External validation of the PREDICT Prostate tool for prognostication in non-metastatic prostate cancer: A study in 69,206 men from Prostate Cancer data Base Sweden
BAUS ePoster online library. Thurtle D. 06/24/19; 259546; P5-2
Mr. David Thurtle
Mr. David Thurtle
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Introduction and objectives
PREDICT Prostate is a novel prognostic model for non-metastatic prostate cancer(PCa). Derived from UK data, it uses baseline clinico-pathological and co-morbidity data to generate individualised survival estimates and model potential treatment benefit. Here we externally validated the model in a large independent dataset, and reviewed performance by treatment groups.

Materials and methods
Data on age, PSA, clinical T stage, grade group, biopsy involvement, primary treatment and comorbidity were retrieved from the nation-wide population-based PCa dataBase Sweden(PCBase). Men with non-metastatic PCa and PSA<100 ng/ml diagnosed between 2000 and 2010 were included.
15-year PCa-specific mortality(PCSM) and all-cause mortality(ACM) estimates were calculated using the PREDICT Prostate algorithm within a competing-risk model. Discrimination was assessed using Harrell's concordance(c)-index. Calibration was evaluated using cumulative follow-up until 15 years.


69,206 men were included with 13 years median follow-up. Overall discrimination of PREDICT was good with c-indices of 0.85(95%CI:0.85-0.86) for PCSM and 0.79(95%CI:0.79-0.79) for ACM. Calibration was excellent with 25,925 deaths predicted and 25,849 deaths observed.
20,384 men underwent conservative management and 32,842men received radical treatment. Within these treatment groups c-indices for 15-year PCSM were 0.81 and 0.78 respectively. C-indices were further improved at 0.88 for PCSM and 0.75 for ACM among men on 'active surveillance'. Differences between observed and predicted deaths were less than 3.5% in each treatment group.


This large external validation demonstrates PREDICT Prostate is a robust and generalisable model. It improves upon existing models by providing individualised estimates, adjusting for competing-risks and modelling treatment benefit.
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