Development and validation of a predictive score for diagnosing prostate cancer in primary care.

Background: Prostate cancer (PCa) represents the fifth cause of death in the male population worldwide. The prostate-specific antigen (PSA) test demonstrated poor accuracy to assess the presence of PCa. Thus, the PSA testing paradigm should be moved from the systematic screening approach to the early identification of men who are harbouring clinically significant disease. Accurate clinical-based tools to predict PCa should therefore be developed for general practice. We derived and validated a PCa predictive score using a primary care data source.

Methods: Using the Italian Health Search Database, we formed a cohort of men aged 45-90 years in the period between 2002 and 2015. These patients were followed up until 31 December 2022. Those with less than a 5-year follow-up were excluded. The cohort was randomly divided into 'derivation' and 'validation' samples in a 1:1 ratio. Along with the demographic and clinical determinants forming the score, we investigated the role of PSA kinetics in the prediction accuracy.

Results: In a cohort of 529,082 men aged 45+ years, we identified 14,524 cases of PCa (incidence rate = 2.71 per 1000 person-years; 95% confidence interval = 2.67-2.80). The prediction accuracy of the PCa-HScore featured an explained variation of 12% and a discrimination power of 70%. The calibration slope was almost equal to 1 (p = 0.951, tested for equivalence against the 'perfect' slope) and the PSA kinetics did not improve the prediction accuracy.

Conclusions: The PCa-HScore might guide the prescription of PSA and/or other clinical strategies in those men reporting certain levels of risk. A related decision support system could therefore be implemented in primary care.