Informatics4life

Summary

The interdisciplinary subproject Comorbidities in Heart Failure – A Network Approach aims to develop a co-morbidity-based understanding based on cases of heart failure (HF) in a real-life clinical and outpatient department.  Data derived from patient contacts from departments of internal medicine of the University Hospital of Heidelberg University will be analyzed with the aim to construct co-morbidity-associated disease models that can be incorporated into a personalized systems medicine approach.

In cooperation with the Research Ware House (RWH, SP 3) and data sources from the clinical-wide hospital information system, which partially relies on human computer interaction, patients suffering systolic or diastolic HF will be retrieved according to state-of-the-art definitions.

A network approach then investigates and evaluates a patient-specific co-morbidity-/risk profile with clinical or subclinical impact on cardiac function based on a six-dimensional model of patient characteristics that comprises environmental , psycho-social (e.g. depression), neuro-vegetative (e.g. HRV, cortisol), inflammatory (e.g. CrP), somatic (e.g. weight), and organ-specific (e.g. nephrological, endocrine, etc.) factors. Molecular data will be analyzed with functional genomics methods. Machine learning, supported by crowdsourcing in the form of a DREAM challenge will be utilized to predict factors affecting HF. The network will comply with high standards of medical data protection.

The applicability of this interdisciplinary approach will be evaluated with a clinical study conducted in different units of the university hospital.