M.L. Universität

M.L. Universität

ERA PerMed JTC 2019 with the Martin Luther University Halle-Wittenberg

Hemodialysis treatment affects in the range of 0.1% of all population. However this complex, longstanding and chronic patient cares requests 5-10% of overall health costs in developed countries.  Quality and process control of hemodialysis treatment is nowadays based on non-individual assessment of biochemical and procedural dialysis measures not impacting patient´s outcome with regard to mortality and dialysis tolerability.

This proposals aims at developing an individual dialysis-analysing and personalized data generation and handling scheme to characterize (I) patient surrogate biodata of sympathetic activation following dialysis treatment (continous heart rate variability) and patient physical activity (step-count), (ii) patient-self reported tolerability outcome (recovery, exhaustion, vomiting, sleep quality and other) and dialysis technical machine data with potenzial to influence treatment outcome and tolerabiity. Innovative, anonymized and non-trackable data procedures shall assure integrated and searchable data storage.  Innovative mechanism to find associations between above referenced data categories (self-reported, biodata, machine data) will ensure the development of future personalized, real-time dialysis treatment procedures.  Those algorithms can be integrated in future smart dialysis facilities to enable personalized real-time variable treatment to respond to individual comorbidity and tolerability behaviour of patients.


Thomas Neumuth

Innovation Center Computer Assisted Surgery (ICCAS) – University of Leipzig, Deutschland


Emilio Gonzalez-Parra

Nephrology and Hypertension, Hospital Universitario Fundacion Jimenez Diaz, Spain


Matthias Girndt

Martin-Luther-University Halle/Wittenberg, Deutschland


Peter Hauschild

Sigmund-Freud-University Wien


Magdalena Krajewska

Wroclaw Medical University, Poland


Johann Krocza

Medifina AG, Wien, Austria


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