2026/04/27 by Franziska Grundmann, Sita Arjune, Samer Alkarkoukly +22 · 1 voice
Biochemistry, Genetics and Molecular Biology · #Genetic and Kidney Cyst Diseases #Genomics and Rare Diseases #Hedgehog Signaling Pathway Studies
paper · pdf · doi:10.21203/rs.3.rs-9305978/v1
crossref issued 2026/04/27 · crossref published 2026/04/27 · openalex publication_date 2026/04/27 · crossref created 2026/04/27 · crossref deposited 2026/04/27 · crossref indexed 2026/04/27 · openalex created_date 2026/04/28 · openalex updated_date 2026/08/01
Abstract Real-life medical data usage is currently limited by lack of structured databases and analysis tools. Autosomal dominant polycystic kidney disease (ADPKD) remains the most common monogenic cause of kidney failure. We present MEDA-PKD, an interactive, real-time, web-based analytics platform that integrates clinical routine with research data from a large, multicenter ADPKD cohort into a harmonized database (https://shiny.cecad.uni-koeln.de/ADPKDregistry/). Importantly, MEDA-PKD provides automated real-time analysis and dynamic visualization using ShinyApps, replacing traditional static approaches to clinical cohorts. As of March 2026, MEDA-PKD visualized data from 1,735 patients (mean follow-up 796 days). Analysis of data from patients initiating tolvaptan therapy demonstrate the platform’s capacity to evaluate therapeutic impact in real time. Other tabs include family history, medication, extrarenal manifestations, lifestyle, quality of life and a wide array of lab values (20,894 entries). Besides, MEDA-PKD integrates proteomics data with deep clinical phenotyping and allows for secure individual patient-level data access to participating centers. MEDA-PKD represents a paradigm shift by transforming static cohort data into a living, actionable knowledge resource providing real-time insight in ADPKD and will serve as a template for other diseases.