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Decision support for prevention of adverse events in cardiovascular diseases


Efficient decision support tools can process large amounts of diverse data and formulate insights quickly.

The constant pressure of managing healthcare costs in the face of an ageing population has raised the need for systematic, extensive analysis of the data accumulated in the healthcare system - often referred to as “healthcare analytics”. 
Data is being collected in large amounts, both within the professional healthcare system as well via self-measurements during daily living. In principle this wealth of data can be used to optimise risk assessments at individual level and personalise interventions. The patient is monitored to assess the symptoms, to make the diagnosis and to follow the immediate recovery after interventions. However, current data exists in silo’s and is rarely combined. Thus it is not systematically exploited or integrated with clinical background data. Quantitative analysis methods are needed to assess the complication risks, in cost-efficiency analysis or in developing clinical treatment guidelines.
An example of setting in which this type of analysis can make a true difference is in cardiovascular diseases, where prediction of potential complications of the patient state is important. Patients hospitalized for cardiac problems such as myocardial infarction and heart failure, have a relatively risk of sudden cardiac death (SCD) and stroke especially during the first month after the index event - assessing this risk, however, is a highly complex problem.


​We have developed a professional data acquisition, management, integration and analysis system for biosignals, such as heart electrocardiogram (ECG), other vital signs and relevant clinical information. With this system the patient is monitored through the healthcare protocol up to the first weeks at home. The acquired data is collected from hospital and personal data storages to an open, well organized and integrated research database from where it is analysed with the state-of-the-art, and newer, methods. The system is implemented with operational, commercially available and working prototypes of devices and software as provided by co-operators and the open source community.


The research database and the analytics developed around it will enable the implementation of devices services to be provided to healthcare professionals and consumers by the project partners for international markets. Interfacing with to long term storage systems such as biobanks is also considered. The medical findings and technical results advance the knowledge in these fields. Furthermore, the results of our clinical study with our pilot population can be utilized in a wider scope; in creating services for patients with other cardiovascular diseases.