A multicenter study of incidence, risk factors and outcomes of babies with birth asphyxia in Nepal

A hybrid approach to skill retention following neonatal resuscitation training: Assessing effectiveness

 Perinatal death, a global health problem, can be prevented with simple resuscitation interventions that help the baby breathe immediately at birth. Latter-day Saint Charities (LDSC) and Safa Sunaulo Nepal (SSN) implemented a program to scale-up Helping Babies Breathe (HBB) training in Karnali Province, Nepal from January 2020-February 2021. The interventions were implemented using a hybrid approach with on-site mentoring in the pre/post COVID period combined with remote support and monitoring during the COVID period. This paper reports overall changes in newborn outcomes in relation to the unique implementation approach used. A prospective cohort design was used to compare outcomes of birth cohorts in 16 public health facilities in the first and last three months of program implementation. Results showed significant decreases in intrapartum stillbirths (23%), and neonatal deaths within (27%) and after (41.3%) 24 hours of life. The scale-up of HBB training resulted in 557 providers receiving training and mentoring support during the program period, half trained during the COVID period. Increased practice sessions, review meetings and debriefing meetings were reported during the COVID period compared to pre/post COVID period. The evaluation is suggestive of the potential of a hybrid approach for improved perinatal outcomes and scaling-up of newborn resuscitation trainings in health system facing disruptions.

A comparative proteomic analysis for non-invasive early prediction of hypoxic-ischemic injury in asphyxiated neonates

AIM

Hypoxic Ischemic Encephalopathy (HIE) is one of the principal causes of neonatal mortality and long-term morbidity worldwide. The neonatal signs of mild cerebral injury are subtle, making an early precise diagnosis difficult. Delayed detection, poor prognosis, and lack of specific biomarkers for the disease are increasing mortality rates. In this study, we intended to identify specific biomarkers using comparative proteomic analysis to predict the severity of perinatal asphyxia so that its outcome can also be prevented.

EXPERIMENTAL DESIGN

A case-control study was conducted on 38 neonates, and urine samples were collected within 24 and 72 h of life. A tandem mass spectrometry-based quantitative proteomics approach, followed by validation via sandwich ELISA, was performed.

RESULTS

The LC-MS/MS-based proteomics analysis resulted in the identification of 1201 proteins in urine, with 229, 244, and 426 being differentially expressed in HIE-1, HIE-2, and HIE-3, respectively. Axon guidance, Diseases of programmed cell death, and Detoxification of reactive oxygen species pathways were significantly enriched in mild HIE versus severe HIE. Among the differentially expressed proteins in various stages of HIE, we chose to validate four proteins - APP, AGT, FABP1, and FN1 - via sandwich ELISA. Individual and cumulative ROC curves were plotted. AGT and FABP1 together showed high sensitivity, specificity, and accuracy as potential biomarkers for early diagnosis of HIE.

CONCLUSION

Establishing putative urinary biomarkers will facilitate clinicians to more accurately screen neonates for brain injury and monitor the disease progression. Prompt treatment of neonates may reduce mortality and neurodevelopmental impairment.

Henriques T. Machine learning models based on clinical indices and cardiotocographic features for discriminating asphyxia fetuses—Porto retrospective intrapartum study

Introduction

Perinatal asphyxia is one of the most frequent causes of neonatal mortality, affecting approximately four million newborns worldwide each year and causing the death of one million individuals. One of the main reasons for these high incidences is the lack of consensual methods of early diagnosis for this pathology. Estimating risk-appropriate health care for mother and baby is essential for increasing the quality of the health care system. Thus, it is necessary to investigate models that improve the prediction of perinatal asphyxia. Access to the cardiotocographic signals (CTGs) in conjunction with various clinical parameters can be crucial for the development of a successful model.

Objectives

This exploratory work aims to develop predictive models of perinatal asphyxia based on clinical parameters and fetal heart rate (fHR) indices.

Methods

Single gestations data from a retrospective unicentric study from Centro Hospitalar e Universitário do Porto de São João (CHUSJ) between 2010 and 2018 was probed. The CTGs were acquired and analyzed by Omniview-SisPorto, estimating several fHR features. The clinical variables were obtained from the electronic clinical records stored by ObsCare. Entropy and compression characterized the complexity of the fHR time series. These variables' contribution to the prediction of asphyxia perinatal was probed by binary logistic regression (BLR) and Naive-Bayes (NB) models.

Results

The data consisted of 517 cases, with 15 pathological cases. The asphyxia prediction models showed promising results, with an area under the receiver operator characteristic curve (AUC) >70%. In NB approaches, the best models combined clinical and SisPorto features. The best model was the univariate BLR with the variable compression ratio scale 2 (CR2) and an AUC of 94.93% [94.55; 95.31%].

Conclusion

Both BLR and Bayesian models have advantages and disadvantages. The model with the best performance predicting perinatal asphyxia was the univariate BLR with the CR2 variable, demonstrating the importance of non-linear indices in perinatal asphyxia detection. Future studies should explore decision support systems to detect sepsis, including clinical and CTGs features (linear and non-linear).

Perinatal maternal characteristics predict a high risk of neonatal asphyxia: A multi-center retrospective cohort study in China

Objective

This study aimed to identify various perinatal maternal characteristics that contributed to neonatal asphyxia (NA) in term and late-preterm newborns based on the data obtained from a Chinese birth registry cohort and to establish an effective model for predicting a high risk of asphyxia.

Method

We retrospectively reviewed and analyzed the birth database from July 1, 2016, to June 30, 2017, in the main economically developed regions of China. Asphyxia was defined as an Apgar score <7 at 5 min post-delivery with umbilical cord arterial blood pH < 7.2 in the infant born after 34weeks. We compared the perinatal maternal characteristics of the newborns who developed asphyxia (NA group, n = 1,152) and those who did not (no NA group, n = 86,393). Candidate predictors of NA were analyzed using multivariable logistic regression. Subsequently, a prediction model was developed and validated by an independent test group.

Result

Of the maternal characteristics, duration of PROM ≥ 48 h, a gestational week at birth <37, prolonged duration of labor, hypertensive disorder, nuchal cord, and birth weight <2,500 or ≥4,000 g, abnormal fetal heart rate, meconium-stained amniotic fluid, and placenta previa were included in the predicting model, which presented a good performance in external validation (c-statistic of 0.731).

Conclusion

Our model relied heavily on clinical predictors that may be determined before or during birth, and pregnant women at high risk of NA might be recognized earlier in pregnancy and childbirth using this methodology, allowing them to avoid being neglected and delayed. Future studies should be conducted to assess its usefulness.