PRM102 - ANALYZING REAL-WORLD DATA TO TARGET HARD-TO-IDENTIFY PATIENT POPULATIONS FOR CLINICAL TRIALS: A CASE STUDY IN METASTATIC BREAST CANCER

Abstract

Clinical research organizations (CROs) face challenges in patient recruitment arising from the increasing complexity of clinical trials. Delays caused by failing to achieve patient recruitment goals [1-3] and protocol amendments [4] contribute to CROs missing pre-launch expectations. These challenges are exacerbated in oncology and orphan diseases, where patient populations are historically difficult to identify and recruit for trials. Analyzing real-world data (RWD) to identify how and where these patients are being treated, and combining this with traditional approaches used for site selection and patient recruitment, is a novel approach to meeting these challenges. Here, we present a case study focusing on late-stage breast cancer (BrCa). We analyzed RWD on drug prescriptions and patient diagnoses from the UK healthcare sector to identify hospitals treating metastatic BrCa. BrCa prescriptions and ICD-10 diagnosis codes were used to identify hospitals with concentrated populations of metastatic BrCa patients. These insights were combined with biomarker testing rate data to further characterize the metastatic BrCa patient population in the UK. Over 240 hospitals were identified with a metastatic BrCa patient population. Approximately 2000 oncologists were identified as specialists working at these hospitals. By linking the identified sites with physician data, we were able to target oncologists at selected hospitals to engage them in clinical trial activity. This approach has the potential to expedite patient recruitment, reduce the cost associated with clinical trial enrolment, and accelerate patient access to novel therapies.