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BMC Pregnancy and Childbirth
Published in: BMC Pregnancy and Childbirth 1/2024

Open Access 01-12-2024 | Septicemia | Research

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

Authors: Tigabu Kidie Tesfie, Degefaye Zelalem Anlay, Birhanu Abie, Yazachew Moges Chekol, Negalgn Byadgie Gelaw, Tsion Mulat Tebeje, Yaregal Animut

Published in: BMC Pregnancy and Childbirth | Issue 1/2024

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Abstract

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.
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Metadata
Title
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
Authors
Tigabu Kidie Tesfie
Degefaye Zelalem Anlay
Birhanu Abie
Yazachew Moges Chekol
Negalgn Byadgie Gelaw
Tsion Mulat Tebeje
Yaregal Animut
Publication date
01-12-2024
Publisher
BioMed Central
Published in
BMC Pregnancy and Childbirth / Issue 1/2024
Electronic ISSN: 1471-2393
DOI
https://doi.org/10.1186/s12884-024-06306-4

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