Background: A neonatal mortality prediction score can assist clinicians in making timely clinical decisions to save neonates' lives by facilitating earlier admissions where needed. It can also help reduce unnecessary admissions.
Objective: The study aimed to develop and validate a prognosis risk score for neonatal mortality within 28 days in public hospitals in the Amhara region, Ethiopia.
Methods: The model was developed using a validated neonatal near miss assessment scale and a prospective cohort of 365 near-miss neonates in six hospitals between July 2021 and January 2022. The model's accuracy was assessed using the area under the receiver operating characteristics curve, calibration belt, and the optimism statistic. Internal validation was performed using a 500-repeat bootstrapping technique. Decision curve analysis was used to evaluate the model's clinical utility.
Results: In total, 63 of the 365 neonates died, giving a neonatal mortality rate of 17.3% (95% CI: 13.7-21.5). Six potential predictors were identified and included in the model: anemia during pregnancy, pregnancy-induced hypertension, gestational age less than 37 weeks, birth asphyxia, 5 min Apgar score less than 7, and birth weight less than 2500 g. The model's AUC was 84.5% (95% CI: 78.8-90.2). The model's predictive ability while accounting for overfitting via internal validity was 82%. The decision curve analysis showed higher clinical utility performance.
Conclusion: The neonatal mortality predictive score could aid in early detection, clinical decision-making, and, most importantly, timely interventions for high-risk neonates, ultimately saving lives in Ethiopia.
Keywords: Ethiopia; Neonatal resuscitation; Sustainable Development Goals; birth asphyxia; clinical decisions; near miss; neonatal mortality; newborn; prediction; risk score.
Main findings: This prognosis risk score for neonatal mortality tested in Ethiopia had high performance accuracy and the decision curve analysis showed increased clinical utility performance.Added knowledge: The tool developed here can aid healthcare providers in identifying high-risk neonates and making timely clinical decisions to save lives.Global health impact for policy and action: The findings have the potential to be applied in local contexts to identify high-risk neonates and make treatment decisions that could improve child survival rates.