Browsing by Author "Cesar G. Victora"

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    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
    (2020-07-01) 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; 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
    © 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.
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    Association between Preterm-Birth Phenotypes and Differential Morbidity, Growth, and Neurodevelopment at Age 2 Years: Results from the INTERBIO-21st Newborn Study
    (2021-05-01) Jose Villar; María C. Restrepo-Méndez; Rose McGready; Fernando C. Barros; Cesar G. Victora; Shama Munim; Aris T. Papageorghiou; Roseline Ochieng; Rachel Craik; Hellen C. Barsosio; James A. Berkley; Maria Carvalho; Michelle Fernandes; Leila Cheikh Ismail; Ann Lambert; Shane A. Norris; Eric O. Ohuma; Alan Stein; Chrystelle O.O. Tshivuila-Matala; Krina T. Zondervan; Adele Winsey; Francois Nosten; Ricardo Uauy; Zulfiqar A. Bhutta; Stephen H. Kennedy; Faculty of Tropical Medicine, Mahidol University; The Wellcome Centre for Human Genetics; University of Sharjah; Wellcome Trust Research Laboratories Nairobi; Aga Khan Hospital Nairobi; The Aga Khan University; London School of Hygiene & Tropical Medicine; Hospital for Sick Children University of Toronto; Green Templeton College; University of Oxford; University of Southampton, Faculty of Medicine; Universidade Catolica de Pelotas; Liverpool School of Tropical Medicine; The World Bank Group; University of the Witwatersrand, Johannesburg; Universidade Federal de Pelotas; Wits School of Public Health; Nuffield Department of Medicine; University of Oxford Medical Sciences Division
    Importance: The etiologic complexities of preterm birth remain inadequately understood, which may impede the development of better preventative and treatment measures. Objective: To examine the association between specific preterm-birth phenotypes and clinical, growth, and neurodevelopmental differences among preterm newborns compared with term newborns up to age 2 years. Design, Setting, and Participants: The INTERBIO-21st study included a cohort of preterm and term newborn singletons enrolled between March 2012 and June 2018 from maternity hospitals in 6 countries worldwide who were followed up from birth to age 2 years. All pregnancies were dated by ultrasonography. Data were analyzed from November 2019 to October 2020. Exposures/Interventions: Preterm-birth phenotypes. Main Outcomes and Measures: Infant size, health, nutrition, and World Health Organization motor development milestones assessed at ages 1 and 2 years; neurodevelopment evaluated at age 2 years using the INTERGROWTH-21st Neurodevelopment Assessment (INTER-NDA) tool. Results: A total of 6529 infants (3312 boys [50.7%]) were included in the analysis. Of those, 1381 were preterm births (mean [SD] gestational age at birth, 34.4 [0.1] weeks; 5148 were term births (mean [SD] gestational age at birth, 39.4 [0] weeks). Among 1381 preterm newborns, 8 phenotypes were identified: no main maternal, fetal, or placental condition detected (485 infants [35.1%]); infections (289 infants [20.9%]); preeclampsia (162 infants [11.7%]); fetal distress (131 infants [9.5%]); intrauterine growth restriction (110 infants [8.0%]); severe maternal disease (85 infants [6.2%]); bleeding (71 infants [5.1%]); and congenital anomaly (48 infants [3.5%]). For all phenotypes, a previous preterm birth was a risk factor for recurrence. Each phenotype displayed differences in neonatal morbidity and infant outcomes. For example, infants with the no main condition detected phenotype had low neonatal morbidity but increased morbidity and hospitalization incidence at age 1 year (odds ratio [OR], 2.2; 95% CI, 1.8-2.7). Compared with term newborns, the highest risk of scoring lower than the 10th centile of INTER-NDA normative values was observed in the fine motor development domain among newborns with the fetal distress (OR, 10.6; 95% CI, 5.1-22.2) phenotype. Conclusions and Relevance: Results of this study suggest that phenotypic classification may provide a better understanding of the etiologic factors and mechanisms associated with preterm birth than continuing to consider it an exclusively time-based entity..
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    Fetal cranial growth trajectories are associated with growth and neurodevelopment at 2 years of age: INTERBIO-21st Fetal Study
    (2021-04-01) José Villar; Robert B. Gunier; Chrystelle O.O. Tshivuila-Matala; Stephen A. Rauch; Francois Nosten; Roseline Ochieng; María C. Restrepo-Méndez; Rose McGready; Fernando C. Barros; Michelle Fernandes; Verena I. Carrara; Cesar G. Victora; Shama Munim; Rachel Craik; Hellen C. Barsosio; Maria Carvalho; James A. Berkley; Leila Cheikh Ismail; Shane A. Norris; Eric O. Ohuma; Alan Stein; Ann Lambert; Adele Winsey; Ricardo Uauy; Brenda Eskenazi; Zulfiqar A. Bhutta; Aris T. Papageorghiou; Stephen H. Kennedy; Faculty of Tropical Medicine, Mahidol University; Center for Environmental Research and Children's Health; University of Sharjah; Wellcome Trust Research Laboratories Nairobi; Aga Khan Hospital Nairobi; The Aga Khan University; London School of Hygiene & Tropical Medicine; Hospital for Sick Children University of Toronto; Green Templeton College; University of Oxford; University of Southampton, Faculty of Medicine; Universidade Catolica de Pelotas; The World Bank Group; University of the Witwatersrand, Johannesburg; Universidade Federal de Pelotas; Wits School of Public Health; Nuffield Department of Medicine; University of Oxford Medical Sciences Division
    Many observational studies and some randomized trials demonstrate how fetal growth can be influenced by environmental insults (for example, maternal infections)1 and preventive interventions (for example, multiple-micronutrient supplementation)2 that can have a long-lasting effect on health, growth, neurodevelopment and even educational attainment and income in adulthood3. In a cohort of pregnant women (n = 3,598), followed-up between 2012 and 2019 at six sites worldwide4, we studied the associations between ultrasound-derived fetal cranial growth trajectories, measured longitudinally from <14 weeks’ gestation, against international standards5,6, and growth and neurodevelopment up to 2 years of age7,8. We identified five trajectories associated with specific neurodevelopmental, behavioral, visual and growth outcomes, independent of fetal abdominal growth, postnatal morbidity and anthropometric measures at birth and age 2. The trajectories, which changed within a 20–25-week gestational age window, were associated with brain development at 2 years of age according to a mirror (positive/negative) pattern, mostly focused on maturation of cognitive, language and visual skills. Further research should explore the potential for preventive interventions in pregnancy to improve infant neurodevelopmental outcomes before the critical window of opportunity that precedes the divergence of growth at 20–25 weeks’ gestation.
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    Mortality risk in preterm and small-for-gestational-age infants in low-income and middle-income countries: A pooled country analysis
    (2013-06-07) Joanne Katz; Anne C.C. Lee; Naoko Kozuki; Joy E. Lawn; Simon Cousens; Hannah Blencowe; Majid Ezzati; Zulfiqar A. Bhutta; Tanya Marchant; Barbara A. Willey; Linda Adair; Fernando Barros; Abdullah H. Baqui; Parul Christian; Wafaie Fawzi; Rogelio Gonzalez; Jean Humphrey; Lieven Huybregts; Patrick Kolsteren; Aroonsri Mongkolchati; Luke C. Mullany; Richard Ndyomugyenyi; Jyh Kae Nien; David Osrin; Dominique Roberfroid; Ayesha Sania; Christentze Schmiegelow; Mariangela F. Silveira; James Tielsch; Anjana Vaidya; Sithembiso C. Velaphi; Cesar G. Victora; Deborah Watson-Jones; Robert E. Black; Johns Hopkins Bloomberg School of Public Health; Brigham and Women's Hospital; Save the Children USA; London School of Hygiene & Tropical Medicine; Imperial College London; The Aga Khan University; The University of North Carolina System; Universidade Federal de Pelotas; Centro; Harvard School of Public Health; Pontificia Universidad Catolica de Chile; Clinica Santa Maria; Zvitambo; Universiteit Gent; Prins Leopold Instituut voor Tropische Geneeskunde; Mahidol University; Uganda Ministry of Health; Clinica Davila; Universidad de los Andes, Santiago; UCL Institute of Child Health; George Washington University; Kobenhavns Universitet; Copenhagen University Hospital; University of Witwatersrand; National Institutes of Medical Research
    Background Babies with low birthweight (<2500 g) are at increased risk of early mortality. However, low birthweight includes babies born preterm and with fetal growth restriction, and not all these infants have a birthweight less than 2500 g. We estimated the neonatal and infant mortality associated with these two characteristics in low-income and middle-income countries. Methods For this pooled analysis, we searched all available studies and identified 20 cohorts (providing data for 2015019 livebirths) from Asia, Africa, and Latin America that recorded data for birthweight, gestational age, and vital statistics through 28 days of life. Study dates ranged from 1982 through to 2010. We calculated relative risks (RR) and risk differences (RD) for mortality associated with preterm birth (<32 weeks, 32 weeks to <34 weeks, 34 weeks to <37 weeks), small-for-gestational-age (SGA; babies with birthweight in the lowest third percentile and between the third and tenth percentile of a US reference population), and preterm and SGA combinations. Findings Pooled overall RRs for preterm were 6·82 (95% CI 3·56-13·07) for neonatal mortality and 2·50 (1·48-4·22) for post-neonatal mortality. Pooled RRs for babies who were SGA (with birthweight in the lowest tenth percentile of the reference population) were 1·83 (95% CI 1·34-2·50) for neonatal mortality and 1·90 (1·32-2·73) for post-neonatal mortality. The neonatal mortality risk of babies who were both preterm and SGA was higher than that of babies with either characteristic alone (15·42; 9·11-26·12). Interpretation Many babies in low-income and middle-income countries are SGA. Preterm birth affects a smaller number of neonates than does SGA, but is associated with a higher mortality risk. The mortality risks associated with both characteristics extend beyond the neonatal period. Differentiation of the burden and risk of babies born preterm and SGA rather than with low birthweight could guide prevention and management strategies to speed progress towards Millennium Development Goal 4 - the reduction of child mortality. © 2013 Elsevier Ltd.