IDEAS home Printed from https://ideas.repec.org/a/plo/pone00/0210329.html
   My bibliography  Save this article

Cardiovascular risk prediction models for women in the general population: A systematic review

Author

Listed:
  • 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 behalf of the CREW consortium

Abstract

Aim: To provide a comprehensive overview of cardiovascular disease (CVD) risk prediction models for women and models that include female-specific predictors. Methods: We performed a systematic review of CVD risk prediction models for women in the general population by updating a previous review. We searched Medline and Embase up to July 2017 and included studies in which; (a) a new model was developed, (b) an existing model was validated, or (c) a predictor was added to an existing model. Results: A total of 285 prediction models for women have been developed, of these 160 (56%) were female-specific models, in which a separate model was developed solely in women and 125 (44%) were sex-predictor models. Out of the 160 female-specific models, 2 (1.3%) included one or more female-specific predictors (mostly reproductive risk factors). A total of 591 validations of sex-predictor or female-specific models were identified in 206 papers. Of these, 333 (56%) validations concerned nine models (five versions of Framingham, SCORE, Pooled Cohort Equations and QRISK). The median and pooled C statistics were comparable for sex-predictor and female-specific models. In 260 articles the added value of new predictors to an existing model was described, however in only 3 of these female-specific predictors (reproductive risk factors) were added. Conclusions: There is an abundance of models for women in the general population. Female-specific and sex-predictor models have similar predictors and performance. Female-specific predictors are rarely included. Further research is needed to assess the added value of female-specific predictors to CVD models for women and provide physicians with a well-performing prediction model for women.

Suggested Citation

  • 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.
  • Handle: RePEc:plo:pone00:0210329
    DOI: 10.1371/journal.pone.0210329
    as

    Download full text from publisher

    File URL: https://journals.plos.org/plosone/article?id=10.1371/journal.pone.0210329
    Download Restriction: no

    File URL: https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0210329&type=printable
    Download Restriction: no

    File URL: https://libkey.io/10.1371/journal.pone.0210329?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. 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. 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.
    2. 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.
    3. Fazel, Seena & Burghart, Matthias & Fanshawe, Thomas & Gil, Sharon Danielle & Monahan, John & Yu, Rongqin, 2022. "The predictive performance of criminal risk assessment tools used at sentencing: Systematic review of validation studies," Journal of Criminal Justice, Elsevier, vol. 81(C).
    4. 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.
    5. 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.
    6. Helder Novais Bastos & Nuno S Osório & António Gil Castro & Angélica Ramos & Teresa Carvalho & Leonor Meira & David Araújo & Leonor Almeida & Rita Boaventura & Patrícia Fragata & Catarina Chaves & Pat, 2016. "A Prediction Rule to Stratify Mortality Risk of Patients with Pulmonary Tuberculosis," PLOS ONE, Public Library of Science, vol. 11(9), pages 1-14, September.
    7. 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.
    8. 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.
    9. Hans Van Remoortel & Hans Scheers & Emmy De Buck & Winne Haenen & Philippe Vandekerckhove, 2020. "Prediction modelling studies for medical usage rates in mass gatherings: A systematic review," PLOS ONE, Public Library of Science, vol. 15(6), pages 1-20, June.
    10. Daniel J Stubbs & Lisa A Grimes & Ari Ercole, 2020. "Performance of cardiopulmonary exercise testing for the prediction of post-operative complications in non cardiopulmonary surgery: A systematic review," PLOS ONE, Public Library of Science, vol. 15(2), pages 1-22, February.
    11. Vieira, Bruno Hebling & Pamplona, Gustavo Santo Pedro & Fachinello, Karim & Silva, Alice Kamensek & Foss, Maria Paula & Salmon, Carlos Ernesto Garrido, 2022. "On the prediction of human intelligence from neuroimaging: A systematic review of methods and reporting," Intelligence, Elsevier, vol. 93(C).
    12. Magdalena Lagerlund & Juan Merlo & Raquel Pérez Vicente & Sophia Zackrisson, 2015. "Does the Neighborhood Area of Residence Influence Non-Attendance in an Urban Mammography Screening Program? A Multilevel Study in a Swedish City," PLOS ONE, Public Library of Science, vol. 10(10), pages 1-21, October.
    13. Wei Zhang & Yun Tang & Huan Liu & Li ping Yuan & Chu chu Wang & Shu fan Chen & Jin Huang & Xin yuan Xiao, 2021. "Risk prediction models for intensive care unit-acquired weakness in intensive care unit patients: A systematic review," PLOS ONE, Public Library of Science, vol. 16(9), pages 1-14, September.

    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:plo:pone00:0210329. 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: plosone (email available below). General contact details of provider: https://journals.plos.org/plosone/ .

    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.