Building a predictive model of low birth weight in low- and middle-income countries: a prospective cohort study.

Pregnancy Child Infant Infant, Newborn Humans Female Child Health Developing Countries Prospective Studies Women's Health Mothers Infant, Low Birth Weight

Low birth weight Low-income country Preterm Small for gestational age

Journal

BMC pregnancy and childbirth

ISSN: 1471-2393

Titre abrégé: BMC Pregnancy Childbirth

Pays: England

ID NLM: 100967799

Informations de publication

Date de publication:
22 Aug 2023

Historique:

received: 13 02 2023

accepted: 21 07 2023

medline: 24 8 2023

pubmed: 23 8 2023

entrez: 23 8 2023

Statut: epublish

Résumé

Low birth weight (LBW, < 2500 g) infants are at significant risk for death and disability. Improving outcomes for LBW infants requires access to advanced neonatal care, which is a limited resource in low- and middle-income countries (LMICs). Predictive modeling might be useful in LMICs to identify mothers at high-risk of delivering a LBW infant to facilitate referral to centers capable of treating these infants. We developed predictive models for LBW using the NICHD Global Network for Women's and Children's Health Research Maternal and Newborn Health Registry. This registry enrolled pregnant women from research sites in the Democratic Republic of the Congo, Zambia, Kenya, Guatemala, India (2 sites: Belagavi, Nagpur), Pakistan, and Bangladesh between January 2017 - December 2020. We tested five predictive models: decision tree, random forest, logistic regression, K-nearest neighbor and support vector machine. We report a rate of LBW of 13.8% among the eight Global Network sites from 2017-2020, with a range of 3.8% (Kenya) and approximately 20% (in each Asian site). Of the five models tested, the logistic regression model performed best with an area under the curve of 0.72, an accuracy of 61% and a recall of 72%. All of the top performing models identified clinical site, maternal weight, hypertensive disorders, severe antepartum hemorrhage and antenatal care as key variables in predicting LBW. Predictive modeling can identify women at high risk for delivering a LBW infant with good sensitivity using clinical variables available prior to delivery in LMICs. Such modeling is the first step in the development of a clinical decision support tool to assist providers in decision-making regarding referral of these women prior to delivery. Consistent referral of women at high-risk for delivering a LBW infant could have extensive public health consequences in LMICs by directing limited resources for advanced neonatal care to the infants at highest risk.

Sections du résumé

BACKGROUND BACKGROUND

METHODS METHODS

We developed predictive models for LBW using the NICHD Global Network for Women's and Children's Health Research Maternal and Newborn Health Registry. This registry enrolled pregnant women from research sites in the Democratic Republic of the Congo, Zambia, Kenya, Guatemala, India (2 sites: Belagavi, Nagpur), Pakistan, and Bangladesh between January 2017 - December 2020. We tested five predictive models: decision tree, random forest, logistic regression, K-nearest neighbor and support vector machine.

RESULTS RESULTS

We report a rate of LBW of 13.8% among the eight Global Network sites from 2017-2020, with a range of 3.8% (Kenya) and approximately 20% (in each Asian site). Of the five models tested, the logistic regression model performed best with an area under the curve of 0.72, an accuracy of 61% and a recall of 72%. All of the top performing models identified clinical site, maternal weight, hypertensive disorders, severe antepartum hemorrhage and antenatal care as key variables in predicting LBW.

CONCLUSIONS CONCLUSIONS

Predictive modeling can identify women at high risk for delivering a LBW infant with good sensitivity using clinical variables available prior to delivery in LMICs. Such modeling is the first step in the development of a clinical decision support tool to assist providers in decision-making regarding referral of these women prior to delivery. Consistent referral of women at high-risk for delivering a LBW infant could have extensive public health consequences in LMICs by directing limited resources for advanced neonatal care to the infants at highest risk.

Identifiants

DOI: 10.1186/s12884-023-05866-1 PMID: 37608358 PMC: PMC10464177

pubmed: 37608358

doi: 10.1186/s12884-023-05866-1

pii: 10.1186/s12884-023-05866-1

pmc: PMC10464177

doi:

Types de publication

Journal Article

Langues

eng

Sous-ensembles de citation

Pagination

600

Subventions

Organisme : NICHD NIH HHS

ID : UG1 HD076465

Pays : United States

Organisme : NICHD NIH HHS

ID : UG1 HD078439

Pays : United States

Informations de copyright

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