Heel and cord blood datasets for Bangladesh and Zambia cohorts

Background: Accurate estimates of gestational age (GA) at birth are
important for preterm birth surveillance but can be challenging to obtain
in low-income countries. Our objective was to develop machine learning
models to accurately estimate GA shortly after birth using clinical and
metabolomic data. Methods: We derived three GA estimation models using
ELASTIC NET multivariable linear regression using metabolomic markers from
heel-prick blood samples and clinical data from a retrospective cohort of
newborns from Ontario, Canada. We conducted internal model validation in
an independent cohort of Ontario newborns, and external validation in heel
prick and cord blood sample data collected from newborns from prospective
birth cohorts in Lusaka, Zambia, and Matlab, Bangladesh. Model performance
was measured by comparing model-derived estimates of GA to reference
estimates from early pregnancy ultrasound. Results: Samples were collected
from 311 newborns from Zambia and 1176 from Bangladesh. The
best-performing model accurately estimated GA within about 6 days of
ultrasound estimates in both cohorts when applied to heel prick data (MAE
0.79 weeks (95% CI 0.69, 0.90) for Zambia; 0.81 weeks (0.75, 0.86) for
Bangladesh), and within about 7 days when applied to cord blood data (1.02
weeks (0.90, 1.15) for Zambia; 0.95 weeks (0.90, 0.99) for Bangladesh).
Conclusions: Algorithms developed in Canada provided accurate estimates of
GA when applied to external cohorts from Zambia and Bangladesh. Model
performance was superior in heel prick data as compared to cord blood
data.