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

L.H. Van Dongen; P.P. Harms; M. Hoogendoorn; D.S. Zimmerman; E.M. Lodder; L.M. T' Hart; R. Herings; H.C.P.M. Van Weert; G. Nijpels; K.M.A. Swart; A.A. Van Der Heijden; M.T. Blom; P.J. Elders; H.L. Tan

doi:10.1136/openhrt-2020-001554

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

L.H. Van Dongen
, P.P. Harms
, M. Hoogendoorn
, D.S. Zimmerman
, E.M. Lodder
, L.M. T' Hart
, R. Herings
, H.C.P.M. Van Weert
, G. Nijpels
, K.M.A. Swart
, A.A. Van Der Heijden
, M.T. Blom
, P.J. Elders
, H.L. Tan

Research output: Contribution to Journal › Article › Academic › peer-review

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.

Original language	English
Article number	e001554
Pages (from-to)	1-8
Number of pages	8
Journal	Open heart
Volume	8
Issue number	1
Early online date	5 Feb 2021
DOIs	https://doi.org/10.1136/openhrt-2020-001554
Publication status	Published - Jun 2021

Bibliographical note

Funding

Funding This work was supported by the European Union’s Horizon 2020 research and innovation programme under acronym ESCAPE-NET, registered under grant agreement No 733381, COST Action PARC under grant agreement No CA19137 supported by COST (European Cooperation in Science and Technology, and the Netherlands CardioVascular Research Initiative, Dutch Heart Foundation, Dutch Federation of University Medical Centres, Netherlands Organisation for Health Research and Development,Royal Netherlands Academy of Sciences - CVON2017-15 RESCUED, and CVON2018-30 Predict2. The ARREST registry is supported by an unconditional grant of Stryker, Emergency Care, Redmond WA, USA. The DCS cohort has received support from several institutions including the VUMC, Dutch Federation of University Medical Centres, health insurers, Dutch Science Organisation NWO, Dutch Organisation for Health Research and Development ZonMw, Dutch Diabetes Foundation, European Foundation for the Study of Diabetes, International Diabetes Federation, European Innovative Medicine Initiative and European Union.

Funders	Funder number
Dutch Heart Foundation
Dutch Organisation for Health Research and Development ZonMw
Dutch Science Organisation NWO
European Foundation for the Study of Diabetes, International Diabetes Federation
European Innovative Medicine Initiative
Netherlands CardioVascular Research Initiative
Royal Netherlands Academy of Sciences	CVON2017-15 RESCUED, CVON2018-30 Predict2
Horizon 2020 Framework Programme	733381
European Commission
European Cooperation in Science and Technology	CA19137
ZonMw
Diabetes Fonds
Nederlandse Federatie van Universitair Medische Centra

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 3 Good Health and Well-being

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Van Dongen, L. H., Harms, P. P., Hoogendoorn, M., Zimmerman, D. S., Lodder, E. M., T' Hart, L. M., Herings, R., Van Weert, H. C. P. M., Nijpels, G., Swart, K. M. A., Van Der Heijden, A. A., Blom, M. T., Elders, P. J., & Tan, H. L. (2021). Discovery of predictors of sudden cardiac arrest in diabetes: Rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project. Open heart, 8(1), 1-8. Article e001554. https://doi.org/10.1136/openhrt-2020-001554

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title = "Discovery of predictors of sudden cardiac arrest in diabetes: Rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project",

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.",

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Van Dongen, LH, Harms, PP, Hoogendoorn, M, Zimmerman, DS, Lodder, EM, T' Hart, LM, Herings, R, Van Weert, HCPM, Nijpels, G, Swart, KMA, Van Der Heijden, AA, Blom, MT, Elders, PJ & Tan, HL 2021, 'Discovery of predictors of sudden cardiac arrest in diabetes: Rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project', Open heart, vol. 8, no. 1, e001554, pp. 1-8. https://doi.org/10.1136/openhrt-2020-001554

TY - JOUR

T1 - Discovery of predictors of sudden cardiac arrest in diabetes

T2 - Rationale and outline of the RESCUED (REcognition of Sudden Cardiac arrest vUlnErability in Diabetes) project

AU - Van Dongen, L.H.

AU - Harms, P.P.

AU - Hoogendoorn, M.

AU - Zimmerman, D.S.

AU - Lodder, E.M.

AU - T' Hart, L.M.

AU - Herings, R.

AU - Van Weert, H.C.P.M.

AU - Nijpels, G.

AU - Swart, K.M.A.

AU - Van Der Heijden, A.A.

AU - Blom, M.T.

AU - Elders, P.J.

AU - Tan, H.L.

PY - 2021/6

Y1 - 2021/6

N2 - 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.

AB - 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.

U2 - 10.1136/openhrt-2020-001554

DO - 10.1136/openhrt-2020-001554

M3 - Article

SN - 2398-595X

VL - 8

SP - 1

EP - 8

JO - Open heart

JF - Open heart

IS - 1

M1 - e001554

ER -

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

Abstract

Bibliographical note

Funding

UN SDGs

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