Kenya heel prick and cord blood sample data

Using data from Ontario Canada, we previously developed machine
learning-based algorithms incorporating newborn screening metabolites to
estimate gestational age (GA). The objective of this study was to evaluate
the use of these algorithms in a population of infants born in Siaya
county, Kenya.  Cord and heel prick samples were collected from
newborns in Kenya and metabolic analysis was carried out by Newborn
Screening Ontario in Ottawa, Canada. Postnatal GA estimation models were
developed with data from Ontario with multivariable linear regression
using ELASTIC NET regularization. Model performance was evaluated by
applying the models to the data collected from Kenya and comparing
model-derived estimates of GA to reference estimates from early pregnancy
ultrasound.  Heel prick samples were collected from 1,039
newborns from Kenya. Of these, 8.9% were born preterm and 8.5% were small
for GA. Cord blood samples were also collected from 1,012 newborns. In
data from heel prick samples, our best-performing model estimated GA
within 9.5 days overall of reference GA [mean absolute error (MAE) 1.35
(95% CI 1.27, 1.43)]. In preterm infants and those small for GA, MAE was
2.62 (2.28, 2.99) and 1.81 (1.57, 2.07) weeks, respectively. In data from
cord blood, model accuracy slightly decreased overall (MAE 1.44 (95% CI
1.36, 1.53)). Accuracy was not impacted by maternal HIV status and
improved when the dating ultrasound occurred between 9 and 13 weeks of
gestation, in both heel prick and cord blood data (overall MAE 1.04 (95%
CI 0.87, 1.22) and 1.08 (95% CI 0.90, 1.27), respectively). Compared to
internal validation performance using Ontario data and to our previously
published external validations, model performance was diminished in the
Kenya cohort, suggesting that reference ultrasound timing is an important
factor in model performance. Our study highlights the challenges in
reliably estimating GA in low resource settings, even those with access to
dating ultrasound, given that the timing of dating ultrasound is critical
to develop algorithms for accurate estimation of GA based on metabolic
analysis of blood obtained at birth.