Magnitude of preterm hospital neonatal mortality and associated factors in northern Ethiopia: a cross-sectional study

Nomogram to predict risk of neonatal mortality among preterm neonates admitted with sepsis at University of Gondar Comprehensive Specialized Hospital: risk prediction model development and validation

BACKGROUND

Mortality in premature neonates is a global public health problem. In developing countries, nearly 50% of preterm births ends with death. Sepsis is one of the major causes of death in preterm neonates. Risk prediction model for mortality in preterm septic neonates helps for directing the decision making process made by clinicians.

OBJECTIVE

We aimed to develop and validate nomogram for the prediction of neonatal mortality. Nomograms are tools which assist the clinical decision making process through early estimation of risks prompting early interventions.

METHODS

A three year retrospective follow up study was conducted at University of Gondar Comprehensive Specialized Hospital and a total of 603 preterm neonates with sepsis were included. Data was collected using KoboCollect and analyzed using STATA version 16 and R version 4.2.1. Lasso regression was used to select the most potent predictors and to minimize the problem of overfitting. Nomogram was developed using multivariable binary logistic regression analysis. Model performance was evaluated using discrimination and calibration. Internal model validation was done using bootstrapping. Net benefit of the nomogram was assessed through decision curve analysis (DCA) to assess the clinical relevance of the model.

RESULT

The nomogram was developed using nine predictors: gestational age, maternal history of premature rupture of membrane, hypoglycemia, respiratory distress syndrome, perinatal asphyxia, necrotizing enterocolitis, total bilirubin, platelet count and kangaroo-mother care. The model had discriminatory power of 96.7% (95% CI: 95.6, 97.9) and P-value of 0.165 in the calibration test before and after internal validation with brier score of 0.07. Based on the net benefit analysis the nomogram was found better than treat all and treat none conditions.

CONCLUSION

The developed nomogram can be used for individualized mortality risk prediction with excellent performance, better net benefit and have been found to be useful in clinical practice with contribution in preterm neonatal mortality reduction by giving better emphasis for those at high risk.

Predictors of time to death among preterm neonates admitted to neonatal intensive care units at public hospitals in southern Ethiopia: A cohort study

BACKGROUND

Although the survival of preterm neonates has improved, thanks to advanced and specialized neonatal intensive care, it remains the main reason for neonatal admission, death, and risk of lifelong complication. In this study, we assessed time to death and its predictors among preterm neonates admitted to neonatal intensive care units (NICU) at public hospitals in southern Ethiopia.

METHODS

A hospital based retrospective cohort was conducted among preterm neonates admitted to NICU at public hospitals in west Guji and Borena zones, Oromia National Regional State, southern Ethiopia. Simple random sampling technique was used to select records of preterm neonates admitted to both major hospitals in the study area. Data on neonatal condition, obstetric information, and status at discharge were collected from admission to discharge by trained research assistant through review of their medical records. Kaplan Meir curve and Log rank test were used to estimate the survival time and compare survival curves between variables. Cox-Proportional Hazards model was used to identify significant predictors of time to death at p<0.05.

RESULT

Of 510 neonates enrolled, 130(25.5%; 95% CI: 22-29) neonates died at discharge or 28days. The median survival time was 18 days with an interquartile range of (IQR = 6, 24). The overall incidence of neonatal mortality was 47.7 (95% CI: 40.2-56.7) per 1000 neonatal days. In the multivariable cox-proportional hazard analysis, lack of antenatal care (AHR: 7.1; 95%CI: 4-12.65), primipara (AHR: 2.3; 95% CI: 1.16-4.43), pregnancy complications (AHR: 3.4; 95% CI: 1.94-6.0), resuscitation at birth (AHR: 2.1, 95% CI: 0.28-0.77) and not receiving Kangaroo mother care (AHR: 9.3, 95% CI: 4.36-19.9) were predictors of preterm neonatal death.

CONCLUSION

Despite admission to NICU for advanced care and follow up, mortality of preterm neonates was found to be high in the study settings. Addressing major intrapartum complications is required to improve survival of neonates admitted to NICU.

Neonatal outcomes of preterm neonates and its predictors in Ethiopian public hospitals: Multicenter prospective follow-up study

Introduction

Preterm birth remains the most significant clinical and public health encounter. Preterm infant outcomes pose key evidence for clinicians and policymakers and are extensively used to set clinical and policy verdicts to improve services. It is necessary to conduct the outcomes of neonates frequently, as it varies from place to place and even from time to time in a similar place. There is limited literature in Ethiopia about preterm neonates' outcomes and their predictors.

Objective

This study aimed to assess the neonatal outcomes of preterm neonates and their predictors in South Gondar zone public hospitals, Northwest Ethiopia, 2021.

Methods

A prospective observational study was employed on 462 preterm neonates in South Gondar Zone Public Hospitals. The data were entered into Epidata 4.6 and analyzed using STATA version 16/MP software. A parametric log-normal survival model was used to identify possible predictors for preterm neonate death. Statistical significance was declared at a P-value less than 0.05.

Result

The overall preterm survival rate was 71.1% (95% CI: 66.7, 75.1). Thirty-six percent of preterm neonates were diagnosed with sepsis. One-fourth of the neonates had respiratory distress syndrome. Gestational age greater than 34 weeks (β = 1.04; 95% CI: 0.53, 1.56), respiratory distress syndrome (β = 0.85; 95% CI: 0.49, 1.22), body mass index (β = -1.34; 95% CI: -1.87, -0.80), non-union marital status (β = -0.71; 95% CI: -1.34, -0.09), multiple pregnancies (β = -0.66; 95% CI: -0.99-0.32), multiparous (β = 0.35; 95% CI: 0.01, 0.69), hypothermia (β = -1.19; 95% CI: -1.76, -0.62), Kangaroo Mother Care (β = -1.9; 95% CI: -2.34, -1.41) and non-cephalic presentation (β = -1.23; 95% CI: -1.99,-0.46) were significant predictors.

Conclusion

In this study, the preterm survival rate was low. Gestational age greater than 34 weeks, no respiratory distress syndrome, and multiparous mothers were positively associated with the survival of preterm neonates. Though, high pre-pregnancy maternal body mass index, non-union marital status of mothers, multiple pregnancies, hypothermia, Kangaroo mother care is not given, and non-cephalic presentation were negatively associated. A significant focus should be given to implementing WHO recommendations on preventing and caring for preterm births.

Determinants of early neonatal mortality (hospital based retrospective cohort study in Somali region of Ethiopia)

Early neonatal mortality occurs when a newborn dies within the first seven days of life. Despite interventions, neonatal mortality remains public health problem over time in Ethiopia (33 per 1000 live births). Determinants varies on level of neonatal mortality. The study's goal was to determine magnitude of early newborn death, as well as its determinants and causes in Newborn Intensive Care Unit of Referral hospital in Ethiopia's Somali region. Health facility based retrospective study review was conducted between May 2019 to May 2021 in Shiek Hassan Yabare Referral Hospital of Jigjiga University of Ethiopia. All neonates admitted at neonatal intensive care unit (NICU) with complete data and get registered using the new NICU registration book from May 2019 to May 2021 were included. Kobo toolkit was used for data collection and analyzed in SPSS 20. The magnitude of early neonatal mortality rate was defined as death between 0 and 7 days. Logistic regression model was used to estimate maternal and neonatal characteristics as a determinant variables on neonatal mortality. The statistical significance was considered at P-value < 0.05. The magnitude of early neonatal mortality rate of Ethiopia's Somali region is estimated to be 130 per 1000 live births-that is say 130 newborn couldn't celebrate their seventh day in every 1000 live births. Hypothermia, prematurity, maternal death at birth and shorter length of stay in NICU were increasing the chance of neonatal mortality at early stage while neonatal resuscitation had shown protective effect against neonatal mortality. Similarly birth asphyxia, preterm, sepsis, and congenital abnormalities were major causes of admission and death in the NICU. The magnitude of early neonatal mortality is considerable and most of the determinants are preventable. Enhancing quality of intra-partum and NICU care including infection prevention, managing hypothermia and neonatal resuscitation as per the national standard within the first golden hour is key.

Predictors of Survival Among Preterm Neonates Admitted to Felege Hiwot Comprehensive Specialized Hospital, Northwest Ethiopia

Background

Pre-maturity is the primary cause of neonatal mortality in the world. Although prematurity was the leading cause of neonatal mortality, the survival rate and its predictors may be varied from setting to setting and time to time due to different reasons. Therefore, this study aimed to assess the survival probability and predictors of mortality among preterm neonates at Felege Hiwot comprehensive specialized hospital.

Methods

This is a retrospective follow-up study that included 542 randomly selected preterm neonates admitted at Felege Hiwot comprehensive specialized hospital from the period of 2016-2020. Semi-parametric and parametric survival models were fitted to identify the survival probability of preterm neonates and its association with different predictors. The best fit model was selected using Akaike's information criteria, Bayesian information criteria and likelihood ratio criteria.

Results

The cumulative incidence and incidence rate of mortality among preterm neonates were 31 per 100 live births and 3.5 per 100 neonate days, respectively. From the adjusted cox-proportional-hazard model, predictors with higher preterm mortality risk include the presence of neonatal respiratory distress syndrome [AHR = 2.55, 95% CI: 1.23; 3.74], perinatal asphyxia [AHR = 4.26, 95% CI: 1.35; 6.79] and jaundice [AHR = 3.25, 95% CI: 2.14, 7.24]. However, admission weight of 1,500-2,499 g (AHR = 0.23, 95% CI: 0.11, 0.56) and ≥2,500 g (AHR = 0.12, 95% CI: 0.02; 0.32), early breastfeeding [AHR = 0.44, 95% CI: 0.36; 0.48] and kangaroo mother care [AHR = 0.11, 95% CI: 0.03; 0.15] were protective factors of preterm mortality.

Conclusion

The cumulative incidence of mortality among preterm neonates was consistent with the national incidence of preterm mortality. Factors such as respiratory distress syndrome, perinatal asphyxia, breastfeeding, kangaroo mother care, admission weight, and jaundice are significant predictors of survival. Therefore, considerable attention such as intensive phototherapy, optimal calorie feeding, oxygenation, and good thermal care should be given for admitted preterm neonates.