The follow up of patients on active surveillance requires to repeat prostate biopsies. Predictive models that identify patients at low risk of progression or reclassification are essential to reduce the number of unnecessary biopsies. The aim of this study is to validate the Prostate Active Surveillance Study risk calculator (PASS-RC) in the multicentric Spanish Urological Association Registry of patients on active surveillance (AS), from common clinical practice. RESULTS: We find significant differences ...
The follow up of patients on active surveillance requires to repeat prostate biopsies. Predictive models that identify patients at low risk of progression or reclassification are essential to reduce the number of unnecessary biopsies. The aim of this study is to validate the Prostate Active Surveillance Study risk calculator (PASS-RC) in the multicentric Spanish Urological Association Registry of patients on active surveillance (AS), from common clinical practice. RESULTS: We find significant differences in age, PSA and clinical stage between our validation cohort and the PASS-RC generation cohort (p < .0001), with a reclassification rate of 10-22% on the follow-up Bx, no cancer was found in 43% of the first follow-up Bx. The calibration curve shows underestimation of real appearance of reclassification. The AUC is 0.65 (C.I.95%: 0.60-0.71). PDF and CUC do not suggest a specific cut-off point of clinical use. METHODS: We select 498 patients on AS with a minimum of one follow-up biopsy (Bx) from the 1,024 males registered by 36 Spanish centers recruiting patients on the Spanish Urological Association Registry on AS. PASS-RC external validation is carried by means of calibration curve and area under de ROC-curve (AUC), identifying cut-offs of clinical utility by probability density functions (PDF) and clinical utility curves (CUC). CONCLUSIONS: in our first external validation of the PASS-RC we have obtained a moderate discrimination ability, although we cannot recommend cut-off points of clinical use. We suggest the exploration of new biomarkers and/or morpho-functional parameters from multiparametric magnetic resonance image, to improve those necessary tools on AS.
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