Alemayehu Digssie Gebremariam1, Sofonyas Abebaw Tiruneh2, Melaku Tadege Engidaw1, Desalegn Tesfa3, Melkalem Mamuye Azanaw2, Getachew Yideg Yitbarek4, Getnet Asmare5. 1. Department of Public Health (Human Nutrition), College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia. 2. Department of Public Health (Epidemiology), College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia. 3. Department of Public Health (Reproductive Health), College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia. 4. Department of Biomedical Science (Medical Physiology), College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia. 5. Department of Pediatrics and Child Health Nursing, College of Health Sciences, Debre Tabor University, Debre Tabor, Ethiopia.
Abstract
BACKGROUND: 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.
BACKGROUND: 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.
Authors: Farhad A Khan; Luke C Mullany; Lee F-S Wu; Hasmot Ali; Saijuddin Shaikh; Kelsey Alland; Keith P West; Alain B Labrique Journal: BMJ Glob Health Date: 2020-01-27
Authors: Richard D Riley; Jill A Hayden; Ewout W Steyerberg; Karel G M Moons; Keith Abrams; Panayiotis A Kyzas; Núria Malats; Andrew Briggs; Sara Schroter; Douglas G Altman; Harry Hemingway Journal: PLoS Med Date: 2013-02-05 Impact factor: 11.069
Authors: Melissa M Medvedev; Helen Brotherton; Abdou Gai; Cally Tann; Christopher Gale; Peter Waiswa; Diana Elbourne; Joy E Lawn; Elizabeth Allen Journal: Lancet Child Adolesc Health Date: 2020-02-28