Development and Validation of a Clinical Prognostic Risk Score to Predict Early Neonatal Mortality, Ethiopia: A Receiver Operating Characteristic Curve Analysis

Abstract

Alemayehu Digssie Gebremariam,1 Sofonyas Abebaw Tiruneh,2 Melaku Tadege Engidaw,1 Desalegn Tesfa,3 Melkalem Mamuye Azanaw,2 Getachew Yideg Yitbarek,4 Getnet Asmare5 1Department of Public Health (Human Nutrition), College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia; 2Department of Public Health (Epidemiology), College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia; 3Department of Public Health (Reproductive Health), College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia; 4Department of Biomedical Science (Medical Physiology), College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia; 5Department of Pediatrics and Child Health Nursing, College of Health Sciences, Debre Tabor University, Debre Tabor, EthiopiaCorrespondence: Sofonyas Abebaw Tiruneh Email [email protected]: Early neonatal death is the death of a live-born baby within the first seven days of life, which is 73% of all postnatal deaths in the globe. This study aimed to develop and validate a prognostic clinical risk tool for the prediction of early neonatal death.Methods: A prospective follow-up study was conducted among 393 neonates at Debre Tabor Referral hospital, Northwest Ethiopia. Multivariable logistic regression model was employed to identify potential prognostic determinants for early neonatal mortality. Area under receiver operating characteristics curve (AUROC) was used to check the model discrimination probability using ‘pROC’ R-package. Model calibration plot was checked using ‘givitiR’ R-package. Finally, a risk score prediction tool was developed for ease of applicability. Decision curve analysis was done for cost-benefit analysis and to check the clinical impact of the model.Results: Overall, 15.27% (95% CI: 12.03– 19.18) of neonates had the event of death during the follow-up period. Maternal undernutrition, antenatal follow-up less than four times, birth asphyxia, low birth weight, and not exclusive breastfeeding were the prognostic predictors of early neonatal mortality. The AUROC for the reduced model was 88.7% (95% CI: 83.8– 93.6%), which had good discriminative probability. The AUROC of the simplified risk score algorithm was 87.8% (95% CI, 82.7– 92.9%). The sensitivity and specificity of the risk score tool was 70% and 89%, respectively. The true prediction accuracy of the risk score tool to predict early neonatal mortality was 86%, and the false prediction probability was 13%.Conclusion: We developed an early neonatal death prediction tool using easily available maternal and neonatal characteristics for resource-limited settings. This risk prediction using risk score is an easily applicable tool to identify neonates at a higher risk of having early neonatal mortality. This risk score tool would offer an opportunity to reduce early neonatal mortality, thus improving the overall early neonatal death in a resource-limited setting.Keywords: prediction model, risk score, neonate, decision curve, Ethiopia

Keywords

• prediction model
• risk score
• neonate
• decision curve
• ethiopia