About CKDNapp

Chronic kidney disease (CKD) is a common and complex disease. It is one of the leading causes of death worldwide and is characterized by varying disease progression patterns and multiple comorbidities.

We are a group of four young investigators, joined in the junior consortium CKDNapp within the e:Med initiative, funded by the German Federal Ministry of Education and Research.

Our goal is to

  1. perform computational modeling of CKD as a complex disease
  2. improve these models using novel -omics techniques
  3. discover new biomarkers
  4. develop clinical decision support software based on our models
Our software CKDNapp (short for CKD Nephrologist's App) will support physicians in providing personalized treatments and will aid them in their daily routine.

CKDNapp features

Event prediction

  • Cardiovascular events
  • Cerebrovascular events
  • Kidney disease progression
  • Acute kidney injury
  • and others

Transparent reasoning

  • Identify and report predictors of adverse events
  • Communicate patient data discrepancies

In-silico modification

  • Patient compliance
  • Lifestyle adaptation
  • Parameter uncertainties

Literature support

  • research articles
  • guidelines
  • medication information
  • established risk scores

Principal investigators

Ulla Schultheiss

Ulla Schultheiß

Nephrology / Epidemiology
Medical Center - University of Freiburg

Dataset consolidation, generation of clinical input variables, medical interpretation and validation

Helena Zacharias

Helena Zacharias

Computational Systems Biology
University Medicine Greifswald

Mathematical modeling

Michael Altenbuchinger

Michael Altenbuchinger

Algorithmic Bioinformatics
University of Hohenheim

Algorithmic foundation of CKDNapp models

Johannes Raffler

Johannes Raffler

Applied Bioinformatics
Helmholtz Zentrum München

CKDNapp mobile application and web service development