Clinical development requires quick access to live trial data to address safety questions and evaluate data quality.  Historically, teams have resorted to Excel to manually populate patient profiles, despite human error limitations, inefficiency, and lack of reproducibility. Other solutions offer singular views that require repetitive programming and CDISC SDTM/ADaM dependencies. These options rarely provide quality information at a fast turnaround pace.  To solve these issues, we developed {ctpatprofile}, a modular Shiny app framework for on-demand, user generated patient profiles using live, raw EDC and central lab data. Building on Sarepta’s 2022 {usebox} talk, {ctpatprofile} features Python BOX SDK for data ingestion, an approachable 1-click UI, and exports to standard outputs for communication.  We will compare trade-offs of using EDC raw data vs derived data and emphasize the use case of starting from raw data, a common scenario in Biotech, or early-stage clinical trials. We will show technical innovations like flexible YAML configurations and parallelized R Markdown PDF rendering for enhanced user experience. We hope the information shared will help Pharmas/Biotechs to explore creating patient profiles from raw EDC data.
Preston Burns | Nathan Kosiba
Sarepta | Sarepta
EDC to Patient Profiles: A Biotech {ctpatprofile} Story
Clinical development requires quick access to live trial data to address safety questions and evaluate data quality.  Historically, teams have resorted to Excel to manually populate patient profiles, despite human error limitations, inefficiency, and lack of reproducibility. Other solutions offer singular views that require repetitive programming and CDISC SDTM/ADaM dependencies. These options rarely provide quality information at a fast turnaround pace.  To solve these issues, we developed  ctpatprofile , a modular Shiny app framework for on-demand, user generated patient profiles using live, raw EDC and central lab data. Building on Sarepta’s 2022  usebox  talk,  ctpatprofile  features Python BOX SDK for data ingestion, an approachable 1-click UI, and exports to standard outputs for communication.  We will compare trade-offs of using EDC raw data vs derived data and emphasize the use case of starting from raw data, a common scenario in Biotech, or early-stage clinical trials. We will show technical innovations like flexible YAML configurations and parallelized R Markdown PDF rendering for enhanced user experience. We hope the information shared will help Pharmas/Biotechs to explore creating patient profiles from raw EDC data.