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Prediction of stillbirth low resource setting in Northern Uganda

BACKGROUND: Women of Afro-Caribbean and Asian origin are more at risk of stillbirths. However, there are limited tools built for risk-prediction models for stillbirth within sub-Saharan Africa. Therefore, we examined the predictors for stillbirth in low resource setting in Northern Uganda. METHODS:...

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Autores principales: Awor, Silvia, Byanyima, Rosemary, Abola, Benard, Kiondo, Paul, Orach, Christopher Garimoi, Ogwal-Okeng, Jasper, Kaye, Dan, Nakimuli, Annettee
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675255/
https://www.ncbi.nlm.nih.gov/pubmed/36403017
http://dx.doi.org/10.1186/s12884-022-05198-6
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author Awor, Silvia
Byanyima, Rosemary
Abola, Benard
Kiondo, Paul
Orach, Christopher Garimoi
Ogwal-Okeng, Jasper
Kaye, Dan
Nakimuli, Annettee
author_facet Awor, Silvia
Byanyima, Rosemary
Abola, Benard
Kiondo, Paul
Orach, Christopher Garimoi
Ogwal-Okeng, Jasper
Kaye, Dan
Nakimuli, Annettee
author_sort Awor, Silvia
collection PubMed
description BACKGROUND: Women of Afro-Caribbean and Asian origin are more at risk of stillbirths. However, there are limited tools built for risk-prediction models for stillbirth within sub-Saharan Africa. Therefore, we examined the predictors for stillbirth in low resource setting in Northern Uganda. METHODS: Prospective cohort study at St. Mary’s hospital Lacor in Northern Uganda. Using Yamane’s 1967 formula for calculating sample size for cohort studies using finite population size, the required sample size was 379 mothers. We doubled the number (to > 758) to cater for loss to follow up, miscarriages, and clients opting out of the study during the follow-up period. Recruited 1,285 pregnant mothers at 16–24 weeks, excluded those with lethal congenital anomalies diagnosed on ultrasound. Their history, physical findings, blood tests and uterine artery Doppler indices were taken, and the mothers were encouraged to continue with routine prenatal care until the time for delivery. While in the delivery ward, they were followed up in labour until delivery by the research team. The primary outcome was stillbirth 24 + weeks with no signs of life. Built models in RStudio. Since the data was imbalanced with low stillbirth rate, used ROSE package to over-sample stillbirths and under-sample live-births to balance the data. We cross-validated the models with the ROSE-derived data using K (10)-fold cross-validation and obtained the area under curve (AUC) with accuracy, sensitivity and specificity. RESULTS: The incidence of stillbirth was 2.5%. Predictors of stillbirth were history of abortion (aOR = 3.07, 95% CI 1.11—8.05, p = 0.0243), bilateral end-diastolic notch (aOR = 3.51, 95% CI 1.13—9.92, p = 0.0209), personal history of preeclampsia (aOR = 5.18, 95% CI 0.60—30.66, p = 0.0916), and haemoglobin 9.5 – 12.1 g/dL (aOR = 0.33, 95% CI 0.11—0.93, p = 0.0375). The models’ AUC was 75.0% with 68.1% accuracy, 69.1% sensitivity and 67.1% specificity. CONCLUSION: Risk factors for stillbirth include history of abortion and bilateral end-diastolic notch, while haemoglobin of 9.5—12.1 g/dL is protective.
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spelling pubmed-96752552022-11-20 Prediction of stillbirth low resource setting in Northern Uganda Awor, Silvia Byanyima, Rosemary Abola, Benard Kiondo, Paul Orach, Christopher Garimoi Ogwal-Okeng, Jasper Kaye, Dan Nakimuli, Annettee BMC Pregnancy Childbirth Research BACKGROUND: Women of Afro-Caribbean and Asian origin are more at risk of stillbirths. However, there are limited tools built for risk-prediction models for stillbirth within sub-Saharan Africa. Therefore, we examined the predictors for stillbirth in low resource setting in Northern Uganda. METHODS: Prospective cohort study at St. Mary’s hospital Lacor in Northern Uganda. Using Yamane’s 1967 formula for calculating sample size for cohort studies using finite population size, the required sample size was 379 mothers. We doubled the number (to > 758) to cater for loss to follow up, miscarriages, and clients opting out of the study during the follow-up period. Recruited 1,285 pregnant mothers at 16–24 weeks, excluded those with lethal congenital anomalies diagnosed on ultrasound. Their history, physical findings, blood tests and uterine artery Doppler indices were taken, and the mothers were encouraged to continue with routine prenatal care until the time for delivery. While in the delivery ward, they were followed up in labour until delivery by the research team. The primary outcome was stillbirth 24 + weeks with no signs of life. Built models in RStudio. Since the data was imbalanced with low stillbirth rate, used ROSE package to over-sample stillbirths and under-sample live-births to balance the data. We cross-validated the models with the ROSE-derived data using K (10)-fold cross-validation and obtained the area under curve (AUC) with accuracy, sensitivity and specificity. RESULTS: The incidence of stillbirth was 2.5%. Predictors of stillbirth were history of abortion (aOR = 3.07, 95% CI 1.11—8.05, p = 0.0243), bilateral end-diastolic notch (aOR = 3.51, 95% CI 1.13—9.92, p = 0.0209), personal history of preeclampsia (aOR = 5.18, 95% CI 0.60—30.66, p = 0.0916), and haemoglobin 9.5 – 12.1 g/dL (aOR = 0.33, 95% CI 0.11—0.93, p = 0.0375). The models’ AUC was 75.0% with 68.1% accuracy, 69.1% sensitivity and 67.1% specificity. CONCLUSION: Risk factors for stillbirth include history of abortion and bilateral end-diastolic notch, while haemoglobin of 9.5—12.1 g/dL is protective. BioMed Central 2022-11-19 /pmc/articles/PMC9675255/ /pubmed/36403017 http://dx.doi.org/10.1186/s12884-022-05198-6 Text en © The Author(s) 2022 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data.
spellingShingle Research
Awor, Silvia
Byanyima, Rosemary
Abola, Benard
Kiondo, Paul
Orach, Christopher Garimoi
Ogwal-Okeng, Jasper
Kaye, Dan
Nakimuli, Annettee
Prediction of stillbirth low resource setting in Northern Uganda
title Prediction of stillbirth low resource setting in Northern Uganda
title_full Prediction of stillbirth low resource setting in Northern Uganda
title_fullStr Prediction of stillbirth low resource setting in Northern Uganda
title_full_unstemmed Prediction of stillbirth low resource setting in Northern Uganda
title_short Prediction of stillbirth low resource setting in Northern Uganda
title_sort prediction of stillbirth low resource setting in northern uganda
topic Research
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9675255/
https://www.ncbi.nlm.nih.gov/pubmed/36403017
http://dx.doi.org/10.1186/s12884-022-05198-6
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