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

  • Hospital Management of Diabetes (G Umpierrez, Section Editor)
  • Published:
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Abstract

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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Acknowledgment

The author is indebted to Dr. Geoffrey Chase, University of Canterbury, Centre for Bio-Engineering, Department of Mechanical Engineering, Christchurch, New Zealand, for discussion of logarithmic transformation, reverse transformation, and use of nonparametric tests for analysis of central tendency and variability of BG data.

Disclosure

Conflicts of interest: S.S. Braithwaite: has received honoraria from AACE/Lily/Novo Nordisk; and has received travel/accommodations expenses covered or reimbursed from AACE.

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  1. Section of Endocrinology, Diabetes and Metabolism, Visiting Clinical Professor of Medicine, University of Illinois at Chicago, 1819 W. Polk Street, M/C 640, Chicago, IL, 60612, USA

    Susan S. Braithwaite

  2. Saint Francis Hospital, Evanston, IL, 60202, USA

    Susan S. Braithwaite

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Braithwaite, S.S. Glycemic Variability in Hospitalized Patients: Choosing Metrics While Awaiting the Evidence. Curr Diab Rep 13, 138–154 (2013). https://doi.org/10.1007/s11892-012-0345-9

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