Implementation of personalised risk prediction and prevention of sudden cardiac death after myocardial infarction
Personalised prevention of sudden cardiac death: from analysis of existing data to large trials
This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement No 847999.
After extensive 2.5 years, the PROFID data analysis has been completed. The results, which were presented at the ESC Congress 2022 in Barcelona, have significant implications for the setup of the randomized trial programme. We are currently finalizing an updated design of the PROFID clinical trial, in order to start the submissions to the Ethics Committees at the beginning of 2023.
The Clinical Research Institute in Munich, Germany, is a full-service Contract Research Organisation (CRO) specialized in large international, commercial and non-commercial (academic) clinical studies in Europe covering both, drug trials and medical device studies.
This year, PROFID had a marked presence at the European Society of Cardiology 2022 Congress, the world’s largest cardiovascular congress, during the session “Latest Science in Arrhythmias”. Project coordinator Dr. Nikolaos Dagres of the Leipzig Heart Institute presented the final results of the PROFID analysis.
The European Association of Cardiology (ESC) is an independent, non-profit organisation and represents more than 95,000 decision-makers and healthcare professionals in the field of cardiology. The ESC acts in the interest of patients by providing cardiologists with the support and tools they need to deliver the best possible care.
In phase one of the development of the PROFID risc prediction model we analysed 19 datasets from Europe, Israel and the US, based on a collection of existing highly phenotyped data with the largest number of post-MI patients (~225,000 observations).
Prof. Peek from the University of Manchester is leading Work Package 1 “Risk Score Development” of PROFID. Together with his team he analysed 19 data sets (from more than 200.000 patients) to develop a new model for predicting the risk of sudden cardiac death after myocardial infarction.