• Open heart · Feb 2021

    Multicenter Study

    Discovery of predictors of sudden cardiac arrest in diabetes: rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project.

    • Laura H van Dongen, Peter P Harms, Mark Hoogendoorn, Dominic S Zimmerman, Elisabeth M Lodder, Leen M 't Hart, Ron Herings, van WeertHenk C P MHCPMDepartment of General Practice, Amsterdam Public Health, Amsterdam UMC Locatie AMC, Amsterdam, Netherlands., Giel Nijpels, SwartKarin M AKMAGeneral Practice Medicine, Amsterdam UMC - Locatie VUmc, Vrije Universiteit, Amsterdam Public Health Research Institute, Amsterdam, Netherlands.PHARMO Institute, Utrecht, Utrecht, Netherlands., Amber A van der Heijden, Marieke T Blom, Petra J Elders, and Hanno L Tan.
    • Clinical and Experimental Cardiology, Amsterdam UMC - Locatie AMC, Heart Centre, Amsterdam Cardiovascular Sciences, University of Amsterdam, Amsterdam, Netherlands.
    • Open Heart. 2021 Feb 1; 8 (1).

    IntroductionEarly recognition of individuals with increased risk of sudden cardiac arrest (SCA) remains challenging. SCA research so far has used data from cardiologist care, but missed most SCA victims, since they were only in general practitioner (GP) care prior to SCA. Studying individuals with type 2 diabetes (T2D) in GP care may help solve this problem, as they have increased risk for SCA, and rich clinical datasets, since they regularly visit their GP for check-up measurements. This information can be further enriched with extensive genetic and metabolic information.AimTo describe the study protocol of the REcognition of Sudden Cardiac arrest vUlnErability in Diabetes (RESCUED) project, which aims at identifying clinical, genetic and metabolic factors contributing to SCA risk in individuals with T2D, and to develop a prognostic model for the risk of SCA.MethodsThe RESCUED project combines data from dedicated SCA and T2D cohorts, and GP data, from the same region in the Netherlands. Clinical data, genetic data (common and rare variant analysis) and metabolic data (metabolomics) will be analysed (using classical analysis techniques and machine learning methods) and combined into a prognostic model for risk of SCA.ConclusionThe RESCUED project is designed to increase our ability at early recognition of elevated SCA risk through an innovative strategy of focusing on GP data and a multidimensional methodology including clinical, genetic and metabolic analyses.© Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY. Published by BMJ.

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