A tool for shared decision making on referral for prostate biopsy in the primary care setting: Integrating risks of cancer with life expectancy
Prostate cancer (PCa) testing involves a complex individually based decision making process. It should consider competing risks from other comorbidities when estimating a survival benefit from the early detection of clinically significant (cs)PCa. We aimed to develop a prediction tool that provides concrete advice for the general practitioner (GP) on whether to refer a man for further assessment. We hereto combined the probability of detecting csPCa and the potential overall survival benefit from early detection and treatment. The PCa detection probabilities were derived from 3616 men enrolled in the Dutch arm of the European Randomized Study of Screening for Prostate Cancer (ERSPC). Survival estimates were derived from 19,834 men from the Surveillance, Epidemiology, and End Results (SEER) registry, ERSPC, and Dutch life tables. Treatment benefit was estimated from the Prostate Cancer Intervention versus Observation Trial (PIVOT, n = 731). The prediction of csPCa detection was based on prostate-specific antigen (PSA), age, %freePSA, and digital rectal examination (DRE). The life expectancy (LE) for patients with PCa receiving no treatment was adjusted for age and Charlson comorbidity index. A negative impact on LE and treatment benefit was found with higher age and more comorbidity. The proposed integrated approach may support triage at GP practices, as PCa is a heterogeneous disease in predominantly elderly men.
|Keywords||Life expectancy, Mortality, Prediction model, Prostate cancer, Prostate cancer survival, Screening, Treatment|
|Persistent URL||dx.doi.org/10.3390/jpm9020019, hdl.handle.net/1765/117964|
|Journal||Journal of Personalized Medicine|
Verbeek, J.F.M, Nieboer, D, Parker, C, Kattan, M.W, Steyerberg, E.W, & Roobol-Bouts, M.J. (2019). A tool for shared decision making on referral for prostate biopsy in the primary care setting: Integrating risks of cancer with life expectancy. Journal of Personalized Medicine, 9(2). doi:10.3390/jpm9020019