Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.

ABSTRACT:

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. Mode

SUBMITTER: Hawken S 

PROVIDER: S-EPMC9987787 | biostudies-literature | 2023

REPOSITORIES: biostudies-literature