Abstract:
Background 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.
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 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 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 clin‑
ical 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