Work Package 1: P4 medicine for coronary heart disease (CHD)

Within the P4 concept, WP 1 is aimed at predictive, preventive, personalized and participatory approaches to coronary heart disease and its complications, such as heart attack and cardiomyopathy. The work package consists of the following three projects.

WP1.1 To understand disease progression through integral digital consideration of individual profiles

At the German Heart Center in Munich, there are highly standardized datasets consisting of coronary imaging, comorbidities, laboratory diagnostics and biomaterials in the biobank. For 1,800 CHD patients or healthy control patients, a project of the German Center for Cardiovascular Research (DZHK) already provides whole genome sequencing and 12,000 SNP genotyping. Further precise omics data are now to be collected from these patients, and analyzed in association with cardiac phenotypes, comorbidities and outcome data. Tissue samples were taken from a sub-group of these patients during coronary bypass surgery. From these tissue samples, extensive transcriptome, metabolome and proteome data are to be generated by RNA sequencing and by mass spectrometry. By using genetic risk scores based on these studies, the individual risk of disease progression and outcome can be better predicted for a person at risk. The main goal of this comprehensive analysis is to be able to predict new and innovative treatment methods for coronary heart disease on the basis of genomic, proteomic and metabolomic data, in order to improve personalized prediction, prevention and therapy of CHD.

The integration of relevant clinical and molecular information for a personalized treatment of patients, enabled by the collaboration of researchers, physicians and IT experts, is also the subject of the WP6 data management. Here, a knowledge platform is being developed together with the other participants of WPs 1 to 5 and 7, which enables complex molecular data to be used efficiently for individual risk assessment and progress monitoring. Software modules will integrate independent data sources and make them accessible to the comprehensive analysis, which in turn uses analytical software and algorithms. The claim goes beyond the technical solution of horizontal networking; the data is analyzed in the context of existing knowledge. This is performed by generating and using structured information, including imaging and free text data, as well as with machine learning and AI approaches.

Patients are followed throughout the treatment period, creating a comprehensive thesaurus that enables molecular patterns of high prognostic quality to be identified. These include, for example, genetic, proteomic or metabolic biomarkers. An essential feature will be the support of clinical studies and the adaption into the clinical routine.

(updated: Jan. 2020)

WP1.2 To understand disease risks through an integral digital view of individual profiles -

Patient-centered, participatory health services research with online risk calculator and HerzFit app

In the interests of preventive and participatory medicine, a digitized prevention program is to be set up in cooperation with the German Hypertension League (DHL) and the German Heart Foundation (DHS), the largest patient organization in Germany. The media presence of DHS ensures optimal visibility and reach for digital cardiovascular prevention among its more than 100,000 members.
The DHS has been operating a highly frequented online risk calculator that allows users to assess their cardiovascular risk. In a cooperation between DigiMed Bayern, DHL and DHS, an APP is to be created as a digital companion for people with manifested heart disease and people who want to monitor their heart health. In addition to information and news on specific diseases, the APP also provides information, tips and practical instructions for a healthy diet, sports and other factors such as stress reduction or recovery management. This is intended to provide personalized support for primary and secondary prevention of heart diseases. The content of the app has been validated by clinical research, and corresponds to the current state of medical research and clinical guidelines. The APP primarily sees itself as an information and communication platform. In addition, the effort is supported by motivating individualized aspects, such as goal setting and success measurement as well as the integration of local self-help groups.

A second aspect of the APP is the ongoing scientific survey of users. A central database allows questions to be sent to all users of the app at regular intervals. The focus is on sustainable, personalized motivation of the participants, which is supported by appropriate psychological interaction modules. These interaction modules aim to improve health-promoting behavior through the targeted provision of information and suggestions. Motivational measures such as personal goal setting, self-monitoring or reinforcement are used to encourage users to make positive behavioral changes. In accordance with data protection regulations, the data is initially only stored on the users' end devices. For a voluntary consent or inclusion in a study, users generally agreeing to this should be addressed without transferring individual data. If you consent to the study, the data will be transmitted pseudonymized or anonymized to a centralized platform. On the basis of the data, users are phenotyped with increasing precision over the long term, and individually over time. On this basis, measures or digital interventions can be implemented more specifically. Moreover, the success of measures already applied (e.g. long-term medication) can be assessed individually. Prevention strategies can be transferred to stratifiable groups of similar users using the APP. Thus, all users of the APP benefit in the long term from the increasing optimization of the overall system, even without participating in scientific evaluations.

The content of the app is initially generated from existing information material from DHS, DHL and Techniker Krankenkasse. This means that an extensive portfolio of high-quality content is available right from the start of the app. To ensure a high level of acceptance and user-friendliness, usability tests are integrated into the development of the APP. The APP should be freely available on the website and in APP stores for every citizen and thus generate significant added value for the health promotion of the population and scientific knowledge.

(updated: Dec. 2019)

WP1.3 Individual disease prevention through participatory and digitally supported prevention

The logistical challenges at the points of contact between participants, patients, clinical medicine, research and insurance companies are extremely sensitive, but are crucial for the success of P4 medicine. The acceptance and benefits of proactive interaction among every stakeholder involved have already been well documented in other countries. The interest in improved and efficient patient care for all those involved in the health system, including health insurers and those responsible for politics, should be ensured while respecting all ethically and data protection-relevant aspects. The systematic transfer of health research results into practice, including the comprehensive data from health insurance companies, has great potential, but is also subject to high structural and legal hurdles. The aim of the planning phase is therefore to develop a secure concept for the anonymized evaluation of digital data from one or more of the leading health insurance companies in Bavaria. Positive preliminary talks have already been held, for example, with the Allgemeine Ortskrankenkasse Bayern.

With regards to the content, aim would be to develop a nationwide risk profile for the development of coronary heart disease and to identify intervention areas for disease prevention. The potential is specified in the following from a scientific point of view: specifically, based on extensive anonymized big data from the health insurance companies, a comparison of the therapy results for coronary heart disease between clinical studies and in everyday care should be made possible. The guidelines for the treatment of CAD are based on mostly large clinical studies on narrowly defined patient populations; many of them were carried out at the DHM. These studies differ from routine patient care by:

  • inclusion of mostly younger patients,
  • clearly defined inclusion and exclusion criteria,
  • intensive motivation for therapy adherence by trained study staff,
  • shorter durations than the often long-term treatment of chronically ill people.

Systematic analyzes have shown that after a heart attack vital medication is no longer taken by almost half of the patients even 12 months later. The result is a significant deterioration in the forecast, with one-year mortality increasing from 8 % to 16 %. The off-label use of coronary stents, for example with a smaller vessel diameter, is also associated with an increased risk.

WP1.3 targets the simultaneous evaluation of clinical and epidemiological cohorts with the data of leading health insurance companies. This way, clinically well-characterized patient groups with medical data can be analyzed together concerning the following questions:

  • •    How big is the deviation from the guideline therapy?
  • What role do demographic factors play?
  • Which demographic and clinical factors prejudge a lack of adherence?
  • What are the consequences of the lack of adherence in primary and secondary prevention?
  • To what extent do patient groups in approval studies differ from routine patient care?
  • To what extent do the treatment results differ in an indication group in everyday clinical practice?

The measurable value of WP1.3 would lie in the cross-sectoral recognition of those factors indicating an inadequate therapy adherence in the reality of patient care. This is to create the basis for targeted programs addressing prognostically relevant therapy deficits. In addition, the implications of extending therapy to patient groups that were not evaluated in the clinical studies should be made transparent, for example by collecting data from the reality of care in older patients, or when using medication beyond the duration of clinical studies. For this purpose, the inpatient as well as the outpatient risk factors should be better recognized by means of systematic analysis of digital, anonymized patient data. The goal is to be able to initiate countermeasures at an early stage by recognizing risk constellations.

(updated: Jan. 2020)

Prof. Dr. med. Heribert Schunkert
Prof. Dr. med. Heribert Schunkert

Wissenschaftlicher Leiter DigiMed Bayern, Direktor der Klinik für Herz- und Kreislauferkrankungen am Deutschen Herzzentrum München

+49 (0) 89 / 1218-4073

Dr. med. Dr. med. univ. Moritz von Scheidt
Dr. med. Dr. med. univ. Moritz von Scheidt

Stellvertretender wissenschaftlicher Leiter DigiMed Bayern, Assistenzarzt der Klinik für Herz- und Kreislauferkrankungen am Deutschen Herzzentrum München

+49 (0) 89 / 1218-2849

Prof. Dr. Dr. Jürgen Beckmann
Prof. Dr. Dr. Jürgen Beckmann

Lehrstuhl für Sportpsychologie, Fakultät für Sport- und Gesundheitswissenschaften, Technische Universität München

+49 (0) 89 / 289-24541

Prof. Dr. Thomas Meitinger
Prof. Dr. Thomas Meitinger

Leitung Institut für Humangenetik, Klinikum rechts der Isar, Technische Universität München

+49 (0) 89 / 4140-6381

Prof. Dr. Matthias Mann
Prof. Dr. Matthias Mann

Director Department of Proteomics and Signal Transduction, Max-Planck-Institute of Biochemistry

+49 (0) 89 / 8578-2557
Prof. Dr. Dieter Kranzlmüller
Prof. Dr. Dieter Kranzlmüller

Vorsitzender des Direktoriums des LRZ

+49 (0) 89 / 35831-8700

Prof. Dr. Stephan Jonas
Prof. Dr. Stephan Jonas

Professorship for Digital Health, Institut für Informatik, Technische Universität München

+49 (0) 89 / 289-18206