Development and Validation of a Predictive Model of Prostate Screening Compliance: A Nationwide Population-Based Study.

Abstract

Introduction: Prostate cancer (PCa) is the second most common cancer in men worldwide, with significant incidence and mortality, particularly in Mexico, where diagnosis at advanced stages is common. Early detection through screening methods such as digital rectal examination and prostate-specific antigen testing is essential to improve outcomes. Despite current efforts, compliance with prostate screening (PS) remains low due to several barriers. This study aims to develop and validate a predictive model for PCa screening compliance in Mexican men.

Materials and methods: Retrospective observational design with data from the Mexican Health and Aging Study (MHAS). Participants were men aged 50-69 from three cohorts: development/internal validation, temporal validation, and external validation. Key predictors were identified using relaxed Least Absolute Shrinkage and Selection Operator (LASSO) regression, and model performance was assessed using the area under the curve (AUC) from receiver operating characteristic (ROC) analyses, along with calibration and decision curve analysis (DCA). A web nomogram was also developed.

Results: The final model included seven key predictors. AUC values indicated good predictive performance: 0.783 for the training subgroup, 0.722 for the test subgroup, 0.748 for the time cohort, and 0.756 for the external cohort, with sensitivities of 73.5%. The DCA demonstrated the superior clinical utility of the model compared to the reference strategies.

Conclusions: The predictive model developed for performance to PCa screening is robust across different cohorts and highlights critical factors influencing performance. The accompanying web-based nomogram enhances clinical applicability and supports interventions aimed at improving PCa screening rates among Mexican men.