IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v17y2020i9p3048-d351224.html
   My bibliography  Save this article

The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal

Author

Listed:
  • Shamil D. Cooray

    (Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3800, Australia
    Diabetes Unit, Monash Health, Clayton, VIC 3168, Australia)

  • Lihini A. Wijeyaratne

    (Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3800, Australia)

  • Georgia Soldatos

    (Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3800, Australia
    Diabetes Unit, Monash Health, Clayton, VIC 3168, Australia)

  • John Allotey

    (Barts Research Centre for Women’s Health, Barts and the London School of Medicine and Dentistry, Queen Mary University of London, London E1 2AB, UK
    Multidisciplinary Evidence Synthesis Hub, Queen Mary University of London, London E1 2AB, UK)

  • Jacqueline A. Boyle

    (Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3800, Australia
    Monash Women’s Program, Monash Health, Clayton, VIC 3168, Australia)

  • Helena J. Teede

    (Monash Centre for Health Research and Implementation, School of Public Health and Preventive Medicine, Monash University, Melbourne, VIC 3800, Australia
    Diabetes Unit, Monash Health, Clayton, VIC 3168, Australia)

Abstract

Gestational diabetes (GDM) increases the risk of pregnancy complications. However, these risks are not the same for all affected women and may be mediated by inter-related factors including ethnicity, body mass index and gestational weight gain. This study was conducted to identify, compare, and critically appraise prognostic prediction models for pregnancy complications in women with gestational diabetes (GDM). A systematic review of prognostic prediction models for pregnancy complications in women with GDM was conducted. Critical appraisal was conducted using the prediction model risk of bias assessment tool (PROBAST). Five prediction modelling studies were identified, from which ten prognostic models primarily intended to predict pregnancy complications related to GDM were developed. While the composition of the pregnancy complications predicted varied, the delivery of a large-for-gestational age neonate was the subject of prediction in four studies, either alone or as a component of a composite outcome. Glycaemic measures and body mass index were selected as predictors in four studies. Model evaluation was limited to internal validation in four studies and not reported in the fifth. Performance was inadequately reported with no useful measures of calibration nor formal evaluation of clinical usefulness. Critical appraisal using PROBAST revealed that all studies were subject to a high risk of bias overall driven by methodologic limitations in statistical analysis. This review demonstrates the potential for prediction models to provide an individualised absolute risk of pregnancy complications for women affected by GDM. However, at present, a lack of external validation and high risk of bias limit clinical application. Future model development and validation should utilise the latest methodological advances in prediction modelling to achieve the evolution required to create a useful clinical tool. Such a tool may enhance clinical decision-making and support a risk-stratified approach to the management of GDM. Systematic review registration: PROSPERO CRD42019115223.

Suggested Citation

  • Shamil D. Cooray & Lihini A. Wijeyaratne & Georgia Soldatos & John Allotey & Jacqueline A. Boyle & Helena J. Teede, 2020. "The Unrealised Potential for Predicting Pregnancy Complications in Women with Gestational Diabetes: A Systematic Review and Critical Appraisal," IJERPH, MDPI, vol. 17(9), pages 1-20, April.
    • Handle: RePEc:gam:jijerp:v:17:y:2020:i:9:p:3048-:d:351224
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/17/9/3048/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/17/9/3048/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. Andrew J. Vickers & Elena B. Elkin, 2006. "Decision Curve Analysis: A Novel Method for Evaluating Prediction Models," Medical Decision Making, , vol. 26(6), pages 565-574, November.
    2. Teus H. Kappen & Yvonne Vergouwe & Wilton A. van Klei & Leo van Wolfswinkel & Cor J. Kalkman & Karel G. M. Moons, 2012. "Adaptation of Clinical Prediction Models for Application in Local Settings," Medical Decision Making, , vol. 32(3), pages 1-10, May.
    3. Neal V. Dawson & Robert Weiss, 2012. "Dichotomizing Continuous Variables in Statistical Analysis," Medical Decision Making, , vol. 32(2), pages 225-226, March.
    4. Lars Holmberg & Andrew Vickers, 2013. "Evaluation of Prediction Models for Decision-Making: Beyond Calibration and Discrimination," PLOS Medicine, Public Library of Science, vol. 10(7), pages 1-2, July.
    5. Geert-Jan Geersing & Walter Bouwmeester & Peter Zuithoff & Rene Spijker & Mariska Leeflang & Karel Moons, 2012. "Search Filters for Finding Prognostic and Diagnostic Prediction Studies in Medline to Enhance Systematic Reviews," PLOS ONE, Public Library of Science, vol. 7(2), pages 1-6, February.
    6. Karel G M Moons & Joris A H de Groot & Walter Bouwmeester & Yvonne Vergouwe & Susan Mallett & Douglas G Altman & Johannes B Reitsma & Gary S Collins, 2014. "Critical Appraisal and Data Extraction for Systematic Reviews of Prediction Modelling Studies: The CHARMS Checklist," PLOS Medicine, Public Library of Science, vol. 11(10), pages 1-12, October.
    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. Ja Hyeon Ku & Myong Kim & Seok-Soo Byun & Hyeon Jeong & Cheol Kwak & Hyeon Hoe Kim & Sang Eun Lee, 2015. "External Validation of Models for Prediction of Lymph Node Metastasis in Urothelial Carcinoma of the Bladder," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-10, October.
    2. Lin Lu & Laurent Dercle & Binsheng Zhao & Lawrence H. Schwartz, 2021. "Deep learning for the prediction of early on-treatment response in metastatic colorectal cancer from serial medical imaging," Nature Communications, Nature, vol. 12(1), pages 1-11, December.
    3. Yiwang Zhou & Peter X.K. Song & Haoda Fu, 2021. "Net benefit index: Assessing the influence of a biomarker for individualized treatment rules," Biometrics, The International Biometric Society, vol. 77(4), pages 1254-1264, December.
    4. Jiaxin Li & Zijun Zhou & Jianyu Dong & Ying Fu & Yuan Li & Ze Luan & Xin Peng, 2021. "Predicting breast cancer 5-year survival using machine learning: A systematic review," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-23, April.
    5. Azwanis Abdul Hadi & Siti Ruziana Roslan & Edre Mohammad Aidid & Nurzulaikha Abdullah & Ramli Musa, 2022. "Development and Validation of a New Gadget Addiction Scale (Screen Dependency Scale) among Pre-School Children in Malaysia," IJERPH, MDPI, vol. 19(24), pages 1-15, December.
    6. Lukas Higi & Angela Lisibach & Patrick E Beeler & Monika Lutters & Anne-Laure Blanc & Andrea M Burden & Dominik Stämpfli, 2021. "External validation of the PAR-Risk Score to assess potentially avoidable hospital readmission risk in internal medicine patients," PLOS ONE, Public Library of Science, vol. 16(11), pages 1-14, November.
    7. Jing Sun & Yue Liu & Jianhui Zhao & Bin Lu & Siyun Zhou & Wei Lu & Jingsun Wei & Yeting Hu & Xiangxing Kong & Junshun Gao & Hong Guan & Junli Gao & Qian Xiao & Xue Li, 2024. "Plasma proteomic and polygenic profiling improve risk stratification and personalized screening for colorectal cancer," Nature Communications, Nature, vol. 15(1), pages 1-10, December.
    8. Fisaha Haile Tesfay & Kathryn Backholer & Christina Zorbas & Steven J. Bowe & Laura Alston & Catherine M. Bennett, 2022. "The Magnitude of NCD Risk Factors in Ethiopia: Meta-Analysis and Systematic Review of Evidence," IJERPH, MDPI, vol. 19(9), pages 1-19, April.
    9. Khushal Arjan & Lui G Forni & Richard M Venn & David Hunt & Luke Eliot Hodgson, 2021. "Clinical decision-making in older adults following emergency admission to hospital. Derivation and validation of a risk stratification score: OPERA," PLOS ONE, Public Library of Science, vol. 16(3), pages 1-12, March.
    10. Maximilian Schnippering, 2020. "R&D: the missing link between corporate social performance and financial performance?," Management Review Quarterly, Springer, vol. 70(2), pages 243-255, May.
    11. Christian Bock & Joan Elias Walter & Bastian Rieck & Ivo Strebel & Klara Rumora & Ibrahim Schaefer & Michael J. Zellweger & Karsten Borgwardt & Christian Müller, 2024. "Enhancing the diagnosis of functionally relevant coronary artery disease with machine learning," Nature Communications, Nature, vol. 15(1), pages 1-16, December.
    12. Antonio Palazón-Bru & María José Prieto-Castelló & David Manuel Folgado-de la Rosa & Ana Macanás-Martínez & Emma Mares-García & María de los Ángeles Carbonell-Torregrosa & Vicente Francisco Gil-Guillé, 2020. "Development, and Internal, and External Validation of a Scoring System to Predict 30-Day Mortality after Having a Traffic Accident Traveling by Private Car or Van: An Analysis of 164,790 Subjects and ," IJERPH, MDPI, vol. 17(24), pages 1-13, December.
    13. Paulien Van Acker & Wim Van Biesen & Evi V Nagler & Muguet Koobasi & Nic Veys & Jill Vanmassenhove, 2021. "Risk prediction models for acute kidney injury in adults: An overview of systematic reviews," PLOS ONE, Public Library of Science, vol. 16(4), pages 1-14, April.
    14. Tracey L. Marsh & Holly Janes & Margaret S. Pepe, 2020. "Statistical inference for net benefit measures in biomarker validation studies," Biometrics, The International Biometric Society, vol. 76(3), pages 843-852, September.
    15. Chang Wook Jeong & Sangchul Lee & Jin-Woo Jung & Byung Ki Lee & Seong Jin Jeong & Sung Kyu Hong & Seok-Soo Byun & Sang Eun Lee, 2014. "Mobile Application-Based Seoul National University Prostate Cancer Risk Calculator: Development, Validation, and Comparative Analysis with Two Western Risk Calculators in Korean Men," PLOS ONE, Public Library of Science, vol. 9(4), pages 1-7, April.
    16. Sara J Baart & Veerle Dam & Luuk J J Scheres & Johanna A A G Damen & René Spijker & Ewoud Schuit & Thomas P A Debray & Bart C J M Fauser & Eric Boersma & Karel G M Moons & Yvonne T van der Schouw & on, 2019. "Cardiovascular risk prediction models for women in the general population: A systematic review," PLOS ONE, Public Library of Science, vol. 14(1), pages 1-14, January.
    17. Yanqing Wang & Yingqi Zhao & Yingye Zheng, 2022. "Targeted Search for Individualized Clinical Decision Rules to Optimize Clinical Outcomes," Statistics in Biosciences, Springer;International Chinese Statistical Association, vol. 14(3), pages 564-581, December.
    18. Tae Yoon Lee & Paul Gustafson & Mohsen Sadatsafavi, 2023. "Closed-Form Solution of the Unit Normal Loss Integral in 2 Dimensions, with Application in Value-of-Information Analysis," Medical Decision Making, , vol. 43(5), pages 621-626, July.
    19. Stephana J Cherak & Andrea Soo & Kyla N Brown & E Wesley Ely & Henry T Stelfox & Kirsten M Fiest, 2020. "Development and validation of delirium prediction model for critically ill adults parameterized to ICU admission acuity," PLOS ONE, Public Library of Science, vol. 15(8), pages 1-18, August.
    20. Baker Stuart G. & Van Calster Ben & Steyerberg Ewout W., 2012. "Evaluating a New Marker for Risk Prediction Using the Test Tradeoff: An Update," The International Journal of Biostatistics, De Gruyter, vol. 8(1), pages 1-37, March.

    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:gam:jijerp:v:17:y:2020:i:9:p:3048-:d:351224. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.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.