Abstract
Non-metastatic castration-resistant prostate cancer (nmCRPC) is a disease state in which cancer is localized but resistant to testosterone suppression therapy (Androgen Deprivation Therapy (ADT) or bilateral orchidectomy). There is relatively little real world data focusing on this disease state either for characterising patients or describing their treatment pathways. This study set out to assess the feasibility of using linked routine health datasets to identify individuals with nmCRPC and to estimate their nmCRPC progression date. The study datasets were routine health data for Wales hosted by the SAIL Databank, Swansea University, Wales. Records of patients diagnosed with prostate cancer between 2000 and 2015 were identified by clinical coding (ICD-10 and Read). An algorithm was designed to specify the features of nmCRPC based on presence of codes for testosterone suppression therapy, Prostate Specific Antigen (PSA) test results and codes indicating absence of metastatic disease. Only records that contained sufficient PSA test results to determine a rise during treatment (indicating progression to castration resistance) were included. The algorithm was tested with three different criteria for PSA test levels and intervals, and two thresholds of stringency for determining cases. The datasets produced a cohort of 38,021 individuals diagnosed with prostate cancer. Of these 14,860 received ADT while non-metastatic and 6,101 had at least three recorded PSA test results during ADT. The number of nmCRPC cases identified was between 1,281 and 1,534 (non-stringent criteria) and between 439 and 509 (stringent criteria). Case identification is dependent on accurate, complete coding. The results of this study suggest that it is feasible to use linked routine health datasets to create cohorts of individuals with nmCRPC, including an estimation of date of progression to nmCRPC. Various processes were investigated to improve data quality, particularly in the identification of metastases, resulting in cohorts of sufficient size to enable statistical analysis.
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