Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in south Asia and sub-Saharan Africa

Authors

Sunil Sazawal, Center for Public Health Kinetics, Lajpat Nagar II, New Delhi, India
Kelli K. Ryckman, University of Iowa, N. Riverside Dr., Iowa City, USA.
Sayan Das, Center for Public Health Kinetics, Lajpat Nagar II, New Delhi, India
Muhammad Imran Nisar, Aga Khan UniversityFollow
Usma Mehmood, Aga Khan UniversityFollow
Amina Barkat, Aga Khan UniversityFollow
Farah Khalid, Aga Khan UniversityFollow
Muhammad Ilyas Muhammad Ilyas, Aga Khan University
Ambreen Nizar, Aga Khan UniversityFollow
Fyezah Jehan, Aga Khan UniversityFollow

Document Type

Article

Department

Paediatrics and Child Health

Abstract

Background: Babies born early and/or small for gestational age in Low and Middle-income countries (LMICs) contribute substantially to global neonatal and infant mortality. Tracking this metric is critical at a population level for informed policy, advocacy, resources allocation and program evaluation and at an individual level for targeted care. Early prenatal ultrasound examination is not available in these settings, gestational age (GA) is estimated using new-born assessment, last menstrual period (LMP) recalls and birth weight, which are unreliable. Algorithms in developed settings, using metabolic screen data, provided GA estimates within 1-2 weeks of ultrasonography-based GA. We sought to leverage machine learning algorithms to improve accuracy and applicability of this approach to LMICs settings.
Methods: This study uses data from AMANHI-ACT, a prospective pregnancy cohorts in Asia and Africa where early pregnancy ultrasonography estimated GA and birth weight are available and metabolite screening data in a subset of 1318 new-borns were also available. We utilized this opportunity to develop machine learning (ML) algorithms. Random Forest Regressor was used where data was randomly split into model-building and model-testing dataset. Mean absolute error (MAE) and root mean square error (RMSE) were used to evaluate performance. Bootstrap procedures were used to estimate confidence intervals (CI) for RMSE and MAE. For pre-term birth identification ROC analysis with bootstrap and exact estimation of CI for area under curve (AUC) were performed.
Results: Overall model estimated GA had MAE of 5.2 days (95% CI 4.6-6.8), which was similar to performance in SGA, MAE 5.3 days (95% CI 4.6-6.2). GA was correctly estimated to within 1 week for 85.21% (95% CI 72.31-94.65). For preterm birth classification, AUC in ROC analysis was 98.1% (95% CI 96.0-99.0; p < 0.001). This model performed better than Iowa regression, AUC Difference 14.4% (95% CI 5-23.7; p = 0.002).
Conclusions: Machine learning algorithms and models applied to metabolomic gestational age dating offer a ladder of opportunity for providing accurate population-level gestational age estimates in LMICs settings. These findings also point to an opportunity for investigation of region-specific models, more focused feasible analyte models, and broad untargeted metabolome investigation.

Comments

Pagination are not provided by the author/publisher

Publication (Name of Journal)

BMC Pregnancy and Childbirth

Recommended Citation

Sazawal, S., Ryckman, K. K., Das, S., Nisar, M. I., Mehmood, U., Barkat, A., Khalid, F., Muhammad Ilyas, M., Nizar, A., Jehan, F. (2021). Machine learning guided postnatal gestational age assessment using new-born screening metabolomic data in south Asia and sub-Saharan Africa. BMC Pregnancy and Childbirth, 21(1), 609.
Available at: https://ecommons.aku.edu/pakistan_fhs_mc_women_childhealth_paediatr/1068

Creative Commons License

This work is licensed under a Creative Commons Attribution 4.0 International License.