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Peer-reviewedCC BYNPJ digital medicine

MEDA-PKD: a dynamic, real-time analytics platform for precision care in autosomal dominant polycystic kidney disease.

  • Franziska Grundmann1,2,3,4
  • Sita Arjune1,2,3,4
  • Samer Alkarkoukly1,5
  • Malte P Bartram1,2,3,4
  • Bodo B Beck6
  • Thomas Benzing1,2,3,4
  • Volker Boehm7
  • Katrin Bohl1,2,4,8
  • Volker Burst1,4,9
  • Martin Busch10
  • Figen Cakiroglu11
  • Sadrija Cukoski1,4
13 more authors
  • Peter Gerke12
  • Nils Große-Hokamp13
  • Jan Halbritter14,15
  • Ludwig Matrisch11
  • Thorsten Persigehl13
  • Friederike Petzold16
  • Johannes Schmidt4,17
  • Roland Schmitt18,19
  • Polina Todorova4,7,20
  • Christoph Wanner21
  • Philipp Antczak22,23,24
  • Roman-Ulrich Müller25,26,27,28,29,30
  • AD(H)PKD Study Group
30 affiliations
  1. 1Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
  2. 2Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
  3. 3Center for Rare Diseases Cologne, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany.
  4. 4Center of Excellence in Nephrology, University Hospitals Aachen, Cologne, Düsseldorf, North Rhine-Westphalia, Germany.
  5. 5Institute for Biomedical Informatics, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
  6. 6Institute of Human Genetics, University of Cologne Faculty of Medicine and University Hospital Cologne, Würzburg, Germany.
  7. 7Institute for Genetics, University of Cologne, Cologne, Germany.
  8. 8Department of Nephrology, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
  9. 9Emergency Department, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
  10. 10Department of Internal Medicine III, University Hospital Jena, Friedrich-Schiller-University, Jena, Germany.
  11. 11Department of Internal Medicine I, University Hospital Schleswig-Holstein, Campus Lübeck, Lübeck, Germany.
  12. 12Nierenzentrum Lübeck, Lübeck, Germany.
  13. 13Institute for Diagnostic and Interventional Radiology, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany.
  14. 14Department of Nephrology and Medical Intensive Care, Charité - Universitätsmedizin Berlin, Berlin, Germany.
  15. 15Renal Division, Department of Medicine, Faculty of Medicine and Medical Center, University of Freiburg, Freiburg, Germany.
  16. 16Division of Nephrology, Department of Endocrinology, Nephrology, and Rheumatology, University of Leipzig Medical Center, Leipzig, Germany.
  17. 17Bonacci GmbH, Cologne, Germany.
  18. 18Department of Nephrology and Hypertension, Hannover Medical School, Hannover, Germany.
  19. 19Department of Nephrology and Hypertension, University Hospital Schleswig-Holstein, Kiel, Germany.
  20. 20Center for Rare Diseases Düsseldorf, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany.
  21. 21Department of Clinical Studies and Epidemiology, Comprehensive Heart Failure Center, University Hospital Würzburg, Würzburg, Germany.
  22. 22Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany. [email protected].
  23. 23Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany. [email protected].
  24. 24Center of Excellence in Nephrology, University Hospitals Aachen, Cologne, Düsseldorf, North Rhine-Westphalia, Germany. [email protected].
  25. 25Department II of Internal Medicine and Center for Molecular Medicine Cologne, University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany. [email protected].
  26. 26Cologne Excellence Cluster on Cellular Stress Responses in Aging-Associated Diseases (CECAD), University of Cologne, Faculty of Medicine and University Hospital Cologne, Cologne, Germany. [email protected].
  27. 27Center for Rare Diseases Cologne, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany. [email protected].
  28. 28Center of Excellence in Nephrology, University Hospitals Aachen, Cologne, Düsseldorf, North Rhine-Westphalia, Germany. [email protected].
  29. 29Department of Nephrology, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany. [email protected].
  30. 30Center for Rare Diseases Düsseldorf, Medical Faculty, University Hospital Düsseldorf, Heinrich-Heine-University Düsseldorf, Düsseldorf, Germany. [email protected].
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Abstract

The use of medical real-world data is constrained by fragmented data infrastructures and the absence of tools integrating longitudinal clinical with molecular data at scale. Autosomal dominant polycystic kidney disease (ADPKD), the most common monogenic cause of kidney failure, exemplifies this gap given its phenotypic complexity and the need for individualized treatment decisions. We present MEDA-PKD (MEdical DAta translation service of Polycystic Kidney Disease), an interactive, near-real-time analytics platform that integrates clinical routine with research data from the German AD(H)PKD Tolvaptan Registry (NCT02497521, DRKS00008910) into a harmonized database ( https://doi.org/10.18716/adpkd_registry ). MEDA-PKD provides automated near-real-time analysis and dynamic visualization, replacing traditional static approaches to clinical cohorts. As of March 2026, MEDA-PKD visualized data from 1735 patients (mean follow-up 796 days). eGFR slope analysis of data from patients initiating tolvaptan therapy (332 patients) demonstrates the platform's capacity to evaluate real-world therapeutic impact in real-time. Other tabs include family history, medication, lifestyle, quality of life and a wide array of lab values (20,894 entries). Besides, MEDA-PKD integrates proteomics data and allows for secure individual patient-level data access. MEDA-PKD represents a paradigm shift by transforming static cohort data into a living, actionable knowledge resource in ADPKD and will serve as a template for other diseases.

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Cite

BibTeXRIS
BibTeX
@article{grundmann2026medapkd,
  title = {{MEDA-PKD: a dynamic, real-time analytics platform for precision care in autosomal dominant polycystic kidney disease.}},
  author = {Franziska Grundmann and Sita Arjune and Samer Alkarkoukly and Malte P Bartram and Bodo B Beck and Thomas Benzing and Volker Boehm and Katrin Bohl and Volker Burst and Martin Busch and Figen Cakiroglu and Sadrija Cukoski and Peter Gerke and Nils Große-Hokamp and Jan Halbritter and Ludwig Matrisch and Thorsten Persigehl and Friederike Petzold and Johannes Schmidt and Roland Schmitt and Polina Todorova and Christoph Wanner and Philipp Antczak and Roman-Ulrich Müller and AD(H)PKD Study Group},
  journal = {NPJ digital medicine},
  year = {2026},
  month = oct,
  doi = {10.1038/s41746-026-03267-0},
  url = {https://europepmc.org/article/MED/42901931},
  note = {CC BY},
}
RIS
TY  - JOUR
TI  - MEDA-PKD: a dynamic, real-time analytics platform for precision care in autosomal dominant polycystic kidney disease.
AU  - Franziska Grundmann
AU  - Sita Arjune
AU  - Samer Alkarkoukly
AU  - Malte P Bartram
AU  - Bodo B Beck
AU  - Thomas Benzing
AU  - Volker Boehm
AU  - Katrin Bohl
AU  - Volker Burst
AU  - Martin Busch
AU  - Figen Cakiroglu
AU  - Sadrija Cukoski
AU  - Peter Gerke
AU  - Nils Große-Hokamp
AU  - Jan Halbritter
AU  - Ludwig Matrisch
AU  - Thorsten Persigehl
AU  - Friederike Petzold
AU  - Johannes Schmidt
AU  - Roland Schmitt
AU  - Polina Todorova
AU  - Christoph Wanner
AU  - Philipp Antczak
AU  - Roman-Ulrich Müller
AU  - AD(H)PKD Study Group
PY  - 2026
DA  - 2026/10/20
JO  - NPJ digital medicine
DO  - 10.1038/s41746-026-03267-0
UR  - https://europepmc.org/article/MED/42901931
AB  - The use of medical real-world data is constrained by fragmented data infrastructures and the absence of tools integrating longitudinal clinical with molecular data at scale. Autosomal dominant polycystic kidney disease (ADPKD), the most common monogenic cause of kidney failure, exemplifies this gap given its phenotypic complexity and the need for individualized treatment decisions. We present MEDA-PKD (MEdical DAta translation service of Polycystic Kidney Disease), an interactive, near-real-time analytics platform that integrates clinical routine with research data from the German AD(H)PKD Tolvaptan Registry (NCT02497521, DRKS00008910) into a harmonized database ( https://doi.org/10.18716/adpkd_registry ). MEDA-PKD provides automated near-real-time analysis and dynamic visualization, replacing traditional static approaches to clinical cohorts. As of March 2026, MEDA-PKD visualized data from 1735 patients (mean follow-up 796 days). eGFR slope analysis of data from patients initiating tolvaptan therapy (332 patients) demonstrates the platform's capacity to evaluate real-world therapeutic impact in real-time. Other tabs include family history, medication, lifestyle, quality of life and a wide array of lab values (20,894 entries). Besides, MEDA-PKD integrates proteomics data and allows for secure individual patient-level data access. MEDA-PKD represents a paradigm shift by transforming static cohort data into a living, actionable knowledge resource in ADPKD and will serve as a template for other diseases.
SN  - 2398-6352
ER  -