Association between time of delivery and poor perinatal outcomes -An evaluation of deliveries in a tertiary hospital, South-east Nigeria

The Effect of Birth Hour on Neonatal Morbidity and Mortality in Very-Low Birth Weight Infants in a Teaching Hospital

OBJECTIVE

With the increase in the preterm birth rate, the factors influencing mortality and morbidity in very-low-birth weight neonates have gained importance in recent years. The results of available studies on the influence of birth hour on the mortality and morbidity rates of preterm infants are contradictory. Moreover, no study on this topic has been conducted in our region.

MATERIALS AND METHODS

This retrospective cohort study was conducted on 127 very-low-birth weight newborns based on birth hour. The newborns were divided into 2 groups, the first born during working hours (7:00 am to 11:59 pm) and the second born during after-hours care (12-6:59 am). Mortality and major diseases were compared using Statistical Package for the Social Sciences by Fisher's exact tests, Pearson's chi-squared test, and independent t-tests. The statistical significance level for all analyses was set at P < .05 and the CI at 95%.

RESULTS

Based on the results of this study in terms of neonatal mortality and major morbidities such as intraventricular hemorrhage and the need for prolonged mechanical ventilation, no significant difference was found between the 2 groups, but the difference in appearance, pulse, grimace, activity, respiration (APGAR) score at the fifth minute was statistically significant and was lower at the working hours.

CONCLUSION

The results of the study may be due to appropriate allocation of resources, assignment of tasks, and professionalism of care in our study area. Further study is needed to determine the differences in clinical care processes that cause these results.

Office hours and caesarean section: systematic review and Meta-analysis

BACKGROUND

Unnecessary caesarean births may be affected by physician factors, such as preferences, incentives and convenience. Delivery during office hours can be a valuable proxy for measuring such effects.

OBJECTIVE

To determine the effect of office hours on the decision for caesarean delivery by assessing the odds of caesarean during office hours compared to out-of-office hours.

SEARCH STRATEGY

We searched CINAHL, ClinicalTrials.gov , The Cochrane Library, PubMed, Scopus and Web of Science from the beginning of records through August 2021.

DATA COLLECTION AND ANALYSIS

Search results were screened by three researchers. First, we selected studies that reported odds ratios of caesareans, or data allowing their calculation, for office and out-of-office hours. We extracted data on the study population, study design, data sources, setting, type of caesarean section, statistical analysis, and outcome measures. For groups reporting the same outcome, we performed a standard inverse-variance random-effects meta-analysis, which enabled us to calculate the overall odds ratios for each group. For groups reporting varying outcomes, we performed descriptive analysis.

MAIN RESULTS

Meta-analysis of weekday vs weekend for any caesarean section showed higher odds of caesarean during weekdays in adjusted analysis 1.40 (95%CI 1.13, 1.72 from 1,952,691 births). A similar effect was observed in the weekday vs Sunday comparison (1.39, 95%CI 1.10, 1.75, 150,932 births). A lower effect was observed for emergency CS, with a slight increase in adjusted analysis (1.06, 95%CI 0.90, 1.26, 2,622,772 births) and a slightly higher increase in unadjusted analysis (1.15, 95%CI 1.03, 1.29, 12,591,485 births). Similar trends were observed in subgroup analyses and descriptive synthesis of studies examining other office hours outcomes.

CONCLUSIONS

Delivery during office hours is associated with higher odds for overall caesarean sections and little to no effect for emergency caesarean. Non-clinical factors associated with office hours may influence the decision to deliver by caesarean section. Further detailed investigation of the "office hours effect" in delivery care is necessary and could lead to improvements in care systems.

FUNDING

The authors received no direct funding for this study.

Distributed Big Data Analytics Method for the Early Prediction of the Neonatal 5-Minute Apgar Score before or during Birth and Ranking the Risk Factors from a National Dataset

One-minute and five-minute Apgar scores are good measures to assess the health status of newborns. A five-minute Apgar score can predict the risk of some disorders such as asphyxia, encephalopathy, cerebral palsy and ADHD. The early prediction of Apgar score before or during birth and ranking the risk factors can be helpful to manage and reduce the probability of birth producing low Apgar scores. Therefore, the main aim of this study is the early prediction of the neonate 5-min Apgar score before or during birth and ranking the risk factors for a big national dataset using big data analytics methods. In this study, a big dataset including 60 features describing birth cases registered in Iranian maternal and neonatal (IMAN) registry from 1 April 2016 to 1 January 2017 is collected. A distributed big data analytics method for the early prediction of neonate Apgar score and a distributed big data feature ranking method for ranking the predictors of neonate Apgar score are proposed in this study. The main aim of this study is to provide the ability to predict birth cases with low Apgar scores by analyzing the features that describe prenatal properties before or during birth. The top 14 features were identified in this study and used for training the classifiers. Our proposed stack ensemble outperforms the compared classifiers with an accuracy of 99.37 ± 1.06, precision of 99.37 ± 1.06, recall of 99.50 ± 0.61 and F-score of 99.41 ± 0.70 (for confidence interval of 95%) to predict low, moderate and high 5-min Apgar scores. Among the top predictors, fetal height around the baby’s head and fetal weight denote fetal growth status. Fetal growth restrictions can lead to low or moderate 5-min Apgar score. Moreover, hospital type and medical science university are healthcare system-related factors that can be managed via improving the quality of healthcare services all over the country.