Multiplicative Surrogate Standard Deviation: a Group Metric for the Glycemic Variability of Individual Hospitalized Patients

Overview

Journal J Diabetes Sci Technol

Specialty Endocrinology

Date 2013 Oct 16

PMID 24124960

Authors

Susan S Braithwaite

Guillermo E Umpierrez

J Geoffrey Chase

Affiliations

Soon will be listed here.

Abstract

Objective: Group metrics are described to quantify blood glucose (BG) variability of hospitalized patients.

Methods: The "multiplicative surrogate standard deviation" (MSSD) is the reverse-transformed group mean of the standard deviations (SDs) of the logarithmically transformed BG data set of each patient. The "geometric group mean" (GGM) is the reverse-transformed group mean of the means of the logarithmically transformed BG data set of each patient. Before reverse transformation is performed, the mean of means and mean of SDs each has its own SD, which becomes a multiplicative standard deviation (MSD) after reverse transformation. Statistical predictions and comparisons of parametric or nonparametric tests remain valid after reverse transformation. A subset of a previously published BG data set of 20 critically ill patients from the first 72 h of treatment under the SPRINT protocol was transformed logarithmically. After rank ordering according to the SD of the logarithmically transformed BG data of each patient, the cohort was divided into two equal groups, those having lower or higher variability.

Results: For the entire cohort, the GGM was 106 (÷/× 1.07) mg/dl, and MSSD was 1.24 (÷/× 1.07). For the subgroups having lower and higher variability, respectively, the GGM did not differ, 104 (÷/× 1.07) versus 109 (÷/× 1.07) mg/dl, but the MSSD differed, 1.17 (÷/× 1.03) versus 1.31 (÷/× 1.05), p = .00004.

Conclusions: By using the MSSD with its MSD, groups can be characterized and compared according to glycemic variability of individual patient members.

References

Mackenzie I, Whitehouse T, Nightingale P . The metrics of glycaemic control in critical care. Intensive Care Med. 2011; 37(3):435-43. DOI: 10.1007/s00134-010-2103-2. View

Rodbard D . A semilogarithmic scale for glucose provides a balanced view of hyperglycemia and hypoglycemia. J Diabetes Sci Technol. 2010; 3(6):1395-401. PMC: 2787040. DOI: 10.1177/193229680900300620. View

Van den Berghe G, Wouters P, Weekers F, Verwaest C, Bruyninckx F, Schetz M . Intensive insulin therapy in critically ill patients. N Engl J Med. 2002; 345(19):1359-67. DOI: 10.1056/NEJMoa011300. View

Meyfroidt G, Keenan D, Wang X, Wouters P, Veldhuis J, Van den Berghe G . Dynamic characteristics of blood glucose time series during the course of critical illness: effects of intensive insulin therapy and relative association with mortality. Crit Care Med. 2010; 38(4):1021-9. DOI: 10.1097/CCM.0b013e3181cf710e. View

Meynaar I, Eslami S, Abu-Hanna A, van der Voort P, de Lange D, de Keizer N . Blood glucose amplitude variability as predictor for mortality in surgical and medical intensive care unit patients: a multicenter cohort study. J Crit Care. 2012; 27(2):119-24. DOI: 10.1016/j.jcrc.2011.11.004. View

Chase J, Shaw G, Le Compte A, Lonergan T, Willacy M, Wong X . Implementation and evaluation of the SPRINT protocol for tight glycaemic control in critically ill patients: a clinical practice change. Crit Care. 2008; 12(2):R49. PMC: 2447603. DOI: 10.1186/cc6868. View

Kosiborod M, Rathore S, Inzucchi S, Masoudi F, Wang Y, Havranek E . Admission glucose and mortality in elderly patients hospitalized with acute myocardial infarction: implications for patients with and without recognized diabetes. Circulation. 2005; 111(23):3078-86. DOI: 10.1161/CIRCULATIONAHA.104.517839. View

Chase J, Lecompte A, Shaw G, Blakemore A, Wong J, Lin J . A benchmark data set for model-based glycemic control in critical care. J Diabetes Sci Technol. 2009; 2(4):584-94. PMC: 2769759. DOI: 10.1177/193229680800200409. View

Braithwaite S . Glycemic variability in hospitalized patients: choosing metrics while awaiting the evidence. Curr Diab Rep. 2012; 13(1):138-54. DOI: 10.1007/s11892-012-0345-9. View

10.

Jacobi J, Bircher N, Krinsley J, Agus M, Braithwaite S, Deutschman C . Guidelines for the use of an insulin infusion for the management of hyperglycemia in critically ill patients. Crit Care Med. 2012; 40(12):3251-76. DOI: 10.1097/CCM.0b013e3182653269. View

11.

Kitabchi A, Umpierrez G, Miles J, Fisher J . Hyperglycemic crises in adult patients with diabetes. Diabetes Care. 2009; 32(7):1335-43. PMC: 2699725. DOI: 10.2337/dc09-9032. View

12.

Krinsley J . Glycemic variability: a strong independent predictor of mortality in critically ill patients. Crit Care Med. 2008; 36(11):3008-13. DOI: 10.1097/CCM.0b013e31818b38d2. View

13.

Hermanides J, Vriesendorp T, Bosman R, Zandstra D, Hoekstra J, DeVries J . Glucose variability is associated with intensive care unit mortality. Crit Care Med. 2009; 38(3):838-42. DOI: 10.1097/CCM.0b013e3181cc4be9. View

14.

Egi M, Bellomo R, Stachowski E, French C, Hart G . Variability of blood glucose concentration and short-term mortality in critically ill patients. Anesthesiology. 2006; 105(2):244-52. DOI: 10.1097/00000542-200608000-00006. View