Derivation and Validation of A Risk Score To Predict Mortality of Early Neonates at Felege Hiwot Specialized Hospital Neonatal Intensive Care Unit, Bahir Dar

Yitayeh, Belsti

dc.contributor.author	Yitayeh, Belsti
dc.date.accessioned	2023-05-12T06:19:03Z
dc.date.available	2023-05-12T06:19:03Z
dc.date.issued	2021-06-12
dc.identifier.uri	http://ir.bdu.edu.et/handle/123456789/15254
dc.description.abstract	Background: Early neonatal death is death of infants in the first week of life. In 2019, 2.4 million newborns died globally, and 99, 000 live births died in Ethiopia. Of this death, 34%-92% of 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. However, no risk score has been derived in our country and the study area. Therefore, this study will help for screening high-risk early neonates at admission using easily measurable and accessible maternal and neonatal variables to estimate, and predict early neonatal death. Objectives: To derive and validate a risk score to predict mortality of early neonates at Felege Hiwot Specialized Hospital neonatal intensive care unit, Bahir Dar, 2021 Methods: The 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 structured 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. To make the score easily applicable the regression coefficients from the final multiple binary logistic regressions were used to assign integers to each variable. Results: Admission 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. Using the ―Youden Index‖ optimal cutoff point for predicted probabilities of mortality 0.1363, the sensitivity, specificity, and positive predictive value, negative predictive value was 89.4%, 82.5%, 55.5%, and 96.9%, respectively. The positive and negative likelihood ratios of the model were also 5.10 and 0.13, respectively. Conclusion and recommendation: The 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 just at admission. Therefore, after external validation, this score will be a better model for application in low and middle-income countries. Keywords: derivation, validation, risk score, early neonatal mortality, NICU, Ethiopia	en_US
dc.language.iso	en	en_US
dc.subject	Epidemiology and Biostatistics	en_US
dc.title	Derivation and Validation of A Risk Score To Predict Mortality of Early Neonates at Felege Hiwot Specialized Hospital Neonatal Intensive Care Unit, Bahir Dar	en_US
dc.type	Thesis	en_US