Time to death and predictors of mortality among early neonates admitted to neonatal intensive care unit of Addis Ababa public Hospitals, Ethiopia: Institutional-based prospective cohort study

INTRODUCTION

The largest risk of child mortality occurs within the first week after birth. Early neonatal mortality remains a global public health concern, especially in sub-Saharan African countries. More than 75% of neonatal death occurs within the first seven days of birth, but there are limited prospective follow- up studies to determine time to death, incidence and predictors of death in Ethiopia particularly in the study area. The study aimed to determine incidence and predictors of early neonatal mortality among neonates admitted to the neonatal intensive care unit of Addis Ababa public hospitals, Ethiopia 2021.

METHODS

Institutional prospective cohort study was conducted in four public hospitals found in Addis Ababa City, Ethiopia from June 7th, 2021 to July 13th, 2021. All early neonates consecutively admitted to the corresponding neonatal intensive care unit of selected hospitals were included in the study and followed until 7 days-old. Data were coded, cleaned, edited, and entered into Epi data version 3.1 and then exported to STATA software version 14.0 for analysis. The Kaplan Meier survival curve with log- rank test was used to compare survival time between groups. Moreover, both bi-variable and multivariable Cox proportional hazard regression model was used to identify the predictors of early neonatal mortality. All variables having P-value ≤0.2 in the bi-variable analysis model were further fitted to the multivariable model. The assumption of the model was checked graphically and using a global test. The goodness of fit of the model was performed using the Cox-Snell residual test and it was adequate.

RESULTS

A total of 391 early neonates with their mothers were involved in this study. The incidence rate among admitted early neonates was 33.25 per 1000 neonate day's observation [95% confidence interval (CI): 26.22, 42.17]. Being preterm birth [adjusted hazard ratio (AHR): 6.0 (95% CI 2.02, 17.50)], having low fifth minute Apgar score [AHR: 3.93 (95% CI; 1.5, 6.77)], low temperatures [AHR: 2.67 (95%CI; 1.41, 5.02)] and, resuscitating of early neonate [AHR: 2.80 (95% CI; 1.51,5.10)] were associated with increased hazard of early neonatal death. However, early neonatal crying at birth [AHR: 0.48 (95%CI; 0.26, 0.87)] was associated with reduced hazard of death.

CONCLUSIONS

Early neonatal mortality is high in Addis Ababa public Hospitals. Preterm birth, low five-minute Apgar score, hypothermia and crying at birth were found to be independent predictors of early neonatal death. Good care and attention to neonate with low Apgar scores, premature, and hypothermic neonates.

Admission Hypothermia and Factors Associated with Mortality among Admitted Hypothermic Preterm Neonates in Neonatal Intensive Care Units of Public Hospitals of Addis Ababa, Ethiopia

Background

Limited knowledge on the proportion of admission hypothermia and factors of death in hypothermic preterm neonates is hindering early and appropriate interventions in Ethiopia. Thus, studies on the proportion of admission hypothermia and factors of death in hypothermic preterm neonates are critical to enhancing preterm infants' survival.

Methods

Hospital-based cross-sectional study was conducted on 398 participants using the systematic sampling method from October 10, 2021, to December 15, 2022. A pretested data extraction tool was used to collect data. EpiData version 4.6 and STATA version 16 were used for data entry and analysis. Multivariable logistic regression analysis evaluated the association between independent and outcome variables with a 95% confidence interval (CI). Hosmer and Lemeshow test and variance inflation factor were assessed to check model fitness and collinearity, respectively. Pvalue < 0.05 was considered statistically significant.

Result

Of the 398 admitted preterm neonates, 331(83.2%) had hypothermia at admission. Factors that were significantly associated with mortality included outborn babies [Adjusted hazard ratio (AOR) = 2.18 : 95% CI (1.03-4.62)], GA less than 32 weeks [AOR = 6.64 : 95% CI (1.87-13.58)], weight less than 1500 gram [AOR = 7.91 : 95% CI (1.21-15.38)], thrombocytopenia [AOR = 3.36 : 95% CI (1.49-7.58)], and kangaroo mother care [AOR = 0.38 : 95% CI (0.16-0.88)].

Conclusion

The proportion of admission hypothermia was high. Outborn babies, birth weight less than 1500 gram, gestational age < 32 weeks, being thrombocytopenic, and lack of kangaroo mother care were identified as risk factors for hypothermic preterm neonatal mortality. Preterm labor prevention, improved inborn delivery, and kangaroo mother care may alleviate the high proportion of admission hypothermia and related mortalities in preterm neonates.

Early prediction of hypothermia in pediatric intensive care units using machine learning

Hypothermia is a life-threatening condition where the temperature of the body drops below 35°C and is a key source of concern in Intensive Care Units (ICUs). Early identification can help to nudge clinical management to initiate early interventions. Despite its importance, very few studies have focused on the early prediction of hypothermia. In this study, we aim to monitor and predict Hypothermia (30 min-4 h) ahead of its onset using machine learning (ML) models developed on physiological vitals and to prospectively validate the best performing model in the pediatric ICU. We developed and evaluated ML algorithms for the early prediction of hypothermia in a pediatric ICU. Sepsis advanced forecasting engine ICU Database (SafeICU) data resource is an in-house ICU source of data built in the Pediatric ICU at the All-India Institute of Medical Science (AIIMS), New Delhi. Each time-stamp at 1-min resolution was labeled for the presence of hypothermia to construct a retrospective cohort of pediatric patients in the SafeICU data resource. The training set consisted of windows of the length of 4.2 h with a lead time of 30 min-4 h from the onset of hypothermia. A set of 3,835 hand-engineered time-series features were calculated to capture physiological features from the time series. Features selection using the Boruta algorithm was performed to select the most important predictors of hypothermia. A battery of models such as gradient boosting machine, random forest, AdaBoost, and support vector machine (SVM) was evaluated utilizing five-fold test sets. The best-performing model was prospectively validated. A total of 148 patients with 193 ICU stays were eligible for the model development cohort. Of 3,939 features, 726 were statistically significant in the Boruta analysis for the prediction of Hypothermia. The gradient boosting model performed best with an Area Under the Receiver Operating Characteristic curve (AUROC) of 85% (SD = 1.6) and a precision of 59.2% (SD = 8.8) for a 30-min lead time before the onset of Hypothermia onset. As expected, the model showed a decline in model performance at higher lead times, such as AUROC of 77.2% (SD = 2.3) and precision of 41.34% (SD = 4.8) for 4 h ahead of Hypothermia onset. Our GBM(gradient boosting machine) model produced equal and superior results for the prospective validation, where an AUROC of 79.8% and a precision of 53% for a 30-min lead time before the onset of Hypothermia whereas an AUROC of 69.6% and a precision of 38.52% for a (30 min-4 h) lead time prospective validation of Hypothermia. Therefore, this work establishes a pipeline termed ThermoGnose for predicting hypothermia, a major complication in pediatric ICUs.

Neonatal Mortality and Associated Factors Among Neonates Admitted to the Neonatal Intensive Care Unit of Dil Chora Referral Hospital, Dire Dawa City, Ethiopia, 2021: A Facility-Based Study

BACKGROUND

Despite the incredible progress made in decreasing under-five mortality, neonatal mortality remains the main and slowly advancing contributor. Though all efforts were made to decline the death of a newborn, current information showed that newborn death is unaverted and not a fastened agenda. This study aimed to assess neonatal mortality and its determinants among neonates admitted at the Dil Chora Hospital in Dire Dawa City.

METHODS

Facility-based cross-sectional study design was employed. A total of 376 newborns were selected systematically from neonates admitted to the NICU of the Dil Chora Referral Hospital from June 1, 2017 to December 31, 2020. Data were collected from medical records using a standard extraction checklist. The data were entered into Epi-data version 3.1 and then exported into SPSS version 24 for statistical analysis. Bivariate and multivariate analyses were employed to identify the association between independent variables and neonatal death.

RESULT

The prevalence of neonatal death was 11.4% (95% CI: 9.44, 13.36). The majority of 37 (86.05%) of the neonates died within 7 days of life. The most common causes of admission included perinatal asphyxia (55.3%), hypoglycemia (21.5%), and hypothermia. Nearly half (40.4%) of the mothers of newborns experience index pregnancy complications, premature rupture of the membrane (AOR = 5.79, 95% CI: 2.08, 16.1), birth weight <2,500 g (AOR = 3.96, 95% CI: 1.56. 10.06), hypothermia (AOR = 2.54, 95% CI: 1.1, 6.02), index pregnancy complications (AOR = 4.79, 95% CI: 1.92, 11.91), and induced labor (AOR = 4.45, 95% CI: 1.53, 12.94), which were significantly associated with neonatal mortality.

CONCLUSION

The prevalence of neonatal mortality was high compared with the national target. Premature rupture of the membrane, birth weight <2,500 g, hypothermia, index pregnancy complications, and induced labor were significantly associated with neonatal mortality. The majority of neonatal deaths are due to complications arising from pregnancy, labor, and delivery, and lack of quality of care at the neonatal intensive care unit. Cultivating and increasing the utilization of antenatal care services, quality of care at delivery, and the neonatal intensive care unit could avert those deaths.