PREDICT: Prostate – A novel prognostic model that estimates individual survival in newly diagnosed primary non-metastatic prostate cancer
Prognostic stratification is the cornerstone of management in non-metastatic prostate cancer (PCa). However, existing prognostic models are inadequate -often using treatment outcomes rather than survival, stratifying by broad heterogeneous groups and using heavily treated cohorts. To address this unmet need, we developed an individualised prognostic model which contextualizes PCa-specific mortality (PCSM) against other cause mortality, and estimates the impact of treatment on survival.
Data were collated for 10,089 men diagnosed with non-metastatic PCa between 2000 and 2010 in Eastern England. These were randomly split 70:30 into model development and validation cohorts. Separate multivariable models were built for 10-year PCSM and non-prostate cancer mortality(NPCM) with fractional polynomials used to fit continuous variables and baseline hazards. Discrimination and calibration were assessed by area under the curve (AUC) and Chi-Square goodness-of-fit. A Singaporean cohort of 2546 men represented a validation set of different ethnicity and geography.
Median follow-up was 9.8years with 3276 deaths (1030 of which were PCa-specific). 19.8%, 14.1%, 34.6% and 31.5% of men underwent conservative management, prostatectomy, radiotherapy and androgen deprivation monotherapy respectively. An individualised model predicting 10-year survival outcomes was constructed combining age, PSA, histological grade group, stage and treatment which were independent prognostic factors for PCSM. Age and comorbidity were prognostic for NPCM (examples in Figure 1). The model demonstrated good discrimination and calibration in the validation cohort with no significant difference in actual and predicted PCSM (p=0.16) or overall mortality (p=0.46) and AUC 0.81 (95%CI 0.78-0.83) and 0.83 (95%CI 0.81-0.84) respectively. In the Singapore cohort (330 deaths, median f/u 5.1-years) the model performed well with