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        Predicting Long-Term Pessary Therapy Success: Insights from a Retrospective Cohort Study

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        Kuijper_6125247_Ma3WS.pdf (1.290Mb)
        Publication date
        2025
        Author
        Kuijper, Kiet
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        Summary
        Introduction: Pelvic Organ Prolapse (POP) symptoms significantly affect quality of life. Only 50-80% of patients continue using a pessary as treatment after one year. Early identification of patients less likely to benefit from pessaries is crucial to prevent unnecessary discomfort and costs. This study aims to do so by identify predictors of successful pessary use, evaluating success rates (SRs) after multiple fitting attempts (FAs), and analyzing treatment strategies. Methods: A monocenter retrospective cohort study was conducted from March to December 2022 in a secondary care setting. Patients with symptomatic POP (POP-Q stage ≥ 1) receiving their first pessary fitting were included. Baseline characteristics and follow-up data during 1 year were collected from electronic patient records. Predictive factors for successful pessary use were analyzed using multivariate regression analysis. Success was defined as continued pessary use after one year. A Kaplan-Meier survival analysis was used to visualize the number of fitting attempts (FAs) until therapy failed. The number of FAs and their correlation with success rates (SRs) were evaluated. Results: Of the 202 included patients, 124 (61.4%) continued pessary use after one year. Predictors of success included POP-Q stage IV (OR 14.5, p = 0.01), while previous hysterectomy (OR 0.35, p = 0.03) and obesity (OR 0.33, p = 0.022) were associated with less success. Failure occurred after a mean of 2.71 FAs and SRs varied between 37.5% and 100.0%, with no significant association between SR and the number of FAs (p = 0.13) Conclusion: There is strong evidence that specific patient characteristics influence successful therapy, which emphasizes the need for individual pessary treatment strategies. However, further research is needed to refine predictive models and pessary care protocols.
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        https://studenttheses.uu.nl/handle/20.500.12932/50005
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