دورية أكاديمية
أعرض في EDS

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

التفاصيل البيبلوغرافية
العنوان: Development and external validation of machine learning algorithms for postnatal gestational age estimation using clinical data and metabolomic markers.
المؤلفون: Hawken S; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada., Ducharme R; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada., Murphy MSQ; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada., Olibris B; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada., Bota AB; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada., Wilson LA; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada., Cheng W; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada., Little J; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada., Potter BK; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada., Denize KM; Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada., Lamoureux M; Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada., Henderson M; Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada., Rittenhouse KJ; University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America., Price JT; University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America., Mwape H; UNC Global Projects Zambia, Lusaka, Zambia., Vwalika B; Department of Obstetrics and Gynaecology, University of Zambia School of Medicine, Lusaka, Zambia., Musonda P; Department of Medical Statistics, University of Zambia College of Public Health, Lusaka, Zambia., Pervin J; International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh., Chowdhury AKA; Dhaka Shishu (Children) Hospital, Dhaka, Bangladesh., Rahman A; International Centre for Diarrhoeal Disease Research, Dhaka, Bangladesh., Chakraborty P; Newborn Screening Ontario, Children's Hospital of Eastern Ontario, Ottawa, Ontario, Canada., Stringer JSA; University of North Carolina at Chapel Hill, Chapel Hill, North Carolina, United States of America., Wilson K; Clinical Epidemiology Program, Ottawa Hospital Research Institute, Ottawa, Ontario, Canada.; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.; Faculty of Medicine, Department of Medicine, University of Ottawa, Ottawa, Ontario, Canada.
المصدر: PloS one [PLoS One] 2023 Mar 06; Vol. 18 (3), pp. e0281074. Date of Electronic Publication: 2023 Mar 06 (Print Publication: 2023).
نوع المنشور: Journal Article; Research Support, N.I.H., Extramural; Research Support, Non-U.S. Gov't
اللغة: English
بيانات الدورية: Publisher: Public Library of Science Country of Publication: United States NLM ID: 101285081 Publication Model: eCollection Cited Medium: Internet ISSN: 1932-6203 (Electronic) Linking ISSN: 19326203 NLM ISO Abbreviation: PLoS One Subsets: MEDLINE
أسماء مطبوعة: Original Publication: San Francisco, CA : Public Library of Science
مواضيع طبية MeSH: Premature Birth* , Ankle Injuries* , Knee Injuries*, Infant, Newborn ; Female ; Pregnancy ; Humans ; Gestational Age ; Prospective Studies ; Retrospective Studies ; Zambia ; Algorithms ; Machine Learning ; Ontario
مستخلص: 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Hawken et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
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معلومات مُعتمدة: D43 TW009340 United States TW FIC NIH HHS
سلسلة جزيئية: Dryad 10.5061/dryad.m37pvmd6b
تواريخ الأحداث: Date Created: 20230306 Date Completed: 20230308 Latest Revision: 20230418
رمز التحديث: 20231215
مُعرف محوري في PubMed: PMC9987787
DOI: 10.1371/journal.pone.0281074
PMID: 36877673
قاعدة البيانات: MEDLINE
الوصف
تدمد:1932-6203
DOI:10.1371/journal.pone.0281074