How “sweet” complexity is and how “bitter” variability can be; the new aspect of intensive care unit hyperglycemia*

Freire, Amado X.; Murillo, Luis C.

doi:10.1097/ccm.0b013e3181ce217e

Cited by 6 publications

(2 citation statements)

References 13 publications

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“…On the other hand, complexity evaluates a different dimension of the glucoregulatory system . Since most biological variables are the result of several recursive feedback and feed‐forward loops, they show an irregular rhythm that is a feature of physiological (normal) function.…”

Section: Introductionmentioning

confidence: 99%

Influence of glucometric ‘dynamical’ variables on duodenal‐jejunal bypass liner (DJBL) anthropometric and metabolic outcomes

Colás

Varela

Mráz

et al. 2020

Diabetes Metabolism Res

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Background: The endoscopically implanted duodenal-jejunal bypass liner (DJBL) is an attractive alternative to bariatric surgery for obese diabetic patients. This article aims to study dynamical aspects of the glycaemic profile that may influence DJBL effects.Methods: Thirty patients underwent DJBL implantation and were followed for 10 months. Continuous glucose monitoring (CGM) was performed before implantation and at month 10. Dynamical variables from CGM were measured: coefficient of variation of glycaemia, mean amplitude of glycaemic excursions (MAGE), detrended fluctuation analysis (DFA), % of time with glycaemia under 6.1 mmol/L (TU6.1), area over 7.8 mmol/L (AO7.8) and time in range. We analysed the correlation between changes in both anthropometric (body mass index, BMI and waist circumference) and metabolic (fasting blood glucose, FBG and HbA1c) variables and dynamical CGMderived metrics and searched for variables in the basal CGM that could predict successful outcomes.Results: There was a poor correlation between anthropometric and metabolic outcomes. There was a strong correlation between anthropometric changes and changes in glycaemic tonic control (ΔBMI-ΔTU6.1: rho = − 0.67, P < .01) and between metabolic outcomes and glycaemic phasic control (ΔFBG-ΔAO7.8: r = .60, P < .01). Basal AO7.8 was a powerful predictor of successful metabolic outcome (0.85 in patients with AO7.8 above the median vs 0.31 in patients with AO7.8 below the median: Chisquared = 5.67, P = .02). Conclusions:In our population, anthropometric outcomes of DJBL correlate with improvement in tonic control of glycaemia, while metabolic outcomes correlate preferentially with improvement in phasic control. Assessment of basal phasic control may help in candidate profiling for DJBL implantation. K E Y W O R D S diabesity, type 2 diabetes mellitus, continuous glucose monitoring, duodenal-jejunal bypass liner (DJBL), metabolic surgery, detrended fluctuation analysis (DFA)

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Section: Introductionmentioning

confidence: 99%

Influence of glucometric ‘dynamical’ variables on duodenal‐jejunal bypass liner (DJBL) anthropometric and metabolic outcomes

Colás

Varela

Mráz

et al. 2020

Diabetes Metabolism Res

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“…[202122] Hypoglycemic episode and glycemic variability are associated with higher mortality ratio. [23242526] Hypoglycemic symptoms are often ill-recognized during surgical procedures due to hemodynamic variability and blunting by anesthetic agents while under general anesthesia, reporting of hypoglycemia is not possible. [27] The added responsibilities of an anesthesiologist, and performance of multiple concurrent tasks during surgical procedures hardly allows an anesthesiologist to accurately ensure the sampling frequency and point of care (POC) monitoring of blood glucose levels.…”

Section: Introductionmentioning

confidence: 99%

Hypoglycaemia in anesthesiology practice: Diagnostic, preventive, and management strategies

et al. 2013

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Diabetes mellitus has emerged as one of the fastest growing non communicable diseases worldwide. Management of diabetic patients during surgical and critically illness is of paramount challenge to anesthesiologist and intensivist. Among its major acute complications, hypoglycemia has been given lesser attention as compared to other major acute complications; diabetic ketoacidosis and hyperosmolar non ketotic coma. However, newer studies and literary evidence have established the serious concerns of morbidity and mortality, both long- and short-term, related to hypoglycemia. basis. Invariably, diabetic patients are encountered in our daily routine practice of anesthesia. During fasting status as well as the perioperative period, it is hypoglycemia that is of high concern to anesthesiologist. Management has to be based on clinical, pharmacological, social, and psychological basis, so as to completely prevent the complications arising from an acute episode of hypoglycemia. This review aims to highlight various aspects of hypoglycemia and its management both from endocrine and anesthesia perspective.

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Glycemic Variability in Hospitalized Patients: Choosing Metrics While Awaiting the Evidence

Braithwaite

2012

Curr Diab Rep

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Hyperglycemia, hypoglycemia, preexisting diabetes, and glycemic variability each may affect hospital outcomes. Observational findings derived from randomized trials or retrospective studies suggest that independent of hypoglycemia and hyperglycemia, a relationship exists between variability and hospital outcomes. A review of studies conducted in diverse hospital populations is reported here, showing a relationship between measures of variability and nonglycemic outcomes, including ICU and hospital mortality and length of stay. "Glycemic variability" has an intuitive meaning, understood as a propensity of a single patient to develop repeated episodes of excursions of BG over a relatively short period of time that exceed the amplitude expected in normal physiology. It is proposed that each of 3 dimensions of variability should be separately studied: (1) magnitude of glycemic excursions during intervals of relative stability of the moving average of BG, (2) frequency with which a critical magnitude of excursion is exceeded, and (3) presence or absence of fine tuning. Multiple hospital studies have found that the standard deviation (SD) of the data set of blood glucose values (BG) of individual patients predicts outcomes. An appropriate refinement would be to report the "Reverse-transformed group mean of the SD of the logarithmically transformed BG data set of each patient," with confidence intervals. In logarithmic space, group means of the SD of BGs of each patient may be compared, using an appropriate parametric test. Upon reverse transformation, the upper and lower bounds of the confidence intervals become asymmetric about the reverse-transformed group mean of the SD. There is a need to understand what patterns of dispersion of BG over time are captured by SD as a predictor of outcomes. Among the causes of high SD, a subgroup may consist of patients having frequent oscillations of BG. Another subgroup may consist of patients experiencing a major change of overall glycemia during the timeframe of data collection. Appropriate metrics should be developed to recognize both variability in the sense of recurrent large oscillations of BG, and separately to recognize any time-dependent change of overall glycemia during hospitalization. Especially in relation to uncontrolled diabetes, there is a need to know whether rapid correction of chronic hyperglycemia adversely affects hospital outcomes. We have some understanding of how to control or prevent change of overall glycemia, and less understanding of how to control variability. Each may be associated with outcomes, and each may be detected by a high SD, but it remains uncertain whether intervention to prevent either pattern of changing glycemia would affect outcomes.

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How “sweet” complexity is and how “bitter” variability can be; the new aspect of intensive care unit hyperglycemia*

Cited by 6 publications

References 13 publications

Influence of glucometric ‘dynamical’ variables on duodenal‐jejunal bypass liner (DJBL) anthropometric and metabolic outcomes

Influence of glucometric ‘dynamical’ variables on duodenal‐jejunal bypass liner (DJBL) anthropometric and metabolic outcomes

Hypoglycaemia in anesthesiology practice: Diagnostic, preventive, and management strategies

Glycemic Variability in Hospitalized Patients: Choosing Metrics While Awaiting the Evidence

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