ArticleInternational journal of general medicine2021
Derivation and Validation of a Risk Score to Predict Mortality of Early Neonates at Neonatal Intensive Care Unit: The END in NICU Score.
Article in International journal of general medicine, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed, 6 citations in OpenAlex.
- Construction and evaluation of a neonatal septic shock prediction model based on multimodal data.Medicine · 2025Article
- 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.BMC pregnancy and childbirth · 2024Article
- Do Clinical Scores Score in Predicting Neonatal Mortality?Indian journal of pediatrics · 2023Article
- Development of a Nomogram for Clinical Risk Prediction of Preterm Neonate Death in Ethiopia.Frontiers in pediatrics · 2022Article
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Authors and funding
3 authors at 2 institutions in 2 countries.
Funding
No grant is acknowledged in the PubMed record.
Abstract
backgroundEarly neonatal death is death of infants in the first week of life. And 34% to 92% of neonatal deaths happen within 7 days of postnatal period. Thus, the early neonatal period is the most critical time for an infant, requiring different strategies to prevent mortality. Among strategies, deriving and implementing early warning scores is crucial to predict early neonatal mortality earlier upon hospital admission.
objectiveTo derive and validate a risk score to predict mortality of early neonates at Felege Hiwot Specialized Hospital neonatal intensive care unit, Bahir Dar, 2021.
methodsThe document review was conducted from February 24, to April 08, 2021, on all early neonates admitted to neonatal intensive care unit from January 1, 2018 to December 31, 2020. The total number of early neonates included in the derivation study was 1100. Data were collected by using checklists prepared on EpiCollect5 software. After exporting the data to R version 4.0.5 software, variables with (p < 0.25) from the simple binary regression were entered into a multiple logistic regression model, and significant variables (p < 0.05) were kept in the model. The discrimination and calibration were assessed. The model was internally validated using bootstrapping technique.
resultsAdmission weight, birth Apgar score, perinatal asphyxia, respiratory distress syndrome, mode of delivery, sepsis, and gestational age at birth remained in the final multiple logistic regression prediction model. The area under curve of receiver operating characteristic curve for early neonatal mortality score was 90.7%. The model retained excellent discrimination under internal validation. The sensitivity, specificity, and positive predictive value, negative predictive value of the model was 89.4%, 82.5%, 55.5%, and 96.9%, respectively.
conclusionThe derived score has an excellent discriminative ability and good prediction performance. This is an important tool for predicting early neonatal mortality in neonatal intensive care units at admission.
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