• Login
    View Item 
    •   Mak IR Home
    • College of Health Sciences (CHS)
    • School of Medicine (Sch. of Med.)
    • School of Medicine (Sch. of Med.) Collections
    • View Item
    •   Mak IR Home
    • College of Health Sciences (CHS)
    • School of Medicine (Sch. of Med.)
    • School of Medicine (Sch. of Med.) Collections
    • View Item
    JavaScript is disabled for your browser. Some features of this site may not work without it.

    Prediction of stillbirth low resource setting in Northern Uganda

    Thumbnail
    View/Open
    Journal Article (BMJ 855) (838.3Kb)
    Date
    2022-11-19
    Author
    Awor, Silvia
    Byanyima, Rosemary
    Abola, Benard
    Kiondo, Paul
    Orach, Christopher Garimoi
    Ogwal‑Okeng, Jasper
    Kaye, Dan
    Nakimuli, Annettee
    Metadata
    Show full item record
    Abstract
    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.
    URI
    https://doi.org/10.1186/s12884-022-05198-6
    http://hdl.handle.net/10570/11117
    Collections
    • School of Medicine (Sch. of Med.) Collections

    DSpace 5.8 copyright © Makerere University 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV
     

     

    Browse

    All of Mak IRCommunities & CollectionsTitlesAuthorsBy AdvisorBy Issue DateSubjectsBy TypeThis CollectionTitlesAuthorsBy AdvisorBy Issue DateSubjectsBy Type

    My Account

    LoginRegister

    Statistics

    Most Popular ItemsStatistics by CountryMost Popular Authors

    DSpace 5.8 copyright © Makerere University 
    Contact Us | Send Feedback
    Theme by 
    Atmire NV