Phenotypic stratification predicts the pace, but not the outcome, of continence recovery after radical prostatectomy

Małgorzata Terek-Derszniak, Danuta Gąsior-Perczak, Małgorzata Biskup, Tomasz Skowronek, Mariusz Nowak, Justyna Falana, Jarosław Jaskulski, Mateusz Obarzanowski, Stanislaw Gozdz and Pawel Macek

PLOS ONE, 2025, vol. 20, issue 12, 1-14

Abstract: Background: Urinary incontinence (UI) is a common complication following radical prostatectomy (RP), with heterogeneous response to pelvic floor rehabilitation. Identifying patient subgroups with distinct recovery patterns may improve treatment planning. Methods: We prospectively enrolled 182 men (mean age 66.1 ± 6.5 years) undergoing RP for localized prostate cancer. All participated in a standardized rehabilitation program. K-means clustering was applied to 11 baseline clinical variables, including urinary incontinence severity, pelvic floor function measures, and oncological risk characteristics, to identify distinct patient phenotypes. Continence was defined as pad test result ≤2 g and assessed at three time points. Statistical analyses included non-parametric tests, clustering validation (internal indices, bootstrap, consensus), and multiple testing correction using the Benjamini–Hochberg procedure. Results: Three phenotypic clusters were identified (Cluster 0: n = 97; Cluster 1: n = 65; Cluster 2: n = 20), differing significantly in oncological severity and UI burden. At the second rehabilitation visit, continence was achieved in 69.2% of Cluster 1 patients, 55.0% in Cluster 0, and 35.0% in Cluster 2 (p = 0.034). By the third rehabilitation assessment (conducted after completing phase III of the rehabilitation program), continence rates increased to 88.4%, 77.5%, and 60.0% across the three clusters. Patients with earlier recovery were more likely to have received preoperative rehabilitation (87% vs. 70%, p = 0.054). Internal validation supported the three-cluster structure, with lower stability for the smallest subgroup. Multiple testing correction confirmed significant differences across clusters and recovery patterns. Predictive models showed low accuracy (AUC

Date: 2025
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0338900

DOI: 10.1371/journal.pone.0338900

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