ArticleEClinicalMedicine2022
Development of a clinical prediction model for perinatal deaths in low resource settings.
Article in EClinicalMedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Prediction of low 5-minute Apgar scores: development and internal validation of parity-stratified clinical prediction models for sub-Saharan Africa.BMC pregnancy and childbirth · 2026Article
- Low birth weight risk prediction model: a prognostic study in the Birhan field site in Ethiopia.Journal of global health · 2025Article
- Development and validation of a prognosis risk score model for neonatal mortality in the Amhara region, Ethiopia. A prospective cohort study.Global health action · 2024Article
- Non-Invasive Ventilation Failure in Pediatric ICU: A Machine Learning Driven Prediction.Diagnostics (Basel, Switzerland) · 2024Article
- Fetal movement trials: Where is the evidence in settings with a high burden of stillbirths?BJOG : an international journal of obstetrics and gynaecology · 2023Article
- Maternal perception of fetal movements: Views, knowledge and practices of women and health providers in a low-resource setting.PLOS global public health · 2023Article
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11 authors.
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Abstract
backgroundMost pregnancy-related deaths in low and middle income countries occur around the time of birth and are avoidable with timely care. This study aimed to develop a prognostic model to identify women at risk of intrapartum-related perinatal deaths in low-resourced settings, by (1) external validation of an existing prediction model, and subsequently (2) development of a novel model.
methodsA prospective cohort study was conducted among pregnant women who presented consecutively for delivery at the maternity unit of Zanzibar's tertiary hospital, Mnazi Mmoja Hospital, the Republic of Tanzania between October 2017 and May 2018. Candidate predictors of perinatal deaths included maternal and foetal characteristics obtained from routine history and physical examination at the time of admission to the labour ward. The outcomes were intrapartum stillbirths and neonatal death before hospital discharge. An existing stillbirth prediction model with six predictors from Nigeria was applied to the Zanzibar cohort to assess its discrimination and calibration performance. Subsequently, a new prediction model was developed using multivariable logistic regression. Model performance was evaluated through internal validation and corrected for overfitting using bootstrapping methods.
findings5747 mother-baby pairs were analysed. The existing model showed poor discrimination performance (c-statistic 0·57). The new model included 15 clinical predictors and showed promising discriminative and calibration performance after internal validation (optimism adjusted c-statistic of 0·78, optimism adjusted calibration slope =0·94).
interpretationThe new model consisted of predictors easily obtained through history-taking and physical examination at the time of admission to the labour ward. It had good performance in predicting risk of perinatal death in women admitted in labour wards. Therefore, it has the potential to assist skilled birth attendance to triage women for appropriate management during labour. Before routine implementation, external validation and usefulness should be determined in future studies.
fundingThe study received funding from Laerdal Foundation, Otto Kranendonk Fund and UMC Global Health Fellowship. TD acknowledges financial support from the Netherlands Organisation for Health Research and Development (grant 91617050).
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