual age (mean difference 3.1 weeks, 95% CI 2.9-3.4 weeks), potentially require respiratory support at discharge (OR 1.34, 95% CI 1.06-1.70) but had lower mortality before discharge (OR 0.38, 95% CI 0.29-0.51). PND4/5 was not associated with severe BPD or discharge respiratory support. CONCLUSIONS: PND treatment after 5 weeks of age was associated with worse respiratory outcomes although residual bias cannot be excluded. A definitive clinical trial to determine the optimal PND treatment window, based on early objective measures to identify high-risk infants, is needed.

Bronchopulmonary dysplasia prediction models: a systematic review and meta-analysis with validation.

Prediction models could identify infants at the greatest risk of bronchopulmonary dysplasia (BPD) and allow targeted preventative strategies. We performed a systematic review and meta-analysis with external validation of identified models. Studies using predictors available before day 14 of life to predict BPD in very preterm infants were included. Two reviewers assessed 7628 studies for eligibility. Meta-analysis of externally validated models was followed by validation using 62,864 very preterm infants in England and Wales. A total of 64 studies using 53 prediction models were included totalling 274,407 infants (range 32-156,587/study). In all, 35 (55%) studies predated 2010; 39 (61%) were single-centre studies. A total of 97% of studies had a high risk of bias, especially in the analysis domain. Following meta-analysis of 22 BPD and 11 BPD/death composite externally validated models, Laughon's day one model was the most promising in predicting BPD and death (C-statistic 0.76 (95% CI 0.70-0.81) and good calibration). Six models were externally validated in our cohort with C-statistics between 0.70 and 0.90 but with poor calibration. Few BPD prediction models were developed with contemporary populations, underwent external validation, or had calibration and impact analyses. Contemporary, validated, and dynamic prediction models are needed for targeted preventative strategies. IMPACT: This review aims to provide a comprehensive assessment of all BPD prediction models developed to address the uncertainty of which model is sufficiently valid and generalisable for use in clinical practice and research. Published BPD prediction models are mostly outdated, single centre and lack external validation. Laughon's 2011 model is the most promising but more robust models, using contemporary data with external validation are needed to support better treatments.

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring: Part 1 wearable technology.

With the development of Artificial Intelligence techniques, smart health monitoring is becoming more popular. In this study, we investigate the trend of wearable sensors being adopted and developed in neonatal cardiorespiratory monitoring. We performed a search of papers published from the year 2000 onwards. We then reviewed the advances in sensor technologies and wearable modalities for this application. Common wearable modalities included clothing (39%); chest/abdominal belts (25%); and adhesive patches (15%). Popular singular physiological information from sensors included electrocardiogram (15%), breathing (24%), oxygen saturation and photoplethysmography (13%). Many studies (46%) incorporated a combination of these signals. There has been extensive research in neonatal cardiorespiratory monitoring using both single and multi-parameter systems. Poor data quality is a common issue and further research into combining multi-sensor information to alleviate this should be investigated. IMPACT STATEMENT: State-of-the-art review of sensor technology for wearable neonatal cardiorespiratory monitoring. Review of the designs for wearable neonatal cardiorespiratory monitoring. The use of multi-sensor information to improve physiological data quality has been limited in past research. Several sensor technologies have been implemented and tested on adults that have yet to be explored in the newborn population.

Artificial intelligence-driven wearable technologies for neonatal cardiorespiratory monitoring. Part 2: artificial intelligence.

BACKGROUND: With the development of Artificial Intelligence (AI) techniques, smart health monitoring, particularly neonatal cardiorespiratory monitoring with wearable devices, is becoming more popular. To this end, it is crucial to investigate the trend of AI and wearable sensors being developed in this domain. METHODS: We performed a review of papers published in IEEE Xplore, Scopus, and PubMed from the year 2000 onwards, to understand the use of AI for neonatal cardiorespiratory monitoring with wearable technologies. We reviewed the advances in AI development for this application and potential future directions. For this review, we assimilated machine learning (ML) algorithms developed for neonatal cardiorespiratory monitoring, designed a taxonomy, and categorised the methods based on their learning capabilities and performance. RESULTS: For AI related to wearable technologies for neonatal cardio-respiratory monitoring, 63% of studies utilised traditional ML techniques and 35% utilised deep learning techniques, including 6% that applied transfer learning on pre-trained models. CONCLUSIONS: A detailed review of AI methods for neonatal cardiorespiratory wearable sensors is presented along with their advantages and disadvantages. Hierarchical models and suggestions for future developments are highlighted to translate these AI technologies into patient benefit. IMPACT: State-of-the-art review in artificial intelligence used for wearable neonatal cardiorespiratory monitoring. Taxonomy design for artificial intelligence methods. Comparative study of AI methods based on their advantages and disadvantages.

Neonatal outcomes of maternal SARS-CoV-2 infection in the UK: a prospective cohort study using active surveillance.

BACKGROUND: Newborns may be affected by maternal SARS-CoV-2 infection during pregnancy. We aimed to describe the epidemiology, clinical course and short-term outcomes of babies admitted to a neonatal unit (NNU) following birth to a mother with confirmed SARS-CoV-2 infection within 7 days of birth. METHODS: This is a UK prospective cohort study; all NHS NNUs, 1 March 2020 to 31 August 2020. Cases were identified via British Paediatric Surveillance Unit with linkage to national obstetric surveillance data. Reporting clinicians completed data forms. Population data were extracted from the National Neonatal Research Database. RESULTS: A total of 111 NNU admissions (1.98 per 1000 of all NNU admissions) involved 2456 days of neonatal care (median 13 [IQR 5, 34] care days per admission). A total of 74 (67%) babies were preterm. In all, 76 (68%) received respiratory support; 30 were mechanically ventilated. Four term babies received therapeutic hypothermia for hypoxic ischaemic encephalopathy. Twenty-eight mothers received intensive care, with four dying of COVID-19. Eleven (10%) babies were SARS-CoV-2 positive. A total of 105 (95%) babies were discharged home; none of the three deaths before discharge was attributed to SARS-CoV-2. CONCLUSION: Babies born to mothers with SARS-CoV-2 infection around the time of birth accounted for a low proportion of total NNU admissions over the first 6 months of the UK pandemic. Neonatal SARS-CoV-2 was uncommon. STUDY REGISTRATION: ISRCTN60033461; protocol available at http://www.npeu.ox.ac.uk/pru-mnhc/research-themes/theme-4/covid-19 . IMPACT: Neonatal unit admissions of babies born to mothers with SARS-CoV-2 infection comprised only a small proportion of total neonatal admissions in the first 6 months of the pandemic. A high proportion of babies requiring neonatal admission who were born to mothers with confirmed SARS-CoV-2 infection were preterm and had neonatal SARS-CoV-2 infection and/or other conditions associated with long-term sequelae. Adverse neonatal conditions were more common in babies whose SARS-CoV-2-positive mothers required intensive care compared to those whose SARS-CoV-2-positive mothers who did not.

The Classification of Movement in Infants for the Autonomous Monitoring of Neurological Development.

Neurodevelopmental delay following extremely preterm birth or birth asphyxia is common but diagnosis is often delayed as early milder signs are not recognised by parents or clinicians. Early interventions have been shown to improve outcomes. Automation of diagnosis and monitoring of neurological disorders using non-invasive, cost effective methods within a patient's home could improve accessibility to testing. Furthermore, said testing could be conducted over a longer period, enabling greater confidence in diagnoses, due to increased data availability. This work proposes a new method to assess the movements in children. Twelve parent and infant participants were recruited (children aged between 3 and 12 months). Approximately 25 min 2D video recordings of the infants organically playing with toys were captured. A combination of deep learning and 2D pose estimation algorithms were used to classify the movements in relation to the children's dexterity and position when interacting with a toy. The results demonstrate the possibility of capturing and classifying children's complexity of movements when interacting with toys as well as their posture. Such classifications and the movement features could assist practitioners to accurately diagnose impaired or delayed movement development in a timely fashion as well as facilitating treatment monitoring.

The newborn delivery room of tomorrow: emerging and future technologies.

Advances in neonatal care have resulted in improved outcomes for high-risk newborns with technologies playing a significant part although many were developed for the neonatal intensive care unit. The care provided in the delivery room (DR) during the first few minutes of life can impact short- and long-term neonatal outcomes. Increasingly, technologies have a critical role to play in the DR particularly with monitoring and information provision. However, the DR is a unique environment and has major challenges around the period of foetal to neonatal transition that need to be overcome when developing new technologies. This review focuses on current DR technologies as well as those just emerging and further over the horizon. We identify what key opinion leaders in DR care think of current technologies, what the important DR measures are to them, and which technologies might be useful in the future. We link these with key technologies including respiratory function monitors, electoral impedance tomography, videolaryngoscopy, augmented reality, video recording, eye tracking, artificial intelligence, and contactless monitoring. Encouraging funders and industry to address the unique technological challenges of newborn care in the DR will allow the continued improvement of outcomes of high-risk infants from the moment of birth. IMPACT: Technological advances for newborn delivery room care require consideration of the unique environment, the variable patient characteristics, and disease states, as well as human factor challenges. Neonatology as a speciality has embraced technology, allowing its rapid progression and improved outcomes for infants, although innovation in the delivery room often lags behind that in the intensive care unit. Investing in new and emerging technologies can support healthcare providers when optimising care and could improve training, safety, and neonatal outcomes.

Accurate neonatal heart rate monitoring using a new wireless, cap mounted device.

AIM: A device for newborn heart rate (HR) monitoring at birth that is compatible with delayed cord clamping and minimises hypothermia risk could have advantages over current approaches. We evaluated a wireless, cap mounted device (fhPPG) for monitoring neonatal HR. METHODS: A total of 52 infants on the neonatal intensive care unit (NICU) and immediately following birth by elective caesarean section (ECS) were recruited. HR was monitored by electrocardiogram (ECG), pulse oximetry (PO) and the fhPPG device. Success rate, accuracy and time to output HR were compared with ECG as the gold standard. Standardised simulated data assessed the fhPPG algorithm accuracy. RESULTS: Compared to ECG HR, the median bias (and 95% limits of agreement) for the NICU was fhPPG -0.6 (-5.6, 4.9) vs PO -0.3 (-6.3, 6.2) bpm, and ECS phase fhPPG -0.5 (-8.7, 7.7) vs PO -0.1 (-7.6, 7.1) bpm. In both settings, fhPPG and PO correlated with paired ECG HRs (both R2  = 0.89). The fhPPG HR algorithm during simulations demonstrated a near-linear correlation (n = 1266, R2  = 0.99). CONCLUSION: Monitoring infants in the NICU and following ECS using a wireless, cap mounted device provides accurate HR measurements. This alternative approach could confer advantages compared with current methods of HR assessment and warrants further evaluation at birth.