Association between initial ventilation mode and hospital outcomes for severe congenital diaphragmatic hernia.

OBJECTIVE: To determine the association between initial delivery room (DR) ventilator (conventional mechanical ventilation [CMV] versus high frequency oscillatory ventilation [HFOV] and hospital outcomes for infants with severe congenital diaphragmatic hernia (CDH). STUDY DESIGN: Quasi-experimental design before/after introducing a clinical protocol promoting HFOV. The primary outcome was first blood gas parameters. Secondary outcomes included serial blood gas assessments, ECMO, survival, duration of ventilation, and length of hospitalization. RESULTS: First pH and CO2 were more favorable in the HFOV group (n = 75) than CMV group (n = 85), median (interquartile range (IQR)) pH 7.18 (7.03, 7.24) vs. 7.05 (6.93, 7.17), adjusted p-value < 0.001; median CO2 62.0 (46.0, 82.0) vs 85.9 (59.0, 103.0), adjusted p-value < 0.001. ECMO, survival, duration of ventilation, and length of hospitalization did not differ between groups in adjusted analysis. CONCLUSION: Among infants with severe CDH, initial DR HFOV was associated with improved early gas exchange with no adverse differences in hospital outcomes.

Development and Implementation of Digital Diagnostic Algorithms for Neonatal Units in Zimbabwe and Malawi: Development and Usability Study.

BACKGROUND: Despite an increase in hospital-based deliveries, neonatal mortality remains high in low-resource settings. Due to limited laboratory diagnostics, there is significant reliance on clinical findings to inform diagnoses. Accurate, evidence-based identification and management of neonatal conditions could improve outcomes by standardizing care. This could be achieved through digital clinical decision support (CDS) tools. Neotree is a digital, quality improvement platform that incorporates CDS, aiming to improve neonatal care in low-resource health care facilities. Before this study, first-phase CDS development included developing and implementing neonatal resuscitation algorithms, creating initial versions of CDS to address a range of neonatal conditions, and a Delphi study to review key algorithms. OBJECTIVE: This second-phase study aims to codevelop and implement neonatal digital CDS algorithms in Malawi and Zimbabwe. METHODS: Overall, 11 diagnosis-specific web-based workshops with Zimbabwean, Malawian, and UK neonatal experts were conducted (August 2021 to April 2022) encompassing the following: (1) review of available evidence, (2) review of country-specific guidelines (Essential Medicines List and Standard Treatment Guidelinesfor Zimbabwe and Care of the Infant and Newborn, Malawi), and (3) identification of uncertainties within the literature for future studies. After agreement of clinical content, the algorithms were programmed into a test script, tested with the respective hospital's health care professionals (HCPs), and refined according to their feedback. Once finalized, the algorithms were programmed into the Neotree software and implemented at the tertiary-level implementation sites: Sally Mugabe Central Hospital in Zimbabwe and Kamuzu Central Hospital in Malawi, in December 2021 and May 2022, respectively. In Zimbabwe, usability was evaluated through 2 usability workshops and usability questionnaires: Post-Study System Usability Questionnaire (PSSUQ) and System Usability Scale (SUS). RESULTS: Overall, 11 evidence-based diagnostic and management algorithms were tailored to local resource availability. These refined algorithms were then integrated into Neotree. Where national management guidelines differed, country-specific guidelines were created. In total, 9 HCPs attended the usability workshops and completed the SUS, among whom 8 (89%) completed the PSSUQ. Both usability scores (SUS mean score 75.8 out of 100 [higher score is better]; PSSUQ overall score 2.28 out of 7 [lower score is better]) demonstrated high usability of the CDS function but highlighted issues around technical complexity, which continue to be addressed iteratively. CONCLUSIONS: This study describes the successful development and implementation of the only known neonatal CDS system, incorporated within a bedside data capture system with the ability to deliver up-to-date management guidelines, tailored to local resource availability. This study highlighted the importance of collaborative participatory design. Further implementation evaluation is planned to guide and inform the development of health system and program strategies to support newborn HCPs, with the ultimate goal of reducing preventable neonatal morbidity and mortality in low-resource settings.

Update on Management and Outcomes of Congenital Diaphragmatic Hernia.

Infants with congenital diaphragmatic hernia (CDH) benefit from comprehensive multidisciplinary teams that have experience in caring for the unique and complex issues associated with CDH. Despite prenatal referral to specialized high-volume centers, advanced ventilation strategies and pulmonary hypertension management, and extracorporeal membrane oxygenation, mortality and morbidity remain high. These infants have unique and complex issues that begin in fetal and infant life, but persist through adulthood. Here we will review the literature and share our clinical care pathway for neonatal care and follow up. While many advances have occurred in the past few decades, our work is just beginning to continue to improve the mortality, but also importantly the morbidity of CDH.

Development and implementation experience of a learning healthcare system for facility based newborn care in low resource settings: The Neotree.

Introduction: Improving peri- and postnatal facility-based care in low-resource settings (LRS) could save over 6000 babies' lives per day. Most of the annual 2.4 million neonatal deaths and 2 million stillbirths occur in healthcare facilities in LRS and are preventable through the implementation of cost-effective, simple, evidence-based interventions. However, their implementation is challenging in healthcare systems where one in four babies admitted to neonatal units die. In high-resource settings healthcare systems strengthening is increasingly delivered via learning healthcare systems to optimise care quality, but this approach is rare in LRS. Methods: Since 2014 we have worked in Bangladesh, Malawi, Zimbabwe, and the UK to co-develop and pilot the Neotree system: an android application with accompanying data visualisation, linkage, and export. Its low-cost hardware and state-of-the-art software are used to support healthcare professionals to improve postnatal care at the bedside and to provide insights into population health trends. Here we summarise the formative conceptualisation, development, and preliminary implementation experience of the Neotree. Results: Data thus far from ~18 000 babies, 400 healthcare professionals in four hospitals (two in Zimbabwe, two in Malawi) show high acceptability, feasibility, usability, and improvements in healthcare professionals' ability to deliver newborn care. The data also highlight gaps in knowledge in newborn care and quality improvement. Implementation has been resilient and informative during external crises, for example, coronavirus disease 2019 (COVID-19) pandemic. We have demonstrated evidence of improvements in clinical care and use of data for Quality Improvement (QI) projects. Conclusion: Human-centred digital development of a QI system for newborn care has demonstrated the potential of a sustainable learning healthcare system to improve newborn care and outcomes in LRS. Pilot implementation evaluation is ongoing in three of the four aforementioned hospitals (two in Zimbabwe and one in Malawi) and a larger scale clinical cost effectiveness trial is planned.

Respiratory function after birth in infants with congenital diaphragmatic hernia.

OBJECTIVE: To characterise the transitional pulmonary physiology of infants with congenital diaphragmatic hernia (CDH) using measures of expiratory tidal volume (TV) and end-tidal carbon dioxide (ETCO2). DESIGN: Prospective single-centre observational study. SETTING: Quaternary neonatal intensive care unit. PATIENTS: Infants with an antenatal diagnosis of CDH born at the Children's Hospital of Philadelphia. INTERVENTIONS: TV and ETCO2 were simultaneously recorded using a respiratory function monitor (RFM) during invasive positive pressure ventilation immediately after birth. MAIN OUTCOME MEASURES: TV per birth weight and ETCO2 values were summarised for each minute after birth. Subgroups of interest were defined by liver position (thoracic vs abdominal) and extracorporeal membrane oxygenation (ECMO) treatment. RESULTS: RFM data were available for 50 infants from intubation until a median (IQR) of 9 (7-14) min after birth. TV and ETCO2 values increased for the first 10 min after birth, but intersubject values were heterogeneous. TVs were overall lower and ETCO2 values higher in infants with an intrathoracic liver and infants who were ultimately treated with ECMO. On hospital discharge, survival was 88% (n=43) and 34% (n=17) of infants were treated with ECMO. CONCLUSION: Respiratory function immediately after birth is heterogeneous for infants with CDH. Lung aeration, as evidenced by expired TV and ETCO2, appears to be ongoing throughout the first 10 min after birth during invasive positive pressure ventilation. Close attention to expired TV and ETCO2 levels by 10 min after birth may provide an opportunity to optimise and individualise ventilatory support for this high-risk population.

Protocol for an intervention development and pilot implementation evaluation study of an e-health solution to improve newborn care quality and survival in two low-resource settings, Malawi and Zimbabwe: Neotree.

INTRODUCTION: Every year 2.4 million deaths occur worldwide in babies younger than 28 days. Approximately 70% of these deaths occur in low-resource settings because of failure to implement evidence-based interventions. Digital health technologies may offer an implementation solution. Since 2014, we have worked in Bangladesh, Malawi, Zimbabwe and the UK to develop and pilot Neotree: an android app with accompanying data visualisation, linkage and export. Its low-cost hardware and state-of-the-art software are used to improve bedside postnatal care and to provide insights into population health trends, to impact wider policy and practice. METHODS AND ANALYSIS: This is a mixed methods (1) intervention codevelopment and optimisation and (2) pilot implementation evaluation (including economic evaluation) study. Neotree will be implemented in two hospitals in Zimbabwe, and one in Malawi. Over the 2-year study period clinical and demographic newborn data will be collected via Neotree, in addition to behavioural science informed qualitative and quantitative implementation evaluation and measures of cost, newborn care quality and usability. Neotree clinical decision support algorithms will be optimised according to best available evidence and clinical validation studies. ETHICS AND DISSEMINATION: This is a Wellcome Trust funded project (215742_Z_19_Z). Research ethics approvals have been obtained: Malawi College of Medicine Research and Ethics Committee (P.01/20/2909; P.02/19/2613); UCL (17123/001, 6681/001, 5019/004); Medical Research Council Zimbabwe (MRCZ/A/2570), BRTI and JREC institutional review boards (AP155/2020; JREC/327/19), Sally Mugabe Hospital Ethics Committee (071119/64; 250418/48). Results will be disseminated via academic publications and public and policy engagement activities. In this study, the care for an estimated 15 000 babies across three sites will be impacted. TRIAL REGISTRATION NUMBER: NCT0512707; Pre-results.

Software development process of Neotree - a data capture and decision support system to improve newborn healthcare in low-resource settings.

The global priority of improving neonatal survival could be tackled through the universal implementation of cost-effective maternal and newborn health interventions. Despite 90% of neonatal deaths occurring in low-resource settings, very few evidence-based digital health interventions exist to assist healthcare professionals in clinical decision-making in these settings. To bridge this gap, Neotree was co-developed through an iterative, user-centered design approach in collaboration with healthcare professionals in the UK, Bangladesh, Malawi, and Zimbabwe. It addresses a broad range of neonatal clinical diagnoses and healthcare indicators as opposed to being limited to specific conditions and follows national and international guidelines for newborn care. This digital health intervention includes a mobile application (app) which is designed to be used by healthcare professionals at the bedside. The app enables real-time data capture and provides education in newborn care and clinical decision support via integrated clinical management algorithms. Comprehensive routine patient data are prospectively collected regarding each newborn, as well as maternal data and blood test results, which are used to inform clinical decision making at the bedside. Data dashboards provide healthcare professionals and hospital management a near real-time overview of patient statistics that can be used for healthcare quality improvement purposes. To enable this workflow, the Neotree web editor allows fine-grained customization of the mobile app. The data pipeline manages data flow from the app to secure databases and then to the dashboard. Implemented in three hospitals in two countries so far, Neotree has captured routine data and supported the care of over 21,000 babies and has been used by over 450 healthcare professionals. All code and documentation are open source, allowing adoption and adaptation by clinicians, researchers, and developers.

Admissions to a Low-Resource Neonatal Unit in Malawi Using a Mobile App: Digital Perinatal Outcome Audit.

BACKGROUND: Mobile health (mHealth) is showing increasing potential to address health outcomes in underresourced settings as smartphone coverage increases. The NeoTree is an mHealth app codeveloped in Malawi to improve the quality of newborn care at the point of admission to neonatal units. When collecting vital demographic and clinical data, this interactive platform provides clinical decision support and training for the end users (health care professionals [HCPs]), according to evidence-based national and international guidelines. OBJECTIVE: This study aims to examine 1 month's data collected using NeoTree in an outcome audit of babies admitted to a district-level neonatal nursery in Malawi and to demonstrate proof of concept of digital outcome audit data in this setting. METHODS: Using a phased approach over 1 month (November 21-December 19, 2016), frontline HCPs were trained and supported to use NeoTree to admit newborns. Discharge data were collected by the research team using a discharge form within NeoTree, called NeoDischarge. We conducted a descriptive analysis of the exported pseudoanonymized data and presented it to the newborn care department as a digital outcome audit. RESULTS: Of 191 total admissions, 134 (70.2%) admissions were completed using NeoTree, and 129 (67.5%) were exported and analyzed. Of 121 patients for whom outcome data were available, 102 (84.3%) were discharged alive. The overall case fatality rate was 93 per 1000 admitted babies. Prematurity with respiratory distress syndrome, birth asphyxia, and neonatal sepsis contributed to 25% (3/12), 58% (7/12), and 8% (1/12) of deaths, respectively. Data were more than 90% complete for all fields. Deaths may have been underreported because of phased implementation and some families of babies with imminent deaths self-discharging home. Detailed characterization of the data enabled departmental discussion of modifiable factors for quality improvement, for example, improved thermoregulation of infants. CONCLUSIONS: This digital outcome audit demonstrates that data can be captured digitally at the bedside by HCPs in underresourced newborn facilities, and these data can contribute to a meaningful review of the quality of care, outcomes, and potential modifiable factors. Coverage may be improved during future implementation by streamlining the admission process to be solely via digital format. Our results present a new methodology for newborn audits in low-resource settings and are a proof of concept for a novel newborn data system in these settings.

The NeoTree application: developing an integrated mHealth solution to improve quality of newborn care and survival in a district hospital in Malawi.

More than two-thirds of newborn lives could be saved worldwide if evidence-based interventions were successfully implemented. We developed the NeoTree application to improve quality of newborn care in resource-poor countries. The NeoTree is a fully integrated digital health intervention that combines immediate data capture, entered by healthcare workers (HCW) on admission, while simultaneously providing them with evidence-based clinical decision support and newborn care education. We conducted a mixed-methods intervention development study, codeveloping and testing the NeoTree prototype with HCWs in a district hospital in Malawi. Focus groups explored the acceptability and feasibility of digital health solutions before and after implementation of the NeoTree in the clinical setting. One-to-one theoretical usability workshops and a 1-month clinical usability study informed iterative changes, gathered process and clinical data, System Usability Scale (SUS) and perceived improvements in quality of care. HCWs perceived the NeoTree to be acceptable and feasible. Mean SUS before and after the clinical usability study were high at 80.4 and 86.1, respectively (above average is >68). HCWs reported high-perceived improvements in quality of newborn care after using the NeoTree on the ward. They described improved confidence in clinical decision-making, clinical skills, critical thinking and standardisation of care. Identified factors for successful implementation included a technical support worker. Coproduction, mixed-methods approaches and user-focused iterative development were key to the development of the NeoTree prototype, which was shown to be an agile, acceptable, feasible and highly usable tool with the potential to improve the quality of newborn care in resource-poor settings.

Evaluation of a bystander education program.

Sexual and partner violence are widespread problems on college campuses. By changing attitudes, beliefs, and behavior, bystander education programs have been found to prevent sexual and partner violence and improve the responses of peers to survivors. The purpose of this study is to evaluate the effectiveness and feasibility of a bystander education program that was adapted to a specific university setting. A convenience sample of 202, full-time undergraduate students aged 18-22 years participated in the bystander education program and completed pre- and post-test measures of attitudes related to sexual and partner violence and willingness to help. Paired sample t-tests were used to examine changes in scores between pre- and post-test conditions. After the program, participants' reported decreased rape myth acceptance and denial of interpersonal violence, and increased intention to act as a bystander and an increased sense of responsibility to intervene. Mental health nurses can use principles of bystander education in violence prevention programs and in providing support to survivors.