External validation of machine learning models including newborn metabolomic markers for postnatal gestational age estimation in East and South-East Asian infants

Gates Open Research

article

Steven Hawken¹  Malia S. Q. Murphy¹  Robin Ducharme¹  A. Brianne Bota¹  Lindsay A. Wilson¹  Wei Cheng¹  Ma-Am Joy Tumulak³  Maria Melanie Liberty Alcausin³  Ma Elouisa Reyes³  Wenjuan Qiu⁴  Beth K. Potter²  Julian Little²  Mark Walker¹  Lin Zhang⁶  Carmencita Padilla⁸  Pranesh Chakraborty^1,10  Kumanan Wilson¹ 

[1] Clinical Epidemiology Program, Ottawa Hospital Research Institute;School of Epidemiology and Public Health, University of Ottawa;Newborn Screening Reference Centre, University of the Philippines Manila;Pediatric Endocrinology and Genetic Metabolism, XinHua Hospital;Better Outcomes Registry & Network;Department of Gynecology and Obsetrics, XinHua Hospital;MOE-Shanghai Key Lab of Children’s Environmental Health, Xinhua Hospital, Shanghai Jiao Tong University School of Medicine;Department of Pediatrics, University of the Philippines Manila;Institute of Human Genetics, National Institutes of Health, University of Philippines Manila;Newborn Screening Ontario, Children's Hospital of Eastern Ontario;Faculty of Medicine, University of Ottawa;Department of Medicine, University of Ottowa;Bruyère Research Institute

关键词: biological modelling;    gestational age;    preterm birth;    newborn screening;   

DOI  :  10.12688/gatesopenres.13131.2

学科分类：电子与电气工程

来源: American Journal Of Pharmtech Research

【 摘 要 】

Background: Postnatal gestational age (GA) algorithms derived from newborn metabolic profiles have emerged as a novel method of acquiring population-level preterm birth estimates in low resource settings. To date, model development and validation have been carried out in North American settings. Validation outside of these settings is warranted.   Methods: This was a retrospective database study using data from newborn screening programs in Canada, the Philippines and China. ELASTICNET machine learning models were developed to estimate GA in a cohort of infants from Canada using sex, birth weight and metabolomic markers from newborn heel prick blood samples. Final models were internally validated in an independent sample of Canadian infants, and externally validated in infant cohorts from the Philippines and China.  Results: Cohorts included 39,666 infants from Canada, 82,909 from the Philippines and 4,448 from China.  For the full model including sex, birth weight and metabolomic markers, GA estimates were within ±5 days of ultrasound values in the Canadian internal validation (mean absolute error (MAE) 0.71, 95% CI: 0.71, 0.72), and within ±6 days of ultrasound GA in both the Filipino (0.90 (0.90, 0.91)) and Chinese cohorts (0.89 (0.86, 0.92)). Despite the decreased accuracy in external settings, our models incorporating metabolomic markers performed better than the baseline model, which relied on sex and birth weight alone. In preterm and growth-restricted infants, the accuracy of metabolomic models was markedly higher than the baseline model. Conclusions: Accuracy of metabolic GA algorithms was attenuated when applied in external settings.  Models including metabolomic markers demonstrated higher accuracy than models using sex and birth weight alone. As innovators look to take this work to scale, further investigation of modeling and data normalization techniques will be needed to improve robustness and generalizability of metabolomic GA estimates in low resource settings, where this could have the most clinical utility.

【 授权许可】

CC BY