Journal Article

Individual prognosis at diagnosis in nonmetastatic prostate cancer: Development and external validation of the PREDICT Prostate multivariable model

Thurtle, David R., Lee, Lui S., Huang, Hong H., Greenberg, David C., Pharoah, Paul D., Gnanapragasam, Vincent J.

2019

Overview

Prognostic stratification is the cornerstone of management in nonmetastatic 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 that contextualises PCa-specific mortality (PCSM) against other cause mortality, and estimates the impact of treatment on survival. Using records from the United Kingdom National Cancer Registration and Analysis Service (NCRAS), data were collated for 10,089 men diagnosed with nonmetastatic PCa between 2000 and 2010 in Eastern England. Median follow-up was 9.8 years with 3,829 deaths (1,202 PCa specific). Totals of 19.8%, 14.1%, 34.6%, and 31.5% of men underwent conservative management, prostatectomy, radiotherapy (RT), and androgen deprivation monotherapy, respectively. A total of 2,546 men diagnosed in Singapore over a similar time period represented an external validation cohort. Data were randomly split 70:30 into model development and validation cohorts. Fifteen-year PCSM and non-PCa mortality (NPCM) were explored using separate multivariable Cox models within a competing risks framework. Fractional polynomials (FPs) were utilised to fit continuous variables and baseline hazards. Model accuracy was assessed by discrimination and calibration using the Harrell C-index and chi-squared goodness of fit, respectively, within both validation cohorts. A multivariable model estimating individualised 10- and 15-year survival outcomes was constructed combining age, prostate-specific antigen (PSA), histological grade, biopsy core involvement, stage, and primary treatment, which were each independent prognostic factors for PCSM, and age and comorbidity, which were prognostic for NPCM. The model demonstrated good discrimination, with a C-index of 0.84 (95% CI: 0.82-0.86) and 0.84 (95% CI: 0.80-0.87) for 15-year PCSM in the UK and Singapore validation cohorts, respectively, comparing favourably to international risk-stratification criteria. Discrimination was maintained for overall mortality, with C-index 0.77 (95% CI: 0.75-0.78) and 0.76 (95% CI: 0.73-0.78). The model was well calibrated with no significant difference between predicted and observed PCa-specific (p = 0.19) or overall deaths (p = 0.43) in the UK cohort. Key study limitations were a relatively small external validation cohort, an inability to account for delayed changes to treatment beyond 12 months, and an absence of tumour-stage subclassifications. 'PREDICT Prostate' is an individualised multivariable PCa prognostic model built from baseline diagnostic information and the first to our knowledge that models potential treatment benefits on overall survival. Prognostic power is high despite using only routinely collected clinicopathological information.

• Publisher: Public Library of Science,Public Library of Science (PLoS)
• Subject: Aged / Antigens / Biology and Life Sciences / Biopsy / Breast cancer / Calibration / Cancer surgery / Cancer therapies / Cell cycle / Cohort Studies / Comorbidity / Diagnosis / Discrimination / Fatalities / Follow-Up Studies / Funding / Health aspects / Health services / Hospitals / Humans / Male / Medical diagnosis / Medical prognosis / Medicine and Health Sciences / Metastasis / Middle Aged / Model accuracy / Models, Theoretical / Mortality / Multivariate Analysis / Oncology / Predictive Value of Tests / Prognosis / Prostate cancer / Prostate-specific antigen / Prostatectomy / Prostatic Neoplasms - diagnosis / Prostatic Neoplasms - epidemiology / Prostatic Neoplasms - therapy / Radiation therapy / Radiotherapy / Registries - standards / Reproducibility of Results / Retirement benefits / Risk Factors / Social Sciences / Surgery / Survival / Therapeutic applications / Tumors / United Kingdom - epidemiology / Urological surgery / Urology