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Observational Study
. 2023 Dec 1;18(12):e0295179.
doi: 10.1371/journal.pone.0295179. eCollection 2023.

Prediction models of incontinence and sexual function one year after radical prostatectomy based on data from 20 164 prostate cancer patients

Affiliations
Observational Study

Prediction models of incontinence and sexual function one year after radical prostatectomy based on data from 20 164 prostate cancer patients

Nora Tabea Sibert et al. PLoS One. .

Abstract

Background: Incontinence and sexual dysfunction are long-lasting side effects after surgical treatment (radical prostatectomy, RP) of prostate cancer (PC). For an informed treatment decision, physicians and patients should discuss expected impairments. Therefore, this paper firstly aims to develop and validate prognostic models that predict incontinence and sexual function of PC patients one year after RP and secondly to provide an online decision making tool.

Methods: Observational cohorts of PC patients treated between July 2016 and March 2021 in Germany were used. Models to predict functional outcomes one year after RP measured by the EPIC-26 questionnaire were developed using lasso regression, 80-20 splitting of the data set and 10-fold cross validation. To assess performance, R2, RMSE, analysis of residuals and calibration-in-the-large were applied. Final models were externally temporally validated. Additionally, percentages of functional impairment (pad use for incontinence and firmness of erection for sexual score) per score decile were calculated to be used together with the prediction models.

Results: For model development and internal as well as external validation, samples of 11 355 and 8 809 patients were analysed. Results from the internal validation (incontinence: R2 = 0.12, RMSE = 25.40, sexual function: R2 = 0.23, RMSE = 21.44) were comparable with those of the external validation. Residual analysis and calibration-in-the-large showed good results. The prediction tool is freely accessible: https://nora-tabea.shinyapps.io/EPIC-26-Prediction/.

Conclusion: The final models showed appropriate predictive properties and can be used together with the calculated risks for specific functional impairments. Main strengths are the large study sample (> 20 000) and the inclusion of an external validation. The models incorporate meaningful and clinically available predictors ensuring an easy implementation. All predictions are displayed together with risks of frequent impairments such as pad use or erectile dysfunction such that the developed online tool provides a detailed and informative overview for clinicians as well as patients.

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Conflict of interest statement

I have read the journal’s policy and the authors of this manuscript have the following competing interests: Breidenbach, Dieng, Kowalski, Sibert, Wesselmann are employees of the two institutions in charge of the certification system (Deutsche Krebsgesellschaft e.V. and OnkoZert GmbH). Darr reports support for attending meetings by Janssen and IPSEN outside the submitted work. Stolzenburg reports outside of the submitted work: speaker honoraria from Intuitive Surgical and Janssen, as well as payments for medical advice by Avatera medical and support for attending meeting by Janssen, stock options by Avatera medical and Intuitive surgical. Conrad reports outside the submitted work honoraria from Jannsen Cilag GmbH and medac GmbH as lecturer as well as from Photocure GmbH for manuscript writing. Harke reports outside the submitted work honoraria by Intuitive Surgical and Pajunk. Lümmen reports outside the submitted work payments from Janssen. Kaftan reports outside the submitted work honoraria from uromed for participation in educationals and support for attending the annual meeting of the Deutsche Kontinenz Gesellschaft. All other authors have nothing to declare. This does not alter our adherence to PLOS ONE policies on sharing data and materials.

Figures

Fig 1
Fig 1. Flow chart for the sample used for model development and internal validation (all T1 questionnaires before June 2021).
Fig 2
Fig 2. Screenshot of the web-application illustrating how the prediction models could be implemented.

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