Hypoglycaemia and QT interval prolongation in type 1 diabetes – bridging the gap between clamp studies and spontaneous episodes

Christensen, Torsten; Cichosz, Simon Lebech; Tarnow, Lise; Randløv, Jette; Kristensen, L. E.; Struijk, Johannes; Eldrup, Ebbe; Hejlesen, Ole

doi:10.1016/j.jdiacomp.2014.03.007

Cited by 24 publications

(24 citation statements)

References 28 publications

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“…Our initial search yielded 4625 citations, of which 67 full texts were assessed for eligibility (Figure ). After examination, 24 publications from 21 studies were considered eligible . Duplicate publications of 3 studies were identified .…”

Section: Resultsmentioning

confidence: 99%

“…A total of 8 studies had an observational design, reporting outcomes among participants with T1DM (n = 5), with T2DM (n = 2) or with both (n = 1) . The remaining 12 studies involved experimentally induced hypoglycaemia in participants with T1DM (n = 6), with T2DM (n = 5) or with both (n = 1) . The definition of hypoglycaemia varied across studies.…”

Section: Resultsmentioning

confidence: 99%

“…A total of 17 studies reported outcome data for QTc. As two of these studies reported mean change between the conditions, as opposed to absolute mean values, random effects meta‐analysis was possible for only 15 studies . The two studies excluded from the meta‐analysis reported a significant increase in QTc length during hypoglycaemia ( P < .05).…”

Section: Resultsmentioning

confidence: 99%

“…Additionally, the influence of confounders among observational studies that lack controlled conditions should also be considered. In contrast, while the highly controlled and widely used hyperinsulinaemic‐euglycaemic clamp technique provides an ideal model for demonstrating mechanisms by which hypoglycaemia may increase the risk of arrhythmia, it does not deliver a true reflection of insulin and glucose dynamics and the sympathoadrenal responses during spontaneous clinical hypoglycaemia …”

Section: Discussionmentioning

confidence: 99%

See 3 more Smart Citations

Association of hypoglycaemia and risk of cardiac arrhythmia in patients with diabetes mellitus: A systematic review and meta‐analysis

Fitzpatrick

Chatterjee

Seidu

et al. 2018

Diabetes Obesity Metabolism

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Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Resultsmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

See 2 more Smart Citations

Association of hypoglycaemia and risk of cardiac arrhythmia in patients with diabetes mellitus: A systematic review and meta‐analysis

Fitzpatrick

Chatterjee

Seidu

et al. 2018

Diabetes Obesity Metabolism

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show abstract

“…[95][96][97][98] Hypoglycemia affects the entire autonomic nervous system, including the heart, the brain, and perspiration. 99,100 This has led to development of prediction systems that include information from EEG, skin impedance measurements, and electrocardiograms. 96,[101][102][103] Some have attempted to use the glucose content in perspiration to predict blood glucose levels.…”

Section: Hypoglycemiamentioning

confidence: 99%

Toward Big Data Analytics

Cichosz

Johansen

Hejlesen

2015

J Diabetes Sci Technol

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Diabetes is one of the top priorities in medical science and health care management, and an abundance of data and information is available on these patients. Whether data stem from statistical models or complex pattern recognition models, they may be fused into predictive models that combine patient information and prognostic outcome results. Such knowledge could be used in clinical decision support, disease surveillance, and public health management to improve patient care. Our aim was to review the literature and give an introduction to predictive models in screening for and the management of prevalent short-and long-term complications in diabetes. Predictive models have been developed for management of diabetes and its complications, and the number of publications on such models has been growing over the past decade. Often multiple logistic or a similar linear regression is used for prediction model development, possibly owing to its transparent functionality. Ultimately, for prediction models to prove useful, they must demonstrate impact, namely, their use must generate better patient outcomes. Although extensive effort has been put in to building these predictive models, there is a remarkable scarcity of impact studies.

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Modelling the relationship between continuously measured glucose and electrocardiographic data in adults with type 1 diabetes mellitus

Charamba

Liew

Coen

et al. 2021

Endocrino Diabet & Metabol

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Hypoglycaemia and QT interval prolongation in type 1 diabetes – bridging the gap between clamp studies and spontaneous episodes

Cited by 24 publications

References 28 publications

Association of hypoglycaemia and risk of cardiac arrhythmia in patients with diabetes mellitus: A systematic review and meta‐analysis

Association of hypoglycaemia and risk of cardiac arrhythmia in patients with diabetes mellitus: A systematic review and meta‐analysis

Toward Big Data Analytics

Modelling the relationship between continuously measured glucose and electrocardiographic data in adults with type 1 diabetes mellitus

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