Each year in the UK around 48,500 men are diagnosed with prostate cancer and 11,900 die from the disease. To improve survival, Professor Julia Hippisley-Cox (Nuffield Department of Primary Care and Health Sciences) and Professor Carol Coupland (University of Nottingham) have developed a tool to calculate personalised risk of prostate cancer using the health records of 1.45 million men in the QResearch database. The new risk prediction algorithm aims to diagnose more tumours earlier when they are easier to treat.
The tool is designed to be used for asymptomatic individuals and combines the prostate specific antigen (PSA) blood test result with factors such as age, ethnicity, body mass index, smoking status, social deprivation and family history. Compared to using the PSA test alone, the new algorithm is more accurate at predicting prostate cancer cases (68.2% compared to 43.9% using PSA-only), high-grade aggressive tumours (49.2 % versus 40.3%) and prostate cancer deaths (67% versus 31.5%).
The decision in most primary care practices to refer men who are asymptomatic is based on binary PSA thresholds, although this can lead to too many false-negative and false-positive results. Furthermore, a binary threshold does not give any indication for the patient as to their absolute risk of developing prostate cancer and/or clinically significant disease requiring immediate intervention. The results show that the risk equation provides a valid measure of absolute risk and is more efficient at identifying incident cases of prostate cancer, high-grade cancers and prostate cancer deaths than an approach based on a PSA threshold. The intended use is to provide a better evidence base for the GP and patient to improve decision-making regarding the most appropriate action, for example, reassurance, repetition of PSA test, referral for MRI, regular monitoring, referral to a urologist, or use of preventative interventions should any become available. - Professor Julia Hippisley-Cox (Nuffield Department of Primary Care and Health Sciences)
More research is now required to assess the best way to implement the algorithm and evaluate the health economics and the impact on prostate cancer diagnosis and subsequent survival.