Publication:
Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study

Issued Date

2020-07-01

Resource Type

Article

ISSN

25897500

DOI

10.1016/S2589-7500(20)30131-X

Other identifier(s)

2-s2.0-85086706974

Rights

Mahidol University

Rights Holder(s)

SCOPUS

Bibliographic Citation

The Lancet Digital Health. Vol.2, No.7 (2020), e368-e375

Suggested Citation

Russell Fung, Jose Villar, Ali Dashti, Leila Cheikh Ismail, Eleonora Staines-Urias, Eric O. Ohuma, Laurent J. Salomon, Cesar G. Victora, Fernando C. Barros, Ann Lambert, Maria Carvalho, Yasmin A. Jaffer, J. Alison Noble, Michael G. Gravett, Manorama Purwar, Ruyan Pang, Enrico Bertino, Shama Munim, Aung Myat Min, Rose McGready, Shane A. Norris, Zulfiqar A. Bhutta, Stephen H. Kennedy, Aris T. Papageorghiou, Abbas Ourmazd, S. E. Abbott, A. Abubakar, J. Acedo, I. Ahmed, F. Al-Aamri, J. Al-Abduwani, J. Al-Abri, D. Alam, E. Albernaz, H. Algren, F. Al-Habsi, M. Alija, H. Al-Jabri, H. Al-Lawatiya, B. Al-Rashidiya, D. G. Altman, W. K. Al-Zadjali, H. F. Andersen, L. Aranzeta, S. Ash, M. Baricco, F. C. Barros, H. Barsosio, C. Batiuk, M. Batra, J. Berkley, E. Bertino, M. K. Bhan, B. A. Bhat, I. Blakey, S. Bornemeier, A. Bradman, M. Buckle, O. Burnham, F. Burton, A. Capp, V. I. Cararra, R. Carew, V. I. Carrara, A. A. Carter, M. Carvalho, P. Chamberlain, Ismail L. Cheikh, L. Cheikh Ismail, A. Choudhary, S. Choudhary, W. C. Chumlea, C. Condon, L. A. Corra, C. Cosgrove, R. Craik, M. F. da Silveira, D. Danelon, T. de Wet, E. de Leon, S. Deshmukh, G. Deutsch, J. Dhami, Nicola P. Di, M. Dighe, H. Dolk, M. Domingues, D. Dongaonkar, D. Enquobahrie, B. Eskenazi, F. Farhi, M. Fernandes, D. Finkton, S. Fonseca, I. O. Frederick, M. Frigerio, P. Gaglioti, C. Garza Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study. The Lancet Digital Health. Vol.2, No.7 (2020), e368-e375. doi:10.1016/S2589-7500(20)30131-X Retrieved from: https://repository.li.mahidol.ac.th/handle/20.500.14594/57837

Research Projects

Organizational Units

Authors

Journal Issue

Thesis

Title

Achieving accurate estimates of fetal gestational age and personalised predictions of fetal growth based on data from an international prospective cohort study: a population-based machine learning study

Author(s)

Russell Fung
 Jose Villar
 Ali Dashti
 Leila Cheikh Ismail
 Eleonora Staines-Urias
 Eric O. Ohuma
 Laurent J. Salomon
 Cesar G. Victora
 Fernando C. Barros
 Ann Lambert
 Maria Carvalho
 Yasmin A. Jaffer
 J. Alison Noble
 Michael G. Gravett
 Manorama Purwar
 Ruyan Pang
 Enrico Bertino
 Shama Munim
 Aung Myat Min
 Rose McGready
 Shane A. Norris
 Zulfiqar A. Bhutta
 Stephen H. Kennedy
 Aris T. Papageorghiou
 Abbas Ourmazd
 S. E. Abbott
 A. Abubakar
 J. Acedo
 I. Ahmed
 F. Al-Aamri
 J. Al-Abduwani
 J. Al-Abri
 D. Alam
 E. Albernaz
 H. Algren
 F. Al-Habsi
 M. Alija
 H. Al-Jabri
 H. Al-Lawatiya
 B. Al-Rashidiya
 D. G. Altman
 W. K. Al-Zadjali
 H. F. Andersen
 L. Aranzeta
 S. Ash
 M. Baricco
 F. C. Barros
 H. Barsosio
 C. Batiuk
 M. Batra
 J. Berkley
 E. Bertino
 M. K. Bhan
 B. A. Bhat
 I. Blakey
 S. Bornemeier
 A. Bradman
 M. Buckle
 O. Burnham
 F. Burton
 A. Capp
 V. I. Cararra
 R. Carew
 V. I. Carrara
 A. A. Carter
 M. Carvalho
 P. Chamberlain
 Ismail L. Cheikh
 L. Cheikh Ismail
 A. Choudhary
 S. Choudhary
 W. C. Chumlea
 C. Condon
 L. A. Corra
 C. Cosgrove
 R. Craik
 M. F. da Silveira
 D. Danelon
 T. de Wet
 E. de Leon
 S. Deshmukh
 G. Deutsch
 J. Dhami
 Nicola P. Di
 M. Dighe
 H. Dolk
 M. Domingues
 D. Dongaonkar
 D. Enquobahrie
 B. Eskenazi
 F. Farhi
 M. Fernandes
 D. Finkton
 S. Fonseca
 I. O. Frederick
 M. Frigerio
 P. Gaglioti
 C. Garza

Other Contributor(s)

Ministry of Health Oman
 University of Sharjah
 Aga Khan Hospital Nairobi
 The Aga Khan University
 Shoklo Malaria Research Unit
 Hospital for Sick Children University of Toronto
 Hôpital Necker Enfants Malades
 Green Templeton College
 University of Oxford
 Universidade Catolica de Pelotas
 University of Wisconsin-Milwaukee
 University of Witwatersrand
 University of Washington, Seattle
 Peking University
 Universidade Federal de Pelotas
 Università degli Studi di Torino
 Nuffield Department of Medicine
 University of Oxford Medical Sciences Division
 Ketkar Hospital

Abstract

© 2020 The Author(s). Published by Elsevier Ltd. This is an Open Access article under the CC BY 4.0 license Background: Preterm birth is a major global health challenge, the leading cause of death in children under 5 years of age, and a key measure of a population's general health and nutritional status. Current clinical methods of estimating fetal gestational age are often inaccurate. For example, between 20 and 30 weeks of gestation, the width of the 95% prediction interval around the actual gestational age is estimated to be 18–36 days, even when the best ultrasound estimates are used. The aims of this study are to improve estimates of fetal gestational age and provide personalised predictions of future growth. Methods: Using ultrasound-derived, fetal biometric data, we developed a machine learning approach to accurately estimate gestational age. The accuracy of the method is determined by reference to exactly known facts pertaining to each fetus—specifically, intervals between ultrasound visits—rather than the date of the mother's last menstrual period. The data stem from a sample of healthy, well-nourished participants in a large, multicentre, population-based study, the International Fetal and Newborn Growth Consortium for the 21st Century (INTERGROWTH-21st). The generalisability of the algorithm is shown with data from a different and more heterogeneous population (INTERBIO-21st Fetal Study). Findings: In the context of two large datasets, we estimated gestational age between 20 and 30 weeks of gestation with 95% confidence to within 3 days, using measurements made in a 10-week window spanning the second and third trimesters. Fetal gestational age can thus be estimated in the 20–30 weeks gestational age window with a prediction interval 3–5 times better than with any previous algorithm. This will enable improved management of individual pregnancies. 6-week forecasts of the growth trajectory for a given fetus are accurate to within 7 days. This will help identify at-risk fetuses more accurately than currently possible. At population level, the higher accuracy is expected to improve fetal growth charts and population health assessments. Interpretation: Machine learning can circumvent long-standing limitations in determining fetal gestational age and future growth trajectory, without recourse to often inaccurately known information, such as the date of the mother's last menstrual period. Using this algorithm in clinical practice could facilitate the management of individual pregnancies and improve population-level health. Upon publication of this study, the algorithm for gestational age estimates will be provided for research purposes free of charge via a web portal. Funding: Bill & Melinda Gates Foundation, Office of Science (US Department of Energy), US National Science Foundation, and National Institute for Health Research Oxford Biomedical Research Centre.

Keyword(s)

Decision Sciences
 Health Professions
 Medicine

Availability

URI

https://repository.li.mahidol.ac.th/handle/20.500.14594/57837

Collections

Scopus 2020