Because autosomal dominant polycystic kidney disease (ADPKD) is one of the most common genetic abnormalities seen in today's medical practice, many internists will likely treat patients affected by this condition. Genetic abnormalities have been increasingly recognized, and the pathophysiology of the disease is beginning to be unraveled. Because of advances in imaging technology, surrogate markers for disease progression have allowed clinical studies of newer therapeutic agents to proceed. In the near future, therapies for this common genetic disease may be available to either prevent or stabilize the disease course for many affected individuals.
Humans ; *Polycystic Kidney, Autosomal Dominant/complications/diagnosis/genetics/therapy ; Prognosis

Objectives: To facilitate clinical and translational research, imaging and non-imaging clinical data from multiple disparate systems must be aggregated for analysis. Study participant records from various sources are linked together and to patient records when possible to address research questions while ensuring patient privacy. This paper presents a novel tool that pseudonymizes participant identifiers (PIDs) using a researcher-driven automated process that takes advantage of application-programming interface (API) and the Perl Open-Source Digital Imaging and Communications in Medicine Archive (POSDA) to further de-identify PIDs. The tool, on-demand cohort and API participant identifier pseudonymization (O-CAPP), employs a pseudonymization method based on the type of incoming research data. Methods: For images, pseudonymization of PIDs is done using API calls that receive PIDs present in Digital Imaging and Communications in Medicine (DICOM) headers and returns the pseudonymized identifiers. For non-imaging clinical research data, PIDs provided by study principal investigators (PIs) are pseudonymized using a nightly automated process. The pseudonymized PIDs (P-PIDs) along with other protected health information is further de-identified using POSDA. Results: A sample of 250 PIDs pseudonymized by O-CAPP were selected and successfully validated. Of those, 125 PIDs that were pseudonymized by the nightly automated process were validated by multiple clinical trial investigators (CTIs). For the other 125, CTIs validated radiologic image pseudonymization by API request based on the provided PID and P-PID mappings. Conclusions We developed a novel approach of an ondemand pseudonymization process that will aide researchers in obtaining a comprehensive and holistic view of study participant data without compromising patient privacy.