Unveiling novel bladder cancer associations from multicentred primary and secondary care electronic health records by machine learning: a case-control study.

OBJECTIVE: The rising incidence and mortality in bladder cancer (BC) underscore the importance of identifying asscociated features. Current reliance on haematuria as a primary indicator for BC proves inadequate. While mining electronic health records (EHRs) offer potential of identifying BC-related signals, traditional data-driven methods struggle with high-dimensional datasets. This study aims to uncover novel BC-associated clinical signals by developing Parsimony-driven cAtegory-balaNced binary Signal extractor for Primary Care EHRs (PanSPICE) tailored to extremely high-dimensional data linked from multi-centres.

METHODS: We collected BC cases and control patients (n = 64,884) linked at patient-level from Welsh nationwide databases, yielding 48,261 features in primary care settings. The PanSPICE approach begins with information gain to pre-rank features, then applies Retentive Stickiness Binary Particle Swarm Optimisation (RSBPSO) combined with C5.0 classification tree to overcome computational barriers in feature selection. A two-layer optimisation treated clinical signals in care processes (POC), diagnoses (DIAG), and medications (MED) separately to prevent feature masking. A tailored fitness function for RSBPSO to simultaneously optimise model performance and feature sparsity. Associations of the selected features were interpreted using logistic regression models adjusted for deprivation indices.

RESULTS: The PanSPICE identified 38 optimal features (AUC (area under the curve) = 0.81, 95 % CI: 0.80-0.82), including urinary tract infections (OR = 2.19, 95 % CI: 2.05-2.14) and inverse associations with stroke (OR = 0.64, 95 % CI: 0.54-0.74) and dementia (OR = 0.25, 95 % CI: 0.17-0.35). Gender stratification revealed female-specific urine glucose testing association (OR = 1.24, 95 % CI: 1.08-1.43). Certain medications, such as trimethoprim, were positively associated with BC, while others, including ramipril and prednisolone, showed protective effects.

CONCLUSION: The PanSPICE enables efficient high-dimensional EHR analysis, revealing under-recognised potential BC risk profiles and protective comorbidities. Gender-specific differences in BC associations highlight the importance of gender-stratified analyses, while computational advances provide a template for EHR-based clinical discovery. Findings warrant further mechanistic research into neurological protective pathways.