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

Dongen, Laura H van; Harms, Peter P.; Hoogendoorn, Mark; Zimmerman, Dominic S; Lodder, Elisabeth M.; Hart, Leen M ’t; Herings, Ron M. C.; Weert, Henk van; Nijpels, Giel; Swart, Karin M. A.; Heijden, Amber A. van der; Blom, Marieke T.; Elders, Petra; Tan, Hanno L.

doi:10.1136/openhrt-2020-001554

Cited by 5 publications

(6 citation statements)

References 36 publications

(15 reference statements)

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“…It would therefore not be unreasonable to hypothesise careful selection of features from both established and novel complex data sources containing signals for SCD could be the key to unlocking the philosopher's stone of arrhythmology. Indeed, two studies are in recruitment that aim to develop ANNs for SCD prediction (45,46). Where classification models are built with high degrees of accuracy, SCD prediction deep learning models can transition from "if" (classification models) to "when" (time-to-event analyses (47,48)) to create targeted ICD implantation strategies for individual patients.…”

Section: Future Direction -Deep Learningmentioning

confidence: 99%

Machine learning in sudden cardiac death risk prediction: a systematic review

Barker

Khavandi

et al. 2022

EP Europace

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Aims Most patients who receive implantable cardioverter defibrillators (ICDs) for primary prevention do not receive therapy during the lifespan of the ICD, whilst up to 50% of sudden cardiac death (SCD) occur in individuals who are considered low risk by conventional criteria. Machine learning offers a novel approach to risk stratification for ICD assignment. Methods and results Systematic search was performed in MEDLINE, Embase, Emcare, CINAHL, Cochrane Library, OpenGrey, MedrXiv, arXiv, Scopus, and Web of Science. Studies modelling SCD risk prediction within days to years using machine learning were eligible for inclusion. Transparency and quality of reporting (TRIPOD) and risk of bias (PROBAST) were assessed. A total of 4356 studies were screened with 11 meeting the inclusion criteria with heterogeneous populations, methods, and outcome measures preventing meta-analysis. The study size ranged from 122 to 124 097 participants. Input data sources included demographic, clinical, electrocardiogram, electrophysiological, imaging, and genetic data ranging from 4 to 72 variables per model. The most common outcome metric reported was the area under the receiver operator characteristic (n = 7) ranging between 0.71 and 0.96. In six studies comparing machine learning models and regression, machine learning improved performance in five. No studies adhered to a reporting standard. Five of the papers were at high risk of bias. Conclusion Machine learning for SCD prediction has been under-applied and incorrectly implemented but is ripe for future investigation. It may have some incremental utility in predicting SCD over traditional models. The development of reporting standards for machine learning is required to improve the quality of evidence reporting in the field.

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Section: Future Direction -Deep Learningmentioning

confidence: 99%

Machine learning in sudden cardiac death risk prediction: a systematic review

Barker

Khavandi

et al. 2022

EP Europace

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“…These biochemical and physiologic changes are typically not manifested as symptoms in the disease process and thus the use of metabolomics would be crucial for early recognition of the risk of developing prolonged QTc and subsequent SCD. An upcoming and highly anticipated study, the Recognition of Sudden Cardiac Arrest Vulnerability in Diabetes (RESCUED) project, plans to assess the metabolomic profiles of Dutch patients with T2DM and SCD from the Amsterdam Resuscitation Studies (ARREST) Registry, the Hoorn Diabetes Care System (DCS) and local family practitioner electronic health records to further elucidate the clinical and metabolic factors to prognosticate the risk of SCD in these patients [ 34 ].…”

Section: Discussionmentioning

confidence: 99%

Association of N-Acetyl Asparagine with QTc in Diabetes: A Metabolomics Study

et al. 2022

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Changes in the cardio-metabolomics profile and hormonal status have been associated with long QT syndrome, sudden cardiac death and increased mortality. The mechanisms underlying QTc duration are not fully understood. Therefore, an identification of novel markers that complement the diagnosis in these patients is needed. In the present study, we performed untargeted metabolomics on the sera of diabetic patients at a high risk of cardiovascular disease, followed up for 2.55 [2.34–2.88] years (NCT02431234), with the aim of identifying the metabolomic changes associated with QTc. We used independent weighted gene correlation network analysis (WGCNA) to explore the association between metabolites clusters and QTc at T1 (baseline) and T2 (follow up). The overlap of the highly correlated modules at T1 and T2 identified N-Acetyl asparagine as the only metabolite in common, which was involved with the urea cycle and metabolism of arginine, proline, glutamate, aspartate and asparagine. This analysis was confirmed by applying mixed models, further highlighting its association with QTc. In the current study, we were able to identify a metabolite associated with QTc in diabetic patients at two chronological time points, suggesting a previously unrecognized potential role of N-Acetyl asparagine in diabetic patients suffering from long QTc.

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“…Conceivably, one would simply need to note the sharp increase in the number of consultations and combine this observation with (yet to be discovered) additional sentinel signs (which can be potentially revealed rather than forever remaining in their black box since the patient has actually appeared before the general practitioner) to recognise the possibility of impending SCA. In addition, this observation demonstrates that existing (large) data sets from clinical practice, including general practitioner practice, while having remained unharvested so far, may contain a wealth of relevant information that may be used to recognise individuals at increased risk of SCA 7. In addition, media campaigns aimed at the general public to increase awareness of cardiovascular disease, gender-dependent symptoms, risk factors, (early) warning signs of SCA/SCD and importance of bystander resuscitation will also be valuable.…”

mentioning

confidence: 93%

“…Clearly, to reduce the societal burden of SCD, we must focus our efforts on earlier recognition of SCA risk. Given the complex underlying causes of SCA and in view of the observation that our ability at early recognition has been stagnant over the last decades,7 we must adopt a more comprehensive strategy and reap the benefit of relatively new methods which have so far been poorly used in SCA research, for example, artificial intelligence-based analysis of large data sets, genetic analysis and metabolomic analysis 7. We must also recognise that we should direct our view to the group in society that has so far received insufficient attention in SCA research, that is, individuals who are in the care of their general practitioner and have not (yet) been referred to a cardiologist 7.…”

mentioning

confidence: 99%

“…Given the complex underlying causes of SCA and in view of the observation that our ability at early recognition has been stagnant over the last decades,7 we must adopt a more comprehensive strategy and reap the benefit of relatively new methods which have so far been poorly used in SCA research, for example, artificial intelligence-based analysis of large data sets, genetic analysis and metabolomic analysis 7. We must also recognise that we should direct our view to the group in society that has so far received insufficient attention in SCA research, that is, individuals who are in the care of their general practitioner and have not (yet) been referred to a cardiologist 7. While this group has traditionally been viewed as a black box from which SCA events occur totally unexpectedly in the absence of any handle to design appropriate interventions, a recent study draws a far less bleak picture 8.…”

mentioning

confidence: 99%

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