Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers
| Autor: | Matthew Henderson, Kathryn M. Denize, Pranesh Chakraborty, Malia S.Q. Murphy, Robin Ducharme, Monica Lamoureux, Julian Little, Humphrey Mwape, Bellington Vwalika, Anisur Rahman, Kumanan Wilson, Joan T. Price, Patrick Musonda, A. K. Azad Chowdhury, Brieanne Olibris, Beth K. Potter, A. Brianne Bota, Steven Hawken, Lindsay A. Wilson, Katelyn J. Rittenhouse, Jesmin Pervin, Wei Cheng, Jeffrey S. A. Stringer |
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| Jazyk: | angličtina |
| Rok vydání: | 2023 |
| Předmět: |
Estimation
Multidisciplinary Heel business.industry External validation Gestational age Retrospective cohort study Machine learning computer.software_genre ESTIMATED GESTATIONAL AGE medicine.anatomical_structure Low and middle income countries Cord blood Medicine Artificial intelligence business Algorithm computer |
| DOI: | 10.17615/s2x3-5k76 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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