Discovery of Predictors of Sudden Cardiac Arrest in Diabetes: Rationale and Outline of the RESCUED (REcognition of Sudden Cardiac Arrest VUlnErability in Diabetes) Project

Overview

Journal Open Heart

Publisher BMJ Publishing Group

Specialty Cardiology & Vascular Diseases

Date 2021 Feb 6

PMID 33547224

Citations 1

Authors

Laura H van Dongen

Peter P Harms

Mark Hoogendoorn

Dominic S Zimmerman

Elisabeth M Lodder

Leen M t Hart

Ron Herings

Henk C P M van Weert

Giel Nijpels

Karin M A Swart

Amber A van der Heijden

Marieke T Blom

Petra J Elders

Hanno L Tan

Affiliations

Soon will be listed here.

Abstract

Introduction: Early 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.

Aim: To 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.

Methods: The 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.

Conclusion: The 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.

Citing Articles

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