Prediction of stillbirth low resource setting in Northern Uganda

• Main Authors: Silvia Awor (Author), Rosemary Byanyima (Author), Benard Abola (Author), Paul Kiondo (Author), Christopher Garimoi Orach (Author), Jasper Ogwal-Okeng (Author), Dan Kaye (Author), Annettee Nakimuli (Author)
• Format: Book
• Published: BMC, 2022-11-01T00:00:00Z.
• Subjects: article
• Online Access: Connect to this object online.
• Notes: 10.1186/s12884-022-05198-6; 1471-2393
• Keywords: Stillbirth; Risk factors; Prediction models; Uganda; Africa; Gynecology and obstetrics; RG1-991
• Source: BMC Pregnancy and Childbirth, Vol 22, Iss 1, Pp 1-9 (2022)

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.