Real-Time Platform for Data Analysis and Visualization in ADPKD: Insights from a Large Prospective Cohort Study

Background: Autosomal dominant polycystic kidney (ADPKD) is the most common genetic cause of kidney failure. Exploiting the full power of existing ADPKD cohorts is often impaired by the cumbersome need for repetitive analyses and limited harmonization of database structures. To overcome these hurdles, we have recently developed an industry-standard database system (MEDA, PMID: 39790555) for patient data which was implemented for the German AD(H)PKD cohort. Methods: MEDA is a Python-based tool designed to unify disparate data sources into a cohesive PostgreSQL database, facilitating real-time analytics and visualization based on R/Shiny scripts. The platform's modular design, utilizing Docker containers and Jenkins automation guarantees that the database remains current with minimal manual intervention. This system was combined with a novel pipeline for direct data export from clinical information systems to fully characterize the German AD(H)PKD cohort (NCT02497521). Results: MEDA provides real-time insight into all key characteristics ranging from classical baseline characteristics to imaging, extrarenal manifestations, medication, family history, quality of life and lab values. As of May 19, 2025, MEDA visualized data of 1631 patients with ADPKD (51% female) at a median age of 45 years and a median eGFR 66 ml/min/1.73m2 (min 6.5, max 143 ml/min at baseline). MEDA currently has access to 14,524 creatinine values (mean follow-up 1,718 days, max 13,880). Male patients show faster eGFR decline than female patients (-3.37 vs. -2.69 ml/min/yr) and stratification by Mayo Imaging Class clearly separates groups based on disease progression velocity (MIC 1A -1.78, 1E -4.48 ml/min/yr). Using a longitudinal analysis comparing slopes before and on tolvaptan currently shows a reduction of annual eGFR decline by 25 % (pvalue 0.0029). Conclusion: MEDA is a powerful tool to characterize the natural disease course of ADPKD in a real-time fashion. It will, thus, also be a crucial basis to patient stratification for clinical trials, detection of novel indicators of disease progression and the examination of the impact of therapeutic interventions as exemplified by the analysis on the effect of tolvaptan.