Evidence map›Paper›PMID 36403017›Full record

ArticleBMC pregnancy and childbirth2022

Prediction of stillbirth low resource setting in Northern Uganda.

Silvia Awor, Rosemary Byanyima, Benard Abola, Paul Kiondo, Christopher Garimoi Orach, Jasper Ogwal-Okeng, Dan Kaye, Annettee Nakimuli

Abstract read
In one paragraph

Article in BMC pregnancy and childbirth, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

8 authors.

Silvia AworDepartment of Obstetrics and Gynecology, Faculty of Medicine Gulu University, Gulu, Uganda. s.awor@gu.ac.ug.ORCID http://orcid.org/0000-0002-9701-2264
Rosemary ByanyimaMulago National Referral Hospital, and Teaching Hospital for Makerere University, P.O.Box 7051, Kampala, Uganda.
Benard AbolaDepartment of Mathematics, Faculty of Science, Gulu University, P.O.Box 166, Gulu, Uganda.
Paul KiondoDepartment of Obstetrics and Gynaecology, Makerere University, P.O.Box 7062, Kampala, Uganda.
Christopher Garimoi OrachDepartment of Community Health, School of Public Health, College of Health Sciences Makerere University, P.O.Box 7062, Kampala, Uganda.
Jasper Ogwal-OkengDepartment of Pharmacology, School of Health Sciences, Lira University, P.O.Box 1035, Lira, Uganda.
Dan KayeDepartment of Obstetrics and Gynaecology, Makerere University, P.O.Box 7062, Kampala, Uganda.
Annettee NakimuliDepartment of Obstetrics and Gynaecology, Makerere University, P.O.Box 7062, Kampala, Uganda.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundWomen 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.

methodsProspective 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.

resultsThe 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.

conclusionRisk factors for stillbirth include history of abortion and bilateral end-diastolic notch, while haemoglobin of 9.5-12.1 g/dL is protective.

Indexed as

Abortion, SpontaneousStillbirthFemaleHumansLive BirthPregnancyProspective StudiesRisk FactorsUgandaAfricaPrediction modelsRisk factorsStillbirthUganda

Identifiers

PMID36403017
PMCPMC9675255

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.