Continuous vital sign analysis for predicting and preventing neonatal diseases in the twenty-first century: big data to the forefront

Beyond the delivery room: Resuscitation in the neonatal intensive care unit

Cardiopulmonary resuscitation is a critical component of neonatal care. While the basic principles of resuscitation are consistent across different settings, the specific challenges and resources available in the delivery room and the Neonatal Intensive Care Unit (NICU) vary significantly. Understanding the differences between these settings is essential for optimizing resuscitation outcomes. This article explores four key areas of difference-environment and equipment, team composition and roles, care protocols and practices, and patient population and condition-and how they impact neonatal resuscitation efforts. By examining these differences, healthcare neonatal care teams can better prepare for the specific resuscitation needs in each setting, ultimately improving neonatal survival and long-term health outcomes.

Understanding necrotizing enterocolitis endotypes and acquired intestinal injury phenotypes from a historical and artificial intelligence perspective

Necrotizing enterocolitis (NEC) remains a devastating disease in preterm and term neonates. Despite significant progress made in understanding NEC pathogenesis over the last 50 years, the inability of current definitions to discriminate the various pathophysiological processes underlying NEC has led to an umbrella term that limits clinical and research progress. In this mini review, we provide a historical perspective on how NEC definitions and pathogenesis have evolved to our current understanding of NEC endotypes. We also discuss how artificial intelligence-based approaches are influencing our knowledge of risk-factors, classification and prognosis of NEC and other neonatal intestinal injury phenotypes.

Cardiorespiratory interactions during the transitional period in extremely preterm infants: a narrative review

We aimed to review the physiology and evidence behind cardiorespiratory interactions during the transitional circulation of extremely preterm infants with fragile physiology and to propose a framework for future research. Cord clamping strategies have a great impact on initial haemodynamic changes, and appropriate transition can be facilitated by establishing spontaneous ventilation before cord clamping. Mechanical ventilation modifies preterm transitional haemodynamics, with positive pressure ventilation affecting the right and left heart loading conditions. Pulmonary vascular resistances can be minimized by ventilating with optimal lung volumes at functional residual capacity, and other pulmonary vasodilator treatments such as inhaled nitric oxide can be used to improve ventilation/perfusion mismatch. Different cardiovascular drugs can be used to provide support during transition in this population, and it is important to understand both their cardiovascular and respiratory effects, in order to provide adequate support to vulnerable preterm infants and improve outcomes. Current available non-invasive bedside tools, such as near-infrared spectroscopy, targeted neonatal echocardiography, or lung ultrasound offer the opportunity to precisely monitor cardiorespiratory interactions in preterm infants. More research is needed in this field using precision medicine to strengthen the benefits and avoid the harms associated to early neonatal interventions. IMPACT: In extremely preterm infants, haemodynamic and respiratory transitions are deeply interconnected, and their changes have a key impact in the establishment of lung aireation and postnatal circulation. We describe how mechanical ventilation modifies heart loading conditions and pulmonary vascular resistances in preterm patients, and how hemodynamic interventions such as cord clamping strategies or cardiovascular drugs affect the infant respiratory status. Current available non-invasive bedside tools can help monitor cardiorespiratory interactions in preterm infants. We highlight the areas of research in which precision medicine can help strengthen the benefits and avoid the harms associated to early neonatal interventions.

Continuous oxygen saturation and risk of retinopathy of prematurity in a Japanese cohort

BACKGROUND/AIMS

We assessed the associations between retinopathy of prematurity (ROP) and continuous measurements of oxygen saturation (SpO2), and developed a risk prediction model for severe ROP using birth data and SpO2 data.

METHODS

This retrospective study included infants who were born before 30 weeks of gestation between August 2009 and January 2019 and who were screened for ROP at a single hospital in Japan. We extracted data on birth weight (BW), birth length, gestational age (GA) and minute-by-minute SpO2 during the first 20 days from the medical records. We defined four SpO2 variables using sequential measurements. Multivariate logistic regression was used to develop a model that combined birth data and SpO2 data to predict treatment-requiring ROP (TR-ROP). The model's performance was evaluated using the area under the receiver operating characteristic curve (AUC).

RESULTS

Among 350 infants, 83 (23.7%) required ROP treatment. The SpO2 variables in infants with TR-ROP differed significantly from those with non-TR-ROP. The average SpO2 and high SpO2 showed strong associations with GA (r=0.73 and r=0.70, respectively). The model incorporating birth data and the four SpO2 variables demonstrated good discriminative ability (AUC=0.83), but it did not outperform the model incorporating BW and GA (AUC=0.82).

CONCLUSION

Data obtained by continuous SpO2 monitoring demonstrated valuable associations with severe ROP, as well as with GA. Differences in the distribution of average SpO2 and high SpO2 between infants with TR-ROP and non-TR-ROP could be used to establish efficient cut-off values for risk determination.

Exploring Computational Techniques in Preprocessing Neonatal Physiological Signals for Detecting Adverse Outcomes: Scoping Review

BACKGROUND

Computational signal preprocessing is a prerequisite for developing data-driven predictive models for clinical decision support. Thus, identifying the best practices that adhere to clinical principles is critical to ensure transparency and reproducibility to drive clinical adoption. It further fosters reproducible, ethical, and reliable conduct of studies. This procedure is also crucial for setting up a software quality management system to ensure regulatory compliance in developing software as a medical device aimed at early preclinical detection of clinical deterioration.

OBJECTIVE

This scoping review focuses on the neonatal intensive care unit setting and summarizes the state-of-the-art computational methods used for preprocessing neonatal clinical physiological signals; these signals are used for the development of machine learning models to predict the risk of adverse outcomes.

METHODS

Five databases (PubMed, Web of Science, Scopus, IEEE, and ACM Digital Library) were searched using a combination of keywords and MeSH (Medical Subject Headings) terms. A total of 3585 papers from 2013 to January 2023 were identified based on the defined search terms and inclusion criteria. After removing duplicates, 2994 (83.51%) papers were screened by title and abstract, and 81 (0.03%) were selected for full-text review. Of these, 52 (64%) were eligible for inclusion in the detailed analysis.

RESULTS

Of the 52 articles reviewed, 24 (46%) studies focused on diagnostic models, while the remainder (n=28, 54%) focused on prognostic models. The analysis conducted in these studies involved various physiological signals, with electrocardiograms being the most prevalent. Different programming languages were used, with MATLAB and Python being notable. The monitoring and capturing of physiological data used diverse systems, impacting data quality and introducing study heterogeneity. Outcomes of interest included sepsis, apnea, bradycardia, mortality, necrotizing enterocolitis, and hypoxic-ischemic encephalopathy, with some studies analyzing combinations of adverse outcomes. We found a partial or complete lack of transparency in reporting the setting and the methods used for signal preprocessing. This includes reporting methods to handle missing data, segment size for considered analysis, and details regarding the modification of the state-of-the-art methods for physiological signal processing to align with the clinical principles for neonates. Only 7 (13%) of the 52 reviewed studies reported all the recommended preprocessing steps, which could have impacts on the downstream analysis.

CONCLUSIONS

The review found heterogeneity in the techniques used and inconsistent reporting of parameters and procedures used for preprocessing neonatal physiological signals, which is necessary to confirm adherence to clinical and software quality management system practices, usefulness, and choice of best practices. Enhancing transparency in reporting and standardizing procedures will boost study interpretation and reproducibility and expedite clinical adoption, instilling confidence in the research findings and streamlining the translation of research outcomes into clinical practice, ultimately contributing to the advancement of neonatal care and patient outcomes.

Artificial intelligence and machine learning: Definition of terms and current concepts in critical care research

With increasing computing power, artificial intelligence (AI) and machine learning (ML) have prospered, which facilitate the analysis of large datasets, especially those found in critical care. It is important to define these terminologies, to inform a standardized approach to critical care research. This manuscript hopes to clarify these terms with examples from medical literature. Three major components that are required for a successful ML implementation: (i) reliable dataset, (ii) ML algorithm, and (iii) unbiased model evaluation, are discussed. A reliable dataset can be structured or unstructured with limited noise, outliers, and missing values. ML, a subset of AI, is typically focused on supervised or unsupervised learning tasks in which the output is based on inputs and derived from iterative pattern recognition algorithms, while AI is the overall ability of a machine to "think" or mimic human behavior; and to analyze data free from human influence. Even with successful implementation, advanced AI and ML algorithms have faced challenges in adoption into practice, mainly due to their lack of interpretability, which hinders trust, buy-in, and engagement from clinicians. Consequently, traditional algorithms, such as linear and logistic regression, that may have reduced predictive power but are highly interpretable, continue to be widely used.

Skin-interfacing wearable biosensors for smart health monitoring of infants and neonates

Health monitoring of infant patients in intensive care can be especially strenuous for both the patient and their caregiver, as testing setups involve a tangle of electrodes, probes, and catheters that keep the patient bedridden. This has typically involved expensive and imposing machines, to track physiological metrics such as heart rate, respiration rate, temperature, blood oxygen saturation, blood pressure, and ion concentrations. However, in the past couple of decades, research advancements have propelled a world of soft, wearable, and non-invasive systems to supersede current practices. This paper summarizes the latest advancements in neonatal wearable systems and the different approaches to each branch of physiological monitoring, with an emphasis on smart skin-interfaced wearables. Weaknesses and shortfalls are also addressed, with some guidelines provided to help drive the further research needed.

Heart Rate and Pulse Oximetry Dynamics in the First Week after Birth in Neonatal Intensive Care Unit Patients and the Risk of Cerebral Palsy

OBJECTIVE

Infants in the neonatal intensive care unit (NICU) are at high risk of adverse neuromotor outcomes. Atypical patterns of heart rate (HR) and pulse oximetry (SpO2) may serve as biomarkers for risk assessment for cerebral palsy (CP). The purpose of this study was to determine whether atypical HR and SpO2 patterns in NICU patients add to clinical variables predicting later diagnosis of CP.

STUDY DESIGN

This was a retrospective study including patients admitted to a level IV NICU from 2009 to 2017 with archived cardiorespiratory data in the first 7 days from birth to follow-up at >2 years of age. The mean, standard deviation (SD), skewness, kurtosis and cross-correlation of HR and SpO2 were calculated. Three predictive models were developed using least absolute shrinkage and selection operator regression (clinical, cardiorespiratory and combined model), and their performance for predicting CP was evaluated.

RESULTS

Seventy infants with CP and 1,733 controls met inclusion criteria for a 3.8% population prevalence. Area under the receiver operating characteristic curve for CP prediction was 0.7524 for the clinical model, 0.7419 for the vital sign model, and 0.7725 for the combined model. Variables included in the combined model were lower maternal age, outborn delivery, lower 5-minute Apgar's score, lower SD of HR, and more negative skewness of HR.

CONCLUSION

In this study including NICU patients of all gestational ages, HR but not SpO2 patterns added to clinical variables to predict the eventual diagnosis of CP. Identification of risk of CP within the first few days of life could result in improved therapy resource allocation and risk stratification in clinical trials of new therapeutics.

KEY POINTS

· SD and skewness of HR have some added predictive value of later diagnosis of CP.. · SpO2 measures do not add to CP prediction.. · Combining clinical variables with early HR measures may improve the prediction of later CP..

Clinical Decision Support for Improved Neonatal Care: The Development of a Machine Learning Model for the Prediction of Late-onset Sepsis and Necrotizing Enterocolitis

OBJECTIVE

To develop an artificial intelligence-based software system for predicting late-onset sepsis (LOS) and necrotizing enterocolitis (NEC) in infants admitted to the neonatal intensive care unit (NICU).

STUDY DESIGN

Single-center, retrospective cohort study, conducted in the NICU of the Antwerp University Hospital. Continuous monitoring data of 865 preterm infants born at <32 weeks gestational age, admitted to the NICU in the first week of life, were used to train an XGBoost machine learning (ML) algorithm for LOS and NEC prediction in a cross-validated setup. Afterward, the model's performance was assessed on an independent test set of 148 patients (internal validation).

RESULTS

The ML model delivered hourly risk predictions with an overall sensitivity of 69% (142/206) for all LOS/NEC episodes and 81% (67/83) for severe LOS/NEC episodes. The model showed a median time gain of ≤10 hours (IQR, 3.1-21.0 hours), compared with historical clinical diagnosis. On the complete retrospective dataset, the ML model made 721 069 predictions, of which 9805 (1.3%) depicted a LOS/NEC probability of ≥0.15, resulting in a total alarm rate of <1 patient alarm-day per week. The model reached a similar performance on the internal validation set.

CONCLUSIONS

Artificial intelligence technology can assist clinicians in the early detection of LOS and NEC in the NICU, which potentially can result in clinical and socioeconomic benefits. Additional studies are required to quantify further the effect of combining artificial and human intelligence on patient outcomes in the NICU.

Total liquid ventilation in an ovine model of extreme prematurity: a randomized study

BACKGROUND

This study aimed at comparing cardiorespiratory stability during total liquid ventilation (TLV)-prior to lung aeration-with conventional mechanical ventilation (CMV) in extremely preterm lambs during the first 6 h of life.

METHODS

23 lambs (11 females) were born by c-section at 118-120 days of gestational age (term = 147 days) to receive 6 h of TLV or CMV from birth. Lung samples were collected for RNA and histology analyses.

RESULTS

The lambs under TLV had higher and more stable arterial oxygen saturation (p = 0.001) and cerebral tissue oxygenation (p = 0.02) than the lambs in the CMV group in the first 10 min of transition to extrauterine life. Although histological assessment of the lungs was similar between the groups, a significant upregulation of IL-1a, IL-6 and IL-8 RNA in the lungs was observed after TLV.

CONCLUSIONS

Total liquid ventilation allowed for remarkably stable transition to extrauterine life in an extremely preterm lamb model. Refinement of our TLV prototype and ventilation algorithms is underway to address specific challenges in this population, such as minimizing tracheal deformation during the active expiration.

IMPACT

Total liquid ventilation allows for remarkably stable transition to extrauterine life in an extremely preterm lamb model. Total liquid ventilation is systematically achievable over the first 6 h of life in the extremely premature lamb model. This study provides additional incentive to pursue further investigation of total liquid ventilation as a transition tool for the most extreme preterm neonates.

The Effects of a New Wireless Non-Adhesive Cardiorespiratory Monitoring Device on the Skin Conditions of Preterm Infants

AIM

The aim of our study was to investigate skin conditions when wearing and removing a novel wireless non-adhesive cardiorespiratory monitoring device for neonates (Bambi-Belt) compared to standard adhesive electrodes.

STUDY DESIGN

This was a prospective study including preterm neonates requiring cardiorespiratory monitoring. Besides standard electrodes, the infants wore a Bambi Belt for 10 consecutive days. Their skin conditions were assessed using Trans Epidermal Water Loss (TEWL) and the Neonatal Skin Condition Score (NSCS) after daily belt and standard electrode removal. The ∆TEWL was calculated as the difference between the TEWL at the device's location (Bambi-Belt/standard electrode) and the adjacent control skin location, with a higher ∆TEWL indicating skin damage.

RESULTS

A total of 15 infants (gestational age (GA): 24.1-35.6 wk) were analyzed. The ΔTEWL significantly increased directly after electrode removal (10.95 ± 9.98 g/m2/h) compared to belt removal (5.18 ± 6.71 g/m2/h; F: 8.73, p = 0.004) and after the washout period (3.72 ± 5.46 g/m2/h vs. 1.86 ± 3.35 g/m2/h; F: 2.84, p = 0.09), although the latter did not reach statistical significance. The TEWL was not influenced by prolonged belt wearing. No significant differences in the NSCS score were found between the belt and electrode (OR: 0.69, 95% CI [0.17, 2.88], p = 0.6).

CONCLUSION

A new wireless non-adhesive device for neonatal cardiorespiratory monitoring was well tolerated in preterm infants and may be less damaging during prolonged wearing.

A clinical informatics approach to bronchopulmonary dysplasia: current barriers and future possibilities

Bronchopulmonary dysplasia (BPD) is a complex, multifactorial lung disease affecting preterm neonates that can result in long-term pulmonary and non-pulmonary complications. Current therapies mainly focus on symptom management after the development of BPD, indicating a need for innovative approaches to predict and identify neonates who would benefit most from targeted or earlier interventions. Clinical informatics, a subfield of biomedical informatics, is transforming healthcare by integrating computational methods with patient data to improve patient outcomes. The application of clinical informatics to develop and enhance clinical therapies for BPD presents opportunities by leveraging electronic health record data, applying machine learning algorithms, and implementing clinical decision support systems. This review highlights the current barriers and the future potential of clinical informatics in identifying clinically relevant BPD phenotypes and developing clinical decision support tools to improve the management of extremely preterm neonates developing or with established BPD. However, the full potential of clinical informatics in advancing our understanding of BPD with the goal of improving patient outcomes cannot be achieved unless we address current challenges such as data collection, storage, privacy, and inherent data bias.

Heart rate variability in school-age children born moderate-to-late preterm

BACKGROUND

Prematurity is associated with reduced cardiac autonomic function. This study aimed to investigate the heart rate variability (HRV) in school-age children born moderately to late preterm (MLPT).

METHODS

This cross-sectional study investigated school-age children, aged 5 to 10 years, born moderate-to-late preterm. Electrocardiograms recordings were performed during fifteen-minutes. Time and frequency domain parameters were calculated, corrected for heart rate and compared between the groups.

RESULTS

A total of 123 children were evaluated and 119 were included in this study. HRV measures, studied in the time and frequency domains, were similar in both groups. Corrected values of root mean square of successive differences between normal cycles (RMSSD), percentage of successive cycles with a duration difference >50 ms (pNN50%), and high frequency (HF), indices that predominantly represent the parasympathetic activity of the autonomic nervous system, were 1.6E-7 and 1.8E-7 (p=0.226); 1.6E-13 and 1.6E-13 (p=0.506); 6.9E-12 and 7.4E-12 (p=0.968) in the preterm and control groups, respectively.

CONCLUSION

This study did not find differences in heart rate variability between school-age children born MLPT and those born at term, suggesting that plasticity of cardiac autonomic modulation continues to occur in children up to school age or there is less impairment of the autonomic system in MLPT.

Utility of saturation trends to predict successful weaning of nasal CPAP in very preterm neonates - A prospective study

BACKGROUND

There is no objective criteria to wean CPAP in preterm neonates. We aimed to assess the accuracy of 'saturation trends' to predict successful CPAP discontinuation.

METHODS

We included very preterm neonates who required CPAP. Index tests were 'saturation trends'. Outcome was successful CPAP discontinuation, defined as baby stable in room air for 72 h.

RESULTS

We had 120 neonates with mean±SD gestation 28.6±1.8 weeks. 96 (80%) neonates had successful discontinuation and 24 (20%) failed. Neonates with successful discontinuation had significantly greater 'saturation trends' during 24 h before discontinuing CPAP compared to those who failed [64.3 (48.1-83.7) vs. 47.3 (23.0-65.0), p = 0.001]. Saturations > 95% while on CPAP with 21% FiO2 for > 60% time had 63% sensitivity and 70% specificity to predict successful CPAP discontinuation.

CONCLUSION

'Saturation trends' is a readily available objective parameter that can be used to guide weaning CPAP in preterm neonates.

Reviving Hope: A Comprehensive Review of Post-resuscitation Care in Pediatric ICUs After Cardiac Arrest

This comprehensive review thoroughly examines post-resuscitation care in pediatric ICUs (PICUs) following cardiac arrest. The analysis encompasses adherence to resuscitation guidelines, advances in therapeutic interventions, and the nuanced management of neurological, cardiovascular, and respiratory considerations during the immediate post-resuscitation phase. Delving into the complexities of long-term outcomes, cognitive and developmental considerations, and rehabilitation strategies, the review emphasizes the importance of family-centered care for pediatric survivors. A call to action is presented, urging continuous education, research initiatives, and quality improvement efforts alongside strengthened multidisciplinary collaboration and advocacy for public awareness. Through implementing these principles, healthcare providers and systems can collectively contribute to ongoing advancements in pediatric post-resuscitation care, ultimately improving outcomes and fostering a culture of excellence in pediatric critical care.

The effect of acute respiratory events and respiratory stimulants on EEG-recorded brain activity in neonates: A systematic review

Objective

We conducted a systematic review to investigate electroencephalography (EEG) changes during periods of acute respiratory events such as apnoea and the effect of respiratory stimulants on EEG features in infants.

Methods

Studies examining respiration and EEG-recorded brain activity in human neonates between 28 and 42 weeks postmenstrual age were included. Two reviewers independently screened all records and included studies were assessed using the Joanna Briggs Institute Critical Appraisal Tool. The protocol was registered in PROSPERO (CRD42022339873).

Results

We identified 14 studies with a total of 534 infants. Nine articles assessed EEG changes in relation to apnoea, one assessed hiccups, and four investigated the effect of respiratory stimulants. The relationship between neonatal apnoea and EEG changes was inconsistent; EEG suppression and decreased amplitude and frequency were observed during some, but not all, apnoeas. Respiratory stimulants increased EEG continuity compared with before use.

Conclusions

Current studies in this area are constrained by small sample sizes. Diverse exposure definitions and outcome measures impact inference.

Significance

This review highlights the need for further work; understanding the relationship between respiration and the developing brain is key to mitigating the long-term effects of apnoea.

Heart rate patterns predicting cerebral palsy in preterm infants

BACKGROUND

Heart rate (HR) patterns can inform on central nervous system dysfunction. We previously used highly comparative time series analysis (HCTSA) to identify HR patterns predicting mortality among patients in the neonatal intensive care unit (NICU) and now use this methodology to discover patterns predicting cerebral palsy (CP) in preterm infants.

METHOD

We studied NICU patients <37 weeks' gestation with archived every-2-s HR data throughout the NICU stay and with or without later diagnosis of CP (n = 57 CP and 1119 no CP). We performed HCTSA of >2000 HR metrics and identified 24 metrics analyzed on HR data from two 7-day periods: week 1 and 37 weeks' postmenstrual age (week 1, week 37). Multivariate modeling was used to optimize a parsimonious prediction model.

RESULTS

Week 1 HR metrics with maximum AUC for CP prediction reflected low variability, including "RobustSD" (AUC 0.826; 0.772-0.870). At week 37, high values of a novel HR metric, "LongSD3," the cubed value of the difference in HR values 100 s apart, were added to week 1 HR metrics for CP prediction. A combined birthweight + early and late HR model had AUC 0.853 (0.805-0.892).

CONCLUSIONS

Using HCTSA, we discovered novel HR metrics and created a parsimonious model for CP prediction in preterm NICU patients.

IMPACT

We discovered new heart rate characteristics predicting CP in preterm infants. Using every-2-s HR from two 7-day periods and highly comparative time series analysis, we found a measure of low variability HR week 1 after birth and a pattern of recurrent acceleration in HR at term corrected age that predicted CP. Combined clinical and early and late HR features had AUC 0.853 for CP prediction.

Development of an Interactive Dashboard to Analyse Physiological Signals in the Neonatal Intensive Care Unit

Premature babies and those born with a medical condition are cared for within the neonatal intensive care unit (NICU) in hospitals. Monitoring physiological signals and subsequent analysis and interpretation can reveal acute and chronic conditions for these neonates. Several advanced algorithms using physiological signals have been built into existing monitoring systems to allow clinicians to analyse signals in real time and anticipate patient deterioration. However, limited enhancements have been made to interactively visualise and adapt them to neonatal monitoring systems. To bridge this gap, we describe the development of a user-friendly and interactive dashboard for neonatal vital signs analysis written in the Python programming language where the analysis can be performed without prior computing knowledge. To ensure practicality, the dashboard was designed in consultation with a neonatologist to visualise electrocardiogram, heart rate, respiratory rate and oxygen saturation data in a time-series format. The resulting dashboard included interactive visualisations, advanced electrocardiogram analysis and statistical analysis which can be used to extract important information on patients' conditions.Clinical Relevance- This will support the care of preterm infants by allowing clinicians to visualise and interpret physiological data in greater granularity, aiding in patient monitoring and detection of adverse conditions. The detection of adverse conditions could allow timely and potentially life-saving interventions for conditions such as sepsis and brain injury.

The use of wireless sensors in the neonatal intensive care unit: a study protocol

Background

Continuous monitoring of vital signs and other biological signals in the Neonatal Intensive Care Unit (NICU) requires sensors connected to the bedside monitors by wires and cables. This monitoring system presents challenges such as risks for skin damage or infection, possibility of tangling around the patient body, or damage of the wires, which may complicate routine care. Furthermore, the presence of cables and wires can act as a barrier for parent-infant interactions and skin to skin contact. This study will investigate the use of a new wireless sensor for routine vital monitoring in the NICU.

Methods

Forty-eight neonates will be recruited from the Montreal Children's Hospital NICU. The primary outcome is to evaluate the feasibility, safety, and accuracy of a wireless monitoring technology called ANNE® One (Sibel Health, Niles, MI, USA). The study will be conducted in 2 phases where physiological signals will be acquired from the standard monitoring system and the new wireless monitoring system simultaneously. In phase 1, participants will be monitored for 8 h, on four consecutive days, and the following signals will be obtained: heart rate, respiratory rate, oxygen saturation and skin temperature. In phase 2, the same signals will be recorded, but for a period of 96 consecutive hours. Safety and feasibility of the wireless devices will be assessed. Analyses of device accuracy and performance will be accomplished offline by the biomedical engineering team.

Conclusion

This study will evaluate feasibility, safety, and accuracy of a new wireless monitoring technology in neonates treated in the NICU.

Continuous Data-Driven Monitoring in Critical Congenital Heart Disease: Clinical Deterioration Model Development

BACKGROUND

Critical congenital heart disease (cCHD)-requiring cardiac intervention in the first year of life for survival-occurs globally in 2-3 of every 1000 live births. In the critical perioperative period, intensive multimodal monitoring at a pediatric intensive care unit (PICU) is warranted, as their organs-especially the brain-may be severely injured due to hemodynamic and respiratory events. These 24/7 clinical data streams yield large quantities of high-frequency data, which are challenging in terms of interpretation due to the varying and dynamic physiology innate to cCHD. Through advanced data science algorithms, these dynamic data can be condensed into comprehensible information, reducing the cognitive load on the medical team and providing data-driven monitoring support through automated detection of clinical deterioration, which may facilitate timely intervention.

OBJECTIVE

This study aimed to develop a clinical deterioration detection algorithm for PICU patients with cCHD.

METHODS

Retrospectively, synchronous per-second data of cerebral regional oxygen saturation (rSO₂) and 4 vital parameters (respiratory rate, heart rate, oxygen saturation, and invasive mean blood pressure) in neonates with cCHD admitted to the University Medical Center Utrecht, the Netherlands, between 2002 and 2018 were extracted. Patients were stratified based on mean oxygen saturation during admission to account for physiological differences between acyanotic and cyanotic cCHD. Each subset was used to train our algorithm in classifying data as either stable, unstable, or sensor dysfunction. The algorithm was designed to detect combinations of parameters abnormal to the stratified subpopulation and significant deviations from the patient's unique baseline, which were further analyzed to distinguish clinical improvement from deterioration. Novel data were used for testing, visualized in detail, and internally validated by pediatric intensivists.

RESULTS

A retrospective query yielded 4600 hours and 209 hours of per-second data in 78 and 10 neonates for, respectively, training and testing purposes. During testing, stable episodes occurred 153 times, of which 134 (88%) were correctly detected. Unstable episodes were correctly noted in 46 of 57 (81%) observed episodes. Twelve expert-confirmed unstable episodes were missed in testing. Time-percentual accuracy was 93% and 77% for, respectively, stable and unstable episodes. A total of 138 sensorial dysfunctions were detected, of which 130 (94%) were correct.

CONCLUSIONS

In this proof-of-concept study, a clinical deterioration detection algorithm was developed and retrospectively evaluated to classify clinical stability and instability, achieving reasonable performance considering the heterogeneous population of neonates with cCHD. Combined analysis of baseline (ie, patient-specific) deviations and simultaneous parameter-shifting (ie, population-specific) proofs would be promising with respect to enhancing applicability to heterogeneous critically ill pediatric populations. After prospective validation, the current-and comparable-models may, in the future, be used in the automated detection of clinical deterioration and eventually provide data-driven monitoring support to the medical team, allowing for timely intervention.

The effects of kangaroo mother care on physiological parameters of premature neonates in neonatal intensive care unit: A systematic review

PURPOSE

The effects of Kangaroo mother care (KMC) on physiological parameters in preterm infants have been reported in the literature by experimental and quasi-experimental studies, and varying findings have been presented. The present study was conducted to determine the effects of KMC on the physiological parameters of premature newborns in the Neonatal Intensive Care Unit.

DESIGN AND METHOD

The review was conducted according to the specified keywords by scanning the EBSCO-host, Cochrane Library, Medline, PubMed, ScienceDirect, Web of Science, and TR index databases using the keywords "kangaroo care AND preterm AND vital signs." The pool mean differences (MDs) were calculated, adopting a 95% confidence interval (CIs) using the Stata 16 software for the meta-analysis [PROSPERO: CRD42021283475].

RESULTS

Eleven studies for systematic review and nine studies for meta-analysis, including 634 participants, were found eligible for inclusion. It was determined that the "temperature" (z = 3.21; p = 0.000) and "oxygen saturation" (z = 2.49; p = 0.000) values created a positive effect in general in the kangaroo care group; however, there was no sufficient evidence to state that it affected the "heart rate" (z = -0.60; p = 0.55) and "respiratory rate" (z = -1.45; p = 0.15) values. In the present study, the duration of KMC application had statistically different effects on temperature and oxygen saturation (SpO₂) (p < 0.05). One-hour or shorter applications of KMC had a higher effect on the temperature and oxygen saturation values (1.83; 1.62, respectively).

CONCLUSION

Our results provided references for clinical implications, and the "temperature" and "oxygen saturation (SpO₂)" values created a positive effect in general in the KMC group. However, there was no sufficient evidence to state that it affected the "heart rate" and "respiratory rate" values. The duration of KMC application had statistically different effects on temperature and oxygen saturation. One-hour or shorter applications of KMC had a higher effect on the temperature and SpO₂ values. Longitudinal, randomized, controlled studies examining the effects of KMC on vital signs in premature newborns with vital parameters outside the normal reference range are recommended.

PRACTICE IMPLICATIONS

The goal of the NICU nurse is to improve the infant's well-being. The application of KMC is a unique care for the nurse in maintaining the newborn's well-being. The vital signs of newborns hospitalized in the NICU with critical problems may be out of normal limits. KMC is an essential developmental care practice that ensures that the neonate's vital signs are kept within normal limits by relaxing the neonate, reducing stress, increasing comfort, and supporting interventions and treatments. KMC application is unique for each mother‑neonate pair. Depending on the tolerance of the mother and infant in terms of duration, it is recommended to perform KMC in the NICU under the supervision of a nurse. Neonatal nurses should support mothers in giving KMC in the NICU since KMC has ameliorative effects on the vital signs of premature neonates.

Hypoxia-Inducible Pathway Polymorphisms and Their Role in the Complications of Prematurity

Excessive oxidative stress resulting from hyperoxia or hypoxia is a recognized risk factor for diseases of prematurity. However, the role of the hypoxia-related pathway in the development of these diseases has not been well studied. Therefore, this study aimed to investigate the association between four functional single nucleotide polymorphisms (SNPs) in the hypoxia-related pathway, and the development of complications of prematurity in relation to perinatal hypoxia. A total of 334 newborns born before or on the 32nd week of gestation were included in the study. The SNPs studied were HIF1A rs11549465 and rs11549467, VEGFA rs2010963, and rs833061. The findings suggest that the HIF1A rs11549465T allele is an independent protective factor against necrotizing enterocolitis (NEC), but may increase the risk of diffuse white matter injury (DWMI) in newborns exposed to hypoxia at birth and long-term oxygen supplementation. In addition, the rs11549467A allele was found to be an independent protective factor against respiratory distress syndrome (RDS). No significant associations with VEGFA SNPs were observed. These findings indicate the potential involvement of the hypoxia-inducible pathway in the pathogenesis of complications of prematurity. Studies with larger sample sizes are needed to confirm these results and explore their clinical implications.

Oribron: An Origami-Inspired Deformable Rigid Bronchoscope for Radial Support

The structure of a traditional rigid bronchoscope includes proximal, distal, and body, representing an important means to treat hypoxic diseases. However, the body structure is too simple, resulting in the utilization rate of oxygen being usually low. In this work, we reported a deformable rigid bronchoscope (named Oribron) by adding a Waterbomb origami structure to the body. The Waterbomb's backbone is made of films, and the pneumatic actuators are placed inside it to achieve rapid deformation at low pressure. Experiments showed that Waterbomb has a unique deformation mechanism, which can transform from a small-diameter configuration (#1) to a large-diameter configuration (#2), showing excellent radial support capability. When Oribron entered or left the trachea, the Waterbomb remained in #1. When Oribron is working, the Waterbomb transforms from #1 to #2. Since #2 reduces the gap between the bronchoscope and the tracheal wall, it effectively slows down the rate of oxygen loss, thus promoting the absorption of oxygen by the patient. Therefore, we believe that this work will provide a new strategy for the integrated development of origami and medical devices.

Clinical Study of Continuous Non-Invasive Blood Pressure Monitoring in Neonates

The continuous monitoring of arterial blood pressure (BP) is vital for assessing and treating cardiovascular instability in a sick infant. Currently, invasive catheters are inserted into an artery to monitor critically-ill infants. Catheterization requires skill, is time consuming, prone to complications, and often painful. Herein, we report on the feasibility and accuracy of a non-invasive, wearable device that is easy to place and operate and continuously monitors BP without the need for external calibration. The device uses capacitive sensors to acquire pulse waveform measurements from the wrist and/or foot of preterm and term infants. Systolic, diastolic, and mean arterial pressures are inferred from the recorded pulse waveform data using algorithms trained using artificial neural network (ANN) techniques. The sensor-derived, continuous, non-invasive BP data were compared with corresponding invasive arterial line (IAL) data from 81 infants with a wide variety of pathologies to conclude that inferred BP values meet FDA-level accuracy requirements for these critically ill, yet normotensive term and preterm infants.

Device for remote and realtime monitoring of neonatal vital signs in neonatal intensive care unit using internet of things: proof-of-concept study

BACKGROUND

Realtime and remote monitoring of neonatal vital signs is a crucial part of providing appropriate care in neonatal intensive care units (NICU) to reduce mortality and morbidity of newborns. In this study, a new approach, a device for remote and real-time monitoring of neonatal vital signs (DRRMNVS) in the neonatal intensive care unit using the internet of things (IoT), was proposed. The system integrates four vital signs: oxygen saturation, pulse rate, body temperature and respiration rate for continuous monitoring using the Blynk app and ThingSpeak IoT platforms.

METHODS

The Wemos D1 mini, a Wi-Fi microcontroller, was used to acquire the four biological biomarkers from sensors, process them and display the result on an OLED display for point of care monitoring and on the Blynk app and ThingSpeak for remote and continuous monitoring of vital signs. The Bland-Altman test was employed to test the agreement of DRRMNVS measurement with reference standards by taking measurements from ten healthy adults.

RESULTS

The prototype of the proposed device was successfully developed and tested. Bias [limits of agreement] were: Oxygen saturation (SpO2): -0.1 [- 1.546 to + 1.346] %; pulse rate: -0.3 [- 2.159 to + 1.559] bpm; respiratory rate: -0.7 [- 0.247 to + 1.647] breaths/min; temperature: 0.21 [+ 0.015˚C to + 0.405˚C] ˚C. The proof-of-concept prototype was developed for $33.19.

CONCLUSION

The developed DRRMNVS device was cheap and had acceptable measurement accuracy of vital signs in a controlled environment. The system has the potential to advance healthcare service delivery for neonates with further development from this proof-of-concept level.

Preterm sepsis is associated with acute lung injury as measured by pulmonary severity score

BACKGROUND

Sepsis related acute lung injury (ALI) is established in adults but has not been investigated in premature infants. Herein, we used pulmonary severity score (PSS) trajectories and C-reactive protein (CRP) to examine the relation between sepsis and ALI in premature infants.

METHODS

This retrospective study identified 211 sepsis and 123 rule out (RO) events in 443 infants born <31 weeks and <1500 grams. The PSS was calculated prior to, at the time of, and up to 1 week after each event. Initial and peak CRP values were collected for each event.

RESULTS

PSS significantly increased at 0 h from baseline (-72h) and remained increased at all subsequent time points (all p < 0.002) in sepsis events. Mean PSS in sepsis episodes were also higher compared to RO events at +24 h, +48 h, +72 h, and +168 h (all p < 0.004). A positive correlation was noted between peak CRP values in sepsis events and PSS at 0 h, +24 h, +48 h, and +72 h.

CONCLUSIONS

The temporal PSS trends and correlation with CRP levels observed in sepsis but not in RO events supports the hypothesis that neonatal sepsis is associated with ALI and contributes to the accumulating evidence that neonatal ARDS occurs.

IMPACT

To evaluate pulmonary severity scores and c-reactive protein values over time to establish an association between preterm neonatal sepsis and acute lung injury (ALI). Though sepsis is well established as the most common indirect cause of ALI leading to acute respiratory distress syndrome (ARDS) in adults and pediatrics, this phenomenon remains undefined in neonates. This study validates the proposal by the Neonatal ARDS Project that ARDS also occurs in neonates by demonstrating acute and sustained changes in markers of pulmonary injury temporally related to a diagnosis of neonatal sepsis in preterm infants.

Dynamic touch induces autonomic changes in preterm infants as measured by changes in heart rate variability

Preterm birth significantly increases the risk of developing various long-term health problems and developmental disabilities. While touch is a crucial component of many perinatal care strategies, the neurobiological underpinnings are rarely considered. C-tactile fibers (CTs) are unmyelinated nerve fibers that are activated by low-force, dynamic touch. Touch directed specifically at CTs activates the posterior insular cortex, consistent with an interoceptive function, and has been shown to reduce heart rate and increase oxygen saturation. The current research compared the effect of five minutes of CT optimal velocity stroking touch versus five minutes of static touch on autonomic markers of preterm infants between 28 and 37 weeks gestational age. CT touch induces a higher increase in heart rate variability metrics related to the parasympathetic system, which persisted for a 5-minute post-touch period. Conversely, there was no such increase in infants receiving static touch. The present findings confirmed that CTs signal the affective quality of nurturing touch, thereby arguing an additional neurobiological substrate for the evident valuable impacts of neonatal tactile interventions and improving the effectiveness of such interventions.

Conditional Generative Adversarial Networks for Data Augmentation of a Neonatal Image Dataset

In today's neonatal intensive care units, monitoring vital signs such as heart rate and respiration is fundamental for neonatal care. However, the attached sensors and electrodes restrict movement and can cause medical-adhesive-related skin injuries due to the immature skin of preterm infants, which may lead to serious complications. Thus, unobtrusive camera-based monitoring techniques in combination with image processing algorithms based on deep learning have the potential to allow cable-free vital signs measurements. Since the accuracy of deep-learning-based methods depends on the amount of training data, proper validation of the algorithms is difficult due to the limited image data of neonates. In order to enlarge such datasets, this study investigates the application of a conditional generative adversarial network for data augmentation by using edge detection frames from neonates to create RGB images. Different edge detection algorithms were used to validate the input images' effect on the adversarial network's generator. The state-of-the-art network architecture Pix2PixHD was adapted, and several hyperparameters were optimized. The quality of the generated RGB images was evaluated using a Mechanical Turk-like multistage survey conducted by 30 volunteers and the FID score. In a fake-only stage, 23% of the images were categorized as real. A direct comparison of generated and real (manually augmented) images revealed that 28% of the fake data were evaluated as more realistic. An FID score of 103.82 was achieved. Therefore, the conducted study shows promising results for the training and application of conditional generative adversarial networks to augment highly limited neonatal image datasets.

Feasibility, performance and acceptability of an innovative vital signs monitor for sick newborns in Western Kenya: A mixed-methods study

Introduction

Low- and middle-income countries (LMICs) account for 99% of the global neonatal mortality. Limited access to advanced technology, such as bedside patient monitors contributes to disproportionately poor outcomes for critically ill newborns in LMICs. We designed a study to assess the feasibility, performance, and acceptability of a low-cost wireless wearable technology for continuous monitoring of sick newborns in resource-limited settings.

Methods

This was a mixed-methods implementation study conducted between March and April 2021 at two health facilities in Western Kenya. Inclusion criteria for newborns monitored included: age 0 to 28 days, birthweight ≥2.0 kg, low-to-moderate severity of illness at admission and the guardian's willingness to provide informed consent. Medical staff who participated in monitoring the newborns were surveyed about their experience with the technology. We used descriptive statistics to summarize our quantitative findings and qualitative data was coded and analyzed as an iterative process to summarize quotes on user acceptability.

Results

The results of the study demonstrated that adoption of neoGuard was feasible and acceptable in this setting. Medical staff described the technology as safe, user-friendly and efficient, after successfully monitoring 134 newborns. Despite the positive user experience, we did observe some notable technology performance issues such as a high percentage of missing vital signs data.

Conclusion

The results of this study were critical in informing the iterative process of refining and validating an innovative vital signs monitor for patients in resource-limited settings. Further research and development are underway to optimize neoGuard's performance and to examine its clinical impact and cost effectiveness.

Nurses' knowledge, perceived challenges, and recommended solutions regarding premature infant care: A mixed method study in the referral and tertiary hospitals in Dar es salaam, Tanzania

BACKGROUND

There has been an increase in preterm birth of about 2% in a span of 14 years (2000-2014) mainly from Asia and Sub-Saharan Africa. Nursing care is very crucial and a lack of knowledge of health care providers is a contributing factor to morbidity and mortality. With the increasing number and investment of preterm infants towards attaining sustainable development goals (SDG) 3.2, nurses' knowledge adequacy, challenges and solutions on their care needs to be affirmed.

METHODS

A mixed method study was conducted between September 2020 to January 2021 in the neonatal units of four hospitals in Dar es Salaam. Self-administered structured questionnaire was used to assess adequacy of knowledge set at 50% or more for the three main domains 1) Essential newborn Care 2) Infection prevention and management 3) Special care and monitoring. A phenomenological design using a structured interview guide focused on challenges and recommended solutions in acquiring on-the- job training on the care of preterm infants. Quantitative data were analyzed using SPSS version 23 and qualitative data were thematically categorized.

RESULTS

Out of 52 of nurses who participated and providing care to preterm infants; 48.1% came from a tertiary hospital, (84.6%) were females, only 28.8% aged more than 40 years and 23.1% had less than one year of experience. Overall, 55.8% of the nurses had never received on job training. Adequate knowledge among nurses was 94% on essential newborn care, 80.8% on infection prevention and management and 36.5% on special care and monitoring of preterm infants. Generally, immediate actions of helping baby breath (HBB) and cord care scored poorest. Components on special care and monitoring which had lowest scores included blood glucose monitoring, temperature monitoring and acceptable daily weight gain. Being more than 41 years old, a female nurse and working in the neonatal unit for at least 1-3 years were more likely to determine adequacy of knowledge on infection prevention and management. Lack of schedule and ways to identify those who require training were among the challenges mentioned in the focus group discussion.

CONCLUSION

The findings demonstrate an urgent need of instilling knowledge, skills and competences among nurses providing preterm care in our hospitals. Most nurses had not attended training on the care of premature infant. Special care and monitoring were most poorly performed. The recommended solutions included continuous medical education (CME) for all nurses through hospital and government commitment and encourage mentorship within and between hospitals. Nurses who are female, older than 41 years and those with 1 to 3 years of experience should be considered when planning for CME and mentorship program on infection prevention and management.

Omics approaches: interactions at the maternal-fetal interface and origins of child health and disease

Immunoperinatology is an emerging field. Transdisciplinary efforts by physicians, physician-scientists, basic science researchers, and computational biologists have made substantial advancements by identifying unique immunologic signatures of specific diseases, discovering innovative preventative or treatment strategies, and establishing foundations for individualized neonatal intensive care of the most vulnerable neonates. In this review, we summarize the immunobiology and immunopathology of pregnancy, highlight omics approaches to study the maternal-fetal interface, and their contributions to pregnancy health. We examined the importance of transdisciplinary, multiomic (such as genomics, transcriptomics, proteomics, metabolomics, and immunomics) and machine-learning strategies in unraveling the mechanisms of adverse pregnancy, neonatal, and childhood outcomes and how they can guide the development of novel therapies to improve maternal and neonatal health. IMPACT: Discuss immunoperinatology research from the lens of omics and machine-learning approaches. Identify opportunities for omics-based approaches to delineate infection/inflammation-associated maternal, neonatal, and later life adverse outcomes (e.g., histologic chorioamnionitis [HCA]).

Artificial and human intelligence for early identification of neonatal sepsis

Artificial intelligence may have a role in the early detection of sepsis in neonates. Machine learning can identify patterns that predict high or increasing risk for clinical deterioration from a sepsis-like illness. In developing this potential addition to NICU care, careful consideration should be given to the data and methods used to develop, validate, and evaluate prediction models. When an AI system alerts clinicians to a change in a patient's condition that warrants a bedside evaluation, human intelligence and experience come into play to determine an appropriate course of action: evaluate and treat or wait and watch closely. With intelligently developed, validated, and implemented AI sepsis systems, both clinicians and patients stand to benefit. IMPACT: This narrative review highlights the application of AI in neonatal sepsis prediction. It describes issues in clinical prediction model development specific to this population. This article reviews the methods, considerations, and literature on neonatal sepsis model development and validation. Challenges of AI technology and potential barriers to using sepsis AI systems in the NICU are discussed.

Assessment of neonatal respiratory rate variability

Accurate measurement of respiratory rate (RR) in neonates is challenging due to high neonatal RR variability (RRV). There is growing evidence that RRV measurement could inform and guide neonatal care. We sought to quantify neonatal RRV during a clinical study in which we compared multiparameter continuous physiological monitoring (MCPM) devices. Measurements of capnography-recorded exhaled carbon dioxide across 60-s epochs were collected from neonates admitted to the neonatal unit at Aga Khan University-Nairobi hospital. Breaths were manually counted from capnograms and using an automated signal detection algorithm which also calculated mean and median RR for each epoch. Outcome measures were between- and within-neonate RRV, between- and within-epoch RRV, and 95% limits of agreement, bias, and root-mean-square deviation. Twenty-seven neonates were included, with 130 epochs analysed. Mean manual breath count (MBC) was 48 breaths per minute. Median RRV ranged from 11.5% (interquartile range (IQR) 6.8-18.9%) to 28.1% (IQR 23.5-36.7%). Bias and limits of agreement for MBC vs algorithm-derived breath count, MBC vs algorithm-derived median breath rate, MBC vs algorithm-derived mean breath rate were - 0.5 (- 2.7, 1.66), - 3.16 (- 12.12, 5.8), and - 3.99 (- 11.3, 3.32), respectively. The marked RRV highlights the challenge of performing accurate RR measurements in neonates. More research is required to optimize the use of RRV to improve care. When evaluating MCPM devices, accuracy thresholds should be less stringent in newborns due to increased RRV. Lastly, median RR, which discounts the impact of extreme outliers, may be more reflective of the underlying physiological control of breathing.

Remote sensing of vital signs by medical radar time-series signal using cardiac peak extraction and adaptive peak detection algorithm: Performance validation on healthy adults and application to neonatal monitoring at an NICU

BACKGROUND AND OBJECTIVE

Continuous monitoring of vital signs plays a pivotal role in neonatal intensive care units (NICUs). In this paper, we present a system for monitoring fully non-contact medical radar-based vital signs to measure the respiratory rate (RR), heart rate (HR), I:E ratio, and heart rate variability (HRV). In addition, we evaluated its performance in a physiological laboratory and examined its adaptability in an NICU.

METHODS

A non-contact medical radar-based vital sign monitoring system that includes 24 GHz radar installed in an incubator was developed. To enable reliable monitoring, an advanced signal processing algorithm (i.e., a nonlinear filter to separate respiration and heartbeat signals from the output of radar), template matching to extract cardiac peaks, and an adaptive peak detection algorithm to estimate cardiac peaks in time-series were proposed and implemented in the system. Nine healthy subjects comprising five males and four females (24 ± 5 years) participated in the laboratory test. To evaluate the adaptability of the system in an NICU setting, we tested it with three hospitalized infants, including two neonates.

RESULTS

The results indicate strong agreement in healthy subjects between the non-contact system and reference contact devices for RR, HR, and inter-beat interval (IBI) measurement, with correlation coefficients of 0.83, 0.96, and 0.94, respectively. As anticipated, the template matching and adaptive peak detection algorithms outperformed the conventional approach. These showed a more accurate IBI close to the reference Bland-Altman analysis (proposed: bias of -3 ms, and 95% limits of agreement ranging from -73 to 67 ms; conventional: bias of -11 ms, and 95% limits of agreement ranging from -229 to 207 ms). Moreover, in the NICU clinical setting, the IBI correlation coefficient and 95% limit of agreement in the conventional method are 0.31 and 91 ms. The corresponding values obtained using the proposed method are 0.93 and 21 ms.

CONCLUSION

The proposed system introduces a novel approach for NICU monitoring using a non-contact medical radar sensor. The signal processing method combining cardiac peak extraction algorithm with the adaptive peak detection algorithm shows high adaptability in detecting IBI the time series in various application settings.

Neonatal Vital Sign Trajectories and Risk Factors During Transport Within a Regional Care Network

OBJECTIVE

The aim of this study was to characterize vital sign abnormalities, trajectories, and related risk factors during neonatal transport.

METHODS

We performed a retrospective analysis of neonates transported within a US regional care network in 2020 to 2021. Demographic and clinical data were collected from electronic records. Group-based trajectory modeling was applied to identify groups of neonates who followed distinct vital sign trajectories during transport. Patients with conditions likely to impact the assessed vital were excluded. Risk factors for trajectories were examined using modified Poisson regression models.

RESULTS

Of the 620 neonates in the study, 92% had one abnormal systolic blood pressure (SBP) measure, approximately half had an abnormal heart rate (47%) or temperature (56%), and 28% had an abnormal oxygen saturation measure during transport. Over half (53%) were in a low and decreasing SBP trajectory, and 36% were in a high and increasing heart rate trajectory. Most infants ≤ 28 weeks postmenstrual age had 2 or more concerning vital sign trajectories during transport.

CONCLUSION

Abnormal vital signs were common during neonatal transport, and potentially negative trajectories in heart rate and SBP were more common than temperature or oxygen saturation. Transport teams should be trained and equipped to detect concerning trends and respond appropriately while en route.

Application and potential of artificial intelligence in neonatal medicine

Neonatal care is becoming increasingly complex with large amounts of rich, routinely recorded physiological, diagnostic and outcome data. Artificial intelligence (AI) has the potential to harness this vast quantity and range of information and become a powerful tool to support clinical decision making, personalised care, precise prognostics, and enhance patient safety. Current AI approaches in neonatal medicine include tools for disease prediction and risk stratification, neurological diagnostic support and novel image recognition technologies. Key to the integration of AI in neonatal medicine is the understanding of its limitations and a standardised critical appraisal of AI tools. Barriers and challenges to this include the quality of datasets used, performance assessment, and appropriate external validation and clinical impact studies. Improving digital literacy amongst healthcare professionals and cross-disciplinary collaborations are needed to harness the full potential of AI to help take the next significant steps in improving neonatal outcomes for high-risk infants.

Biomarker und Neuromonitoring zur Entwicklungsprognose nach perinataler Hirnschädigung

Das sich entwickelnde Gehirn ist in der Perinatalperiode besonders empfindlich für eine Vielzahl von Insulten, wie z. B. Extremfrühgeburtlichkeit und perinatale Asphyxie. Ihre Komplikationen können zu lebenslangen neurokognitiven, sensorischen und psychosozialen Einschränkungen führen; deren Vorhersage bleibt eine Herausforderung. Eine Schlüsselfunktion kommt der möglichst exakten Identifikation von Hirnläsionen und funktionellen Störungen zu. Die Prädiktion stützt sich auf frühe diagnostische Verfahren und die klinische Erfassung der Meilensteine der Entwicklung. Zur klinischen Diagnostik und zum Neuromonitoring in der Neonatal- und frühen Säuglingsperiode stehen bildgebende Verfahren zur Verfügung. Hierzu zählen zerebrale Sonographie, MRT am errechneten Termin, amplitudenintegriertes (a)EEG und/oder klassisches EEG, Nah-Infrarot-Spektroskopie, General Movements Assessment und die frühe klinische Nachuntersuchung z. B. mithilfe der Hammersmith Neonatal/Infant Neurological Examination. Innovative Biomarker und -muster (Omics) sowie (epi)genetische Prädispositionen sind Gegenstand wissenschaftlicher Untersuchungen. Neben der Erfassung klinischer Risiken kommt psychosozialen Faktoren im Umfeld des Kindes eine entscheidende Rolle zu. Eine möglichst akkurate Prognose ist mit hohem Aufwand verbunden, jedoch zur gezielten Beratung der Familien und der Einleitung von frühen Interventionen, insbesondere vor dem Hintergrund der hohen Plastizität des sich entwickelnden Gehirns, von großer Bedeutung. Diese Übersichtsarbeit fokussiert die Charakterisierung der oben genannten Verfahren und ihrer Kombinationsmöglichkeiten. Zudem wird ein Ausblick gegeben, wie innovative Techniken in Zukunft die Prädiktion der Entwicklung und Nachsorge dieser Kinder vereinfachen können.

Evaluation of a contactless neonatal physiological monitor in Nairobi, Kenya

BACKGROUND

Globally, 2.5 million neonates died in 2018, accounting for 46% of under-5 deaths. Multiparameter continuous physiological monitoring (MCPM) of neonates allows for early detection and treatment of life-threatening health problems. However, neonatal monitoring technology is largely unavailable in low-resource settings.

METHODS

In four evaluation rounds, we prospectively compared the accuracy of the EarlySense under-mattress device to the Masimo Rad-97 pulse CO-oximeter with capnography reference device for heart rate (HR) and respiratory rate (RR) measurements in neonates in Kenya. EarlySense algorithm optimisations were made between evaluation rounds. In each evaluation round, we compared 200 randomly selected epochs of data using Bland-Altman plots and generated Clarke error grids with zones of 20% to aid in clinical interpretation.

RESULTS

Between 9 July 2019 and 8 January 2020, we collected 280 hours of MCPM data from 76 enrolled neonates. At the final evaluation round, the EarlySense MCPM device demonstrated a bias of -0.8 beats/minute for HR and 1.6 breaths/minute for RR, and normalised spread between the 95% upper and lower limits of agreement of 6.2% for HR and 27.3% for RR. Agreement between the two MCPM devices met the a priori-defined threshold of 30%. The Clarke error grids showed that all observations for HR and 197/200 for RR were within a 20% difference.

CONCLUSION

Our research indicates that there is acceptable agreement between the EarlySense and Masimo MCPM devices in the context of large within-subject variability; however, further studies establishing cost-effectiveness and clinical effectiveness are needed before large-scale implementation of the EarlySense MCPM device in neonates.

TRIAL REGISTRATION NUMBER

NCT03920761.

Need for oxygen therapy and ventilatory support in premature infants in a hospital in Southern Brazil

BACKGROUND

Prematurity in newborns is a condition that is associated with worse hospital outcomes when compared to birth to term. A preterm infant (PI) is classified when gestational age (GA) < 37 wk.

AIM

To analyze prognostic indicators related to the use of oxygen therapy, non-invasive ventilation (continuous positive airway pressure) and mechanical ventilation (MV) in PI.

METHODS

This is a retrospective cohort. The sample was composed of PIs from a private hospital in southern Brazil. We included neonates with GA < 37 wk of gestation in the period of January 1, 2018 to December 31, 2018. For data collection, electronic records were used in the Tasy PhilipsTM system, identifying the variables: maternal age, type of birth, prenatal information, GA, Apgar score, birth weight, neonatal morbidities, vital signs in the 1st hour at birth, need for oxygen therapy, continuous positive airway pressure and MV, hospitalization in the neonatal intensive care unit, length of stay and discharge or death.

RESULTS

In total, 90 PI records were analyzed. The median (p25-p75) of GA was 34.0 (31.9-35.4) wk, and there were 45 (50%) males. The most common morbidity among PIs was the acute respiratory discomfort syndrome, requiring hospitalization in the neonatal intensive care unit in 76 (84.4%) cases. The utilization rate of oxygen therapy, continuous positive airway pressure and MV was 12 (13.3%), 37 (41.1%) and 13 (14.4%), respectively. The median (p25-p75) length of stay was 12.0 (5.0-22.2) d, with 10 (11.1%) deaths. A statistical association was observed with the use of MV and GA < 28 wk, lower maternal age, low birth weight, Apgar < 8 and neonatal deaths.

CONCLUSION

The identification of factors related to the need for MV in prematurity may help in the indication of a qualified team and technologies to promptly meet the unforeseen events that may occur after birth.

Osteopathic Manipulative Treatment Regulates Autonomic Markers in Preterm Infants: A Randomized Clinical Trial

Osteopathic manipulative treatment (OMT) has been found to be effective in the context of premature infants. Nonetheless, no studies have investigated the immediate effects of OMT on heart rate variability (HRV). As altered HRV reflects poor or worsening newborn's clinical conditions and neurodevelopment, should OMT improve HRV fluctuations, it could become a relevant intervention for improving the care of preterm newborns. Therefore, this study aimed to evaluate whether OMT could affect HRV. The study was carried out at the Buzzi Hospital in Milan. From the neonatal intensive care unit, ninety-six preterm infants (41 males) were enrolled and were randomly assigned to one of two treatment groups: OMT or Static Touch. The infants were born at 33.5 weeks (±4.3) and had a mean birth weight of 2067 g (±929). The study had as primary outcome the change in the beat-to-beat variance in heart rate measured through root mean square of consecutive RR interval differences (RMSSD); other metrics were used as secondary and exploratory analyses. Despite the lack of statistically significant results regarding the primary outcomeand some study limitations, compared to static touch, OMT seemed to favor a parasympathetic modulation and improved HRV, which could reflect improvement in newborn's clinical conditions and development.

Longitudinal Analysis of Continuous Pulse Oximetry as Prognostic Factor in Neonatal Respiratory Distress

OBJECTIVE

Analysis of longitudinal data can provide neonatologists with tools that can help predict clinical deterioration and improve outcomes. The aim of this study is to analyze continuous monitoring data in newborns, using vital signs to develop predictive models for intensive care admission and time to discharge.

STUDY DESIGN

We conducted a retrospective cohort study, including term and preterm newborns with respiratory distress patients admitted to the neonatal ward. Clinical and epidemiological data, as well as mean heart rate and saturation, at every minute for the first 12 hours of admission were collected. Multivariate mixed, survival and joint models were developed.

RESULTS

A total of 56,377 heart rate and 56,412 oxygen saturation data were analyzed from 80 admitted patients. Of them, 73 were discharged home and 7 required transfer to the intensive care unit (ICU). Longitudinal evolution of heart rate (p < 0.01) and oxygen saturation (p = 0.01) were associated with time to discharge, as well as birth weight (p < 0.01) and type of delivery (p < 0.01). Longitudinal heart rate evolution (p < 0.01) and fraction of inspired oxygen at admission at the ward (p < 0.01) predicted neonatal ICU (NICU) admission.

CONCLUSION

Longitudinal evolution of heart rate can help predict time to transfer to intensive care, and both heart rate and oxygen saturation can help predict time to discharge. Analysis of continuous monitoring data in patients admitted to neonatal wards provides useful tools to stratify risks and helps in taking medical decisions.

KEY POINTS

· Continuous monitoring of vital signs can help predict and prevent clinical deterioration in neonatal patients.. · In our study, longitudinal analysis of heart rate and oxygen saturation predicted time to discharge and intensive care admission.. · More studies are needed to prospectively prove that these models can helpmake clinical decisions and stratify patients' risks..

Vital signs as physiomarkers of neonatal sepsis

Neonatal sepsis accounts for significant morbidity and mortality, particularly among premature infants in the Neonatal Intensive Care Unit. Abnormal vital sign patterns serve as physiomarkers of sepsis and provide early warning of illness before overt clinical decompensation. The systemic inflammatory response to pathogens signals the autonomic nervous system, leading to changes in temperature, respiratory rate, heart rate, and blood pressure. In infants with comorbidities of prematurity, vital sign abnormalities often occur in the absence of infection, which confounds sepsis diagnosis. This review will cover the mechanisms of vital sign changes in neonatal sepsis, including the cholinergic anti-inflammatory pathway mediated by the vagus nerve, which is critical to the host response to infectious and inflammatory insults. We will also review the clinical implications of vital sign changes in neonatal sepsis, including their use in early warning scores and systems to direct clinicians to the bedside of infants with physiologic changes that might be due to sepsis. IMPACT: This manuscript summarizes and reviews the relevant literature on the physiological manifestations of neonatal sepsis and how we monitor and analyze these through vital signs and advanced analytics.

Newborns' clinical conditions are correlated with the neonatal assessment manual scorE (NAME)

OBJECTIVE

To investigate the relationship between the Neonatal Assessment Manual scorE (NAME) and newborns' clinical condition on a large number of infants. The NAME model was developed as an instrument to assess the infant's general conditions, especially in NICUs, by evaluating how the infant's body responds to an external stressor such as static touch. Previous studies, employing experienced assessors, showed good validity indices as well as high inter-rater reliability.

STUDY DESIGN

Newborns were recruited at the "Vittore Buzzi" Pediatric Hospital NICU ward in Milan and their clinical conditions were collected through a standardized form-the complexity index. Two manual practitioners assessed all eligible newborns using the NAME scores. Data was analyzed using Kendall's τ correlation and odds ratio (OR) to assess the relationship between the NAME scores and the complexity index.

RESULTS

Two hundred two newborns (46% female; 34.1 w ± 4.3; birth weight of 2,093.4 gr ± 879.8) entered the study. The Kendall's correlation between the clinical conditions (complexity index) and the NAME score was -0.206 [95% CI: (-0.292, -0.116), p-value < 0.001], corresponding to an OR of 0.838 [95% CI: (0.757, 0.924), p-value < 0.001]. Further exploratory analyses showed significant correlation between gestational age, birth weight and NAME scores.

CONCLUSION

The present paper adds evidence to the NAME model validity by demonstrating its applicability in the clinical neonatological context.

Knowledge gaps in late-onset neonatal sepsis in preterm neonates: a roadmap for future research

Late-onset neonatal sepsis (LONS) remains an important threat to the health of preterm neonates in the neonatal intensive care unit. Strategies to optimize care for preterm neonates with LONS are likely to improve survival and long-term neurocognitive outcomes. However, many important questions on how to improve the prevention, early detection, and therapy for LONS in preterm neonates remain unanswered. This review identifies important knowledge gaps in the management of LONS and describe possible methods and technologies that can be used to resolve these knowledge gaps. The availability of computational medicine and hypothesis-free-omics approaches give way to building bedside feedback tools to guide clinicians in personalized management of LONS. Despite advances in technology, implementation in clinical practice is largely lacking although such tools would help clinicians to optimize many aspects of the management of LONS. We outline which steps are needed to get possible research findings implemented on the neonatal intensive care unit and provide a roadmap for future research initiatives. IMPACT: This review identifies knowledge gaps in prevention, early detection, antibiotic, and additional therapy of late-onset neonatal sepsis in preterm neonates and provides a roadmap for future research efforts. Research opportunities are addressed, which could provide the means to fill knowledge gaps and the steps that need to be made before possible clinical use. Methods to personalize medicine and technologies feasible for bedside clinical use are described.

Hourly Kinetics of Critical Organ Dysfunction in Extremely Preterm Infants

Rationale: Use of severity of illness scores to classify patients for clinical care and research is common outside of the neonatal ICU. Extremely premature (<29 weeks' gestation) infants with extremely low birth weight (<1,000 g) experience significant mortality and develop severe pathology during the protracted birth hospitalization. Objectives: To measure at high resolution the changes in organ dysfunction that occur from birth to death or discharge home by gestational age and time, and among extremely preterm infants with and without clinically meaningful outcomes using the neonatal sequential organ failure assessment score. Methods: A single-center, retrospective, observational cohort study of inborn, extremely preterm infants with extremely low birth weight admitted between January 2012 and January 2020. Neonatal sequential organ failure assessment scores were calculated every hour for every patient from admission until death or discharge. Measurements and Main Results: Longitudinal, granular scores from 436 infants demonstrated early and sustained discrimination of those who died versus those who survived to discharge. The discrimination for mortality by the maximum score was excellent (area under curve, 0.91; 95% confidence intervals, 0.88-0.94). Among survivors with and without adverse outcomes, most score variation occurred at the patient level. The weekly average score over the first 28 days was associated with the sum of adverse outcomes at discharge. Conclusions: The neonatal sequential organ failure assessment score discriminates between survival and nonsurvival on the first day of life. The major contributor to score variation occurred at the patient level. There was a direct association between scores and major adverse outcomes, including death.

New method to measure interbreath intervals in infants for the assessment of apnoea and respiration

Background

Respiratory disorders, including apnoea, are common in preterm infants due to their immature respiratory control compared with term-born infants. However, our inability to accurately measure respiratory rate in hospitalised infants results in unreported episodes of apnoea and an incomplete picture of respiratory activity.

Methods

We develop, validate and use a novel algorithm to identify interbreath intervals (IBIs) and apnoeas in preterm infants. In 42 preterm infants (1600 hours of recordings), we assess IBIs from the chest electrical impedance pneumograph using an adaptive amplitude threshold for the detection of breaths. The algorithm is refined by comparing its accuracy with clinically observed breaths and pauses in breathing. We develop an automated classifier to differentiate periods of true apnoea from artefactually low amplitude signal. We assess the performance of this algorithm in the detection of morphine-induced respiratory depression. Finally, we use the algorithm to investigate whether retinopathy of prematurity (ROP) screening alters the IBI distribution.

Results

Individual breaths were detected with a false-positive rate of 13% and a false-negative rate of 12%. The classifier identified true apnoeas with an accuracy of 93%. As expected, morphine caused a significant shift in the IBI distribution towards longer IBIs. Following ROP screening, there was a significant increase in pauses in breathing that lasted more than 10 s (t-statistic=1.82, p=0.023). This was not reflected by changes in the monitor-derived respiratory rate and no episodes of apnoea were recorded in the medical records.

Conclusions

We show that our algorithm offers an improved method for the identification of IBIs and apnoeas in preterm infants. Following ROP screening, increased respiratory instability can occur even in the absence of clinically significant apnoeas. Accurate assessment of infant respiratory activity is essential to inform clinical practice.

Neonatal sepsis prediction through clinical decision support algorithms: A systematic review

AIM

To systematically summarise the current evidence of employing clinical decision support algorithms (CDSAs) using non-invasive parameters for sepsis prediction in neonates.

METHODS

A comprehensive search in PubMed, CENTRAL and EMBASE was conducted. Screening, data extraction and risk of bias were performed by two authors. The certainty of the evidence was assessed using GRADE.

PROSPERO ID

CRD42020205143.

RESULTS

After abstract and full-text screening, 36 studies comprising 18,096 infants were included. Most CDSAs evaluated heart rate (HR)-based parameters. Two publications derived from one randomised-controlled trial assessing HR characteristics reported significant reduction in 30-day septicaemia-related mortality. Thirty-four non-randomised studies found promising yet inconclusive results.

CONCLUSION

Heart rate-based parameters are reliable components of CDSAs for sepsis prediction, particularly in combination with additional vital signs and demographics. However, inconclusive evidence and limited standardisation restricts clinical implementation of CDSAs outside of a controlled research environment. Further experimentation and comparison of parameter combinations and testing of new CDSAs are warranted.