IDEAS home Printed from https://ideas.repec.org/a/gam/jijerp/v19y2022i2p734-d721371.html
   My bibliography  Save this article

Construction of Progress Prediction Model of Urinary Incontinence in Elderly Women: Protocol for a Multi-Center, Prospective Cohort Study

Author

Listed:
  • Di Zhang

    (Department of Obstetrics and Gynecology, Peking University People’s Hospital, No. 11, Xi-Zhi-Men South Street, Xi Cheng District, Beijing 100044, China
    The Key Laboratory of Female Pelvic Floor Disorders, Beijing 100044, China
    Research Center of Female Pelvic Floor Disorders, Peking University, Beijing 100044, China
    These authors contributed equally to this work.)

  • Lei Gao

    (Department of Obstetrics and Gynecology, Peking University People’s Hospital, No. 11, Xi-Zhi-Men South Street, Xi Cheng District, Beijing 100044, China
    The Key Laboratory of Female Pelvic Floor Disorders, Beijing 100044, China
    Research Center of Female Pelvic Floor Disorders, Peking University, Beijing 100044, China
    These authors contributed equally to this work.)

  • Yuanyuan Jia

    (Department of Obstetrics and Gynecology, Peking University People’s Hospital, No. 11, Xi-Zhi-Men South Street, Xi Cheng District, Beijing 100044, China
    The Key Laboratory of Female Pelvic Floor Disorders, Beijing 100044, China
    Research Center of Female Pelvic Floor Disorders, Peking University, Beijing 100044, China)

  • Shiyan Wang

    (Department of Obstetrics and Gynecology, Peking University People’s Hospital, No. 11, Xi-Zhi-Men South Street, Xi Cheng District, Beijing 100044, China
    The Key Laboratory of Female Pelvic Floor Disorders, Beijing 100044, China
    Research Center of Female Pelvic Floor Disorders, Peking University, Beijing 100044, China)

  • Haibo Wang

    (Clinical Research Institute, Peking University, Beijing 100191, China)

  • Xiuli Sun

    (Department of Obstetrics and Gynecology, Peking University People’s Hospital, No. 11, Xi-Zhi-Men South Street, Xi Cheng District, Beijing 100044, China
    The Key Laboratory of Female Pelvic Floor Disorders, Beijing 100044, China
    Research Center of Female Pelvic Floor Disorders, Peking University, Beijing 100044, China)

  • Jianliu Wang

    (Department of Obstetrics and Gynecology, Peking University People’s Hospital, No. 11, Xi-Zhi-Men South Street, Xi Cheng District, Beijing 100044, China
    The Key Laboratory of Female Pelvic Floor Disorders, Beijing 100044, China
    Research Center of Female Pelvic Floor Disorders, Peking University, Beijing 100044, China)

Abstract

Background: Urinary incontinence (UI) is a common health problem and seriously affects quality of life. Many women lack understanding of UI or are too ashamed to seek medical advice early, leading to a low treatment rate. The aim of this study is to establish an effective UI progress prediction model for elderly women with UI for earlier detection and better treatment. Methods: This study is conducted as a prospective, multi-center, cohort study, and recruits 800 women aged ≥60 with mild or moderate UI in China. Participants are divided into three groups: stress urinary incontinence group (SUI), urgency urinary incontinence group (UUI), and mixed urinary incontinence group (MUI). This study will investigate the general conditions of patients, after complete relevant pelvic floor function assessment, as well as after follow up at 6 months, 12 months, and 18 months by telephone. The primary endpoint is UI disease progress. Single factor and multi-factor Cox regression model analyses are undertaken to evaluate the associated risk factors affecting the progress of UI to establish a progress prediction model for elderly women. Discussion: This study will provide more predictive information for elderly women with UI, and new clinical references for the intervention and the treatment of UI for medical staff.

Suggested Citation

  • Di Zhang & Lei Gao & Yuanyuan Jia & Shiyan Wang & Haibo Wang & Xiuli Sun & Jianliu Wang, 2022. "Construction of Progress Prediction Model of Urinary Incontinence in Elderly Women: Protocol for a Multi-Center, Prospective Cohort Study," IJERPH, MDPI, vol. 19(2), pages 1-11, January.
    • Handle: RePEc:gam:jijerp:v:19:y:2022:i:2:p:734-:d:721371
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1660-4601/19/2/734/pdf
    Download Restriction: no
    File URL: https://www.mdpi.com/1660-4601/19/2/734/
    Download Restriction: no
    ---><---

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Di Zhang & Shiyan Wang & Lei Gao & Yuanyuan Jia & Haibo Wang & Xiuli Sun & Jianliu Wang, 2022. "Analysis of Characteristics and Quality of Life of Elderly Women with Mild to Moderate Urinary Incontinence in Community Dwellings," IJERPH, MDPI, vol. 19(9), pages 1-13, May.

    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:19:y:2022:i:2:p:734-:d:721371. 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.

    We have no bibliographic references for this item. You can help adding them by using 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.