IDEAS home Printed from https://ideas.repec.org/a/nat/natcom/v14y2023i1d10.1038_s41467-023-41069-0.html
   My bibliography  Save this article

A comparative study of postnatal anthropometric growth in very preterm infants and intrauterine growth

Author

Listed:
  • Fu-Sheng Chou

    (Kaiser Permanente Riverside Medical Center
    Southern California Permanente Medical Group)

  • Hung-Wen Yeh

    (Children’s Mercy Research Institute
    University of Missouri-Kansas City)

  • Reese H. Clark

    (Pediatrix® Medical Group)

Abstract

Most growth references for very preterm infants were developed using measurements taken at birth, and were thought to represent intrauterine growth. However, it remains unclear whether the goal of approximating an intrauterine growth rate as stated by the American Academy of Pediatrics is attainable by very preterm infants. Using real-world measurement data from very preterm infants born between 2010 through 2020, we develop models to characterize the patterns of postnatal growth, and compare them to intrauterine growth. By assessing the weight growth rate, we show three phases of postnatal growth not evident in intrauterine growth. Furthermore, postnatal length and head circumference growth exhibit a slow rate after birth, followed by an acceleration. Collectively, postnatal and intrauterine growth are distinctly different. Although postnatal growth models do not represent optimal growth of very preterm infants, they can serve as a practical tool for clinical assessment of growth and for nutrition research.

Suggested Citation

  • Fu-Sheng Chou & Hung-Wen Yeh & Reese H. Clark, 2023. "A comparative study of postnatal anthropometric growth in very preterm infants and intrauterine growth," Nature Communications, Nature, vol. 14(1), pages 1-9, December.
    • Handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41069-0
    DOI: 10.1038/s41467-023-41069-0
    as

    Download full text from publisher

    File URL: https://www.nature.com/articles/s41467-023-41069-0
    File Function: Abstract
    Download Restriction: no
    File URL: https://libkey.io/10.1038/s41467-023-41069-0?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    References listed on IDEAS

    as
    1. Simon N. Wood, 2003. "Thin plate regression splines," Journal of the Royal Statistical Society Series B, Royal Statistical Society, vol. 65(1), pages 95-114, February.
    Full references (including those not matched with items on IDEAS)

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. M.L. Nores & M.P. Díaz, 2016. "Bootstrap hypothesis testing in generalized additive models for comparing curves of treatments in longitudinal studies," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(5), pages 810-826, April.
    2. Iñaki Galán & Lorena Simón & Elena Boldo & Cristina Ortiz & Rafael Fernández-Cuenca & Cristina Linares & María José Medrano & Roberto Pastor-Barriuso, 2017. "Changes in hospitalizations for chronic respiratory diseases after two successive smoking bans in Spain," PLOS ONE, Public Library of Science, vol. 12(5), pages 1-14, May.
    3. Longhi, Christian & Musolesi, Antonio & Baumont, Catherine, 2014. "Modeling structural change in the European metropolitan areas during the process of economic integration," Economic Modelling, Elsevier, vol. 37(C), pages 395-407.
    4. Strasak, Alexander M. & Umlauf, Nikolaus & Pfeiffer, Ruth M. & Lang, Stefan, 2011. "Comparing penalized splines and fractional polynomials for flexible modelling of the effects of continuous predictor variables," Computational Statistics & Data Analysis, Elsevier, vol. 55(4), pages 1540-1551, April.
    5. Schwemmer, Philipp & Güpner, Franziska & Adler, Sven & Klingbeil, Knut & Garthe, Stefan, 2016. "Modelling small-scale foraging habitat use in breeding Eurasian oystercatchers (Haematopus ostralegus) in relation to prey distribution and environmental predictors," Ecological Modelling, Elsevier, vol. 320(C), pages 322-333.
    6. Soutik Ghosal & Timothy S. Lau & Jeremy Gaskins & Maiying Kong, 2020. "A hierarchical mixed effect hurdle model for spatiotemporal count data and its application to identifying factors impacting health professional shortages," Journal of the Royal Statistical Society Series C, Royal Statistical Society, vol. 69(5), pages 1121-1144, November.
    7. Damien Rousselière, 2019. "A Flexible Approach to Age Dependence in Organizational Mortality: Comparing the Life Duration for Cooperative and Non-Cooperative Enterprises Using a Bayesian Generalized Additive Discrete Time Survi," Journal of Quantitative Economics, Springer;The Indian Econometric Society (TIES), vol. 17(4), pages 829-855, December.
    8. Ferraccioli, Federico & Sangalli, Laura M. & Finos, Livio, 2022. "Some first inferential tools for spatial regression with differential regularization," Journal of Multivariate Analysis, Elsevier, vol. 189(C).
    9. Abdollah Jalilian, 2017. "Modelling and classification of species abundance: a case study in the Barro Colorado Island plot," Journal of Applied Statistics, Taylor & Francis Journals, vol. 44(13), pages 2401-2409, October.
    10. Carlo Fezzi & Ian Bateman, 2015. "The Impact of Climate Change on Agriculture: Nonlinear Effects and Aggregation Bias in Ricardian Models of Farmland Values," Journal of the Association of Environmental and Resource Economists, University of Chicago Press, vol. 2(1), pages 57-92.
    11. Ronald E. Gangnon & Natasha K. Stout & Oguzhan Alagoz & John M. Hampton & Brian L. Sprague & Amy Trentham-Dietz, 2018. "Contribution of Breast Cancer to Overall Mortality for US Women," Medical Decision Making, , vol. 38(1_suppl), pages 24-31, April.
    12. Marra, Giampiero & Wood, Simon N., 2011. "Practical variable selection for generalized additive models," Computational Statistics & Data Analysis, Elsevier, vol. 55(7), pages 2372-2387, July.
    13. Paolo Veneri & Vicente Ruiz, 2016. "Urban-To-Rural Population Growth Linkages: Evidence From Oecd Tl3 Regions," Journal of Regional Science, Wiley Blackwell, vol. 56(1), pages 3-24, January.
    14. Weiping Li & Su Chen, 2018. "The Early Exercise Premium In American Options By Using Nonparametric Regressions," International Journal of Theoretical and Applied Finance (IJTAF), World Scientific Publishing Co. Pte. Ltd., vol. 21(07), pages 1-29, November.
    15. Basile, Roberto & Durbán, María & Mínguez, Román & María Montero, Jose & Mur, Jesús, 2014. "Modeling regional economic dynamics: Spatial dependence, spatial heterogeneity and nonlinearities," Journal of Economic Dynamics and Control, Elsevier, vol. 48(C), pages 229-245.
    16. Ersin Körpeoğlu & Zachary Kurtz & Fatma Kılınç-Karzan & Sunder Kekre & Pat A. Basu, 2014. "Business Analytics Assists Transitioning Traditional Medicine to Telemedicine at Virtual Radiologic," Interfaces, INFORMS, vol. 44(4), pages 393-410, August.
    17. Sun, Tianyu & Chand, Satish & Sharpe, Keiran, 2018. "Effect of aging on housing prices: evidence from a panel data," MPRA Paper 94418, University Library of Munich, Germany, revised 01 Mar 2019.
    18. Feuillet, Thierry & Bulteau, Julie & Dantan, Sophie, 2021. "Modelling context-specific relationships between neighbourhood socioeconomic disadvantage and private car use," Journal of Transport Geography, Elsevier, vol. 93(C).
    19. Shailendra Gurjar & Usha Ananthakumar, 2023. "The economics of art: price determinants and returns on investment in Indian paintings," International Journal of Social Economics, Emerald Group Publishing Limited, vol. 50(6), pages 839-859, January.
    20. Ruth Salway & Nicole Helene Augustin & Miranda Elaine Glynis Armstrong, 2023. "Tortoise or Hare? The Associations between Physical Activity Volume and Intensity Distribution and the Risk of All-Cause Mortality: A Large Prospective Analysis of the UK Biobank," IJERPH, MDPI, vol. 20(14), pages 1-13, July.

    More about this item

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:nat:natcom:v:14:y:2023:i:1:d:10.1038_s41467-023-41069-0. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.nature.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.