TY - JOUR
T1 - Building a predictive model of low birth weight in low- and middle-income countries
T2 - a prospective cohort study
AU - Patterson, Jackie K.
AU - Thorsten, Vanessa R.
AU - Eggleston, Barry
AU - Nolen, Tracy
AU - Lokangaka, Adrien
AU - Tshefu, Antoinette
AU - Goudar, Shivaprasad S.
AU - Derman, Richard J.
AU - Chomba, Elwyn
AU - Carlo, Waldemar A.
AU - Mazariegos, Manolo
AU - Krebs, Nancy F.
AU - Saleem, Sarah
AU - Goldenberg, Robert L.
AU - Patel, Archana
AU - Hibberd, Patricia L.
AU - Esamai, Fabian
AU - Liechty, Edward A.
AU - Haque, Rashidul
AU - Petri, Bill
AU - Koso-Thomas, Marion
AU - McClure, Elizabeth M.
AU - Bose, Carl L.
AU - Bauserman, Melissa
N1 - Publisher Copyright:
© 2023, BioMed Central Ltd., part of Springer Nature.
PY - 2023/12
Y1 - 2023/12
N2 - 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 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.
AB - 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 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.
KW - Low birth weight
KW - Low-income country
KW - Preterm
KW - Small for gestational age
UR - http://www.scopus.com/inward/record.url?scp=85168684814&partnerID=8YFLogxK
U2 - 10.1186/s12884-023-05866-1
DO - 10.1186/s12884-023-05866-1
M3 - Article
C2 - 37608358
AN - SCOPUS:85168684814
SN - 1471-2393
VL - 23
JO - BMC Pregnancy and Childbirth
JF - BMC Pregnancy and Childbirth
IS - 1
M1 - 600
ER -