A semilogarithmic scale for glucose provides a balanced view of hyperglycemia and hypoglycemia.

David Rodbard
Author Information
  1. David Rodbard: Biomedical Informatics Consultants, LLC, Potomac, Maryland 20854-4721, USA. drodbard@comcast.net

Abstract

OBJECTIVE: It would be desirable to improve the ability of physicians and patients to identify hypoglycemic episodes when viewing displays of glucose by date, time of day, or day of the week.
RESEARCH DESIGN AND METHODS: A logarithmic scale is utilized for display of glucose versus date and time of day using a range of 40 to 400 mg/dl. Several plausible alternatives are considered for transformation of the glucose data.
RESULT: Use of a semilogarithmic plot triples the percentage of the vertical axis allocated to hypoglycemia (e.g., 40-80 mg/dl) from 10% to 30.1% while compressing the hyperglycemic region. The log scale improves the symmetry of the glucose distribution. Transformations were evaluated corresponding to the Schlichtkrull M(100) value, the high blood glucose index/low blood glucose index of Kovatchev and associates, an index of glycemic control developed by the present author, and the GRADE score of Hill and coworkers. Results are similar for all four transformations. This approach is applicable both to self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM). Based on preliminary results, it is proposed that the log transform could potentially facilitate analysis of glucose patterns and may facilitate rapid and consistent detection and appreciation of the severity and consistency of hypoglycemic episodes, even in the presence of complex overlapping patterns commonly observed in both SMBG and CGM glucose profiles.
CONCLUSION: Display of glucose on a logarithmic scale can potentially improve the accuracy of analysis and interpretation of popular methods for graphic display of glucose values. Device manufacturers should consider including options for semilogarithmic display of glucose on SMBG meters, CGM sensors, and software for retrospective analyses of glucose data.

References

  1. Acta Med Scand. 1965 Jan;177:95-102 [PMID: 14251860]
  2. Diabetes Technol Ther. 2009 Nov;11(11):717-23 [PMID: 19905888]
  3. Diabetes. 1997 Feb;46(2):271-86 [PMID: 9000705]
  4. Diabetes Care. 2006 Jan;29(1):44-50 [PMID: 16373894]
  5. Diabet Med. 2007 Jul;24(7):753-8 [PMID: 17459094]
  6. Diabetes Technol Ther. 2009 Dec;11(12):757-65 [PMID: 20001676]
  7. J Diabetes Sci Technol. 2009 Nov 01;3(6):1388-94 [PMID: 20144393]
  8. Diabetes Care. 2006 Dec;29(12):2644-9 [PMID: 17130198]
  9. J Diabetes Sci Technol. 2009 Sep 01;3(5):1121-7 [PMID: 20144425]
  10. J Am Coll Cardiol. 2009 Jan 20;53(3):298-304 [PMID: 19147051]
  11. Diabetes Care. 1998 Nov;21(11):1870-5 [PMID: 9802735]
  12. Diabetes Technol Ther. 2007 Jun;9(3):203-10 [PMID: 17561790]
  13. Diabetes Care. 2006 Nov;29(11):2433-8 [PMID: 17065680]
  14. Diabetes Technol Ther. 2009 Sep;11(9):551-65 [PMID: 19764834]
  15. Diabetes. 1976 Jul;25(7):580-5 [PMID: 1278606]
  16. Diabetes Technol Ther. 2009 Jun;11 Suppl 1:S55-67 [PMID: 19469679]

MeSH Term

Algorithms
Blood Glucose
Blood Glucose Self-Monitoring
Computer Graphics
Diabetes Mellitus
Diagnostic Equipment
Humans
Hyperglycemia
Hypoglycemia
Hypoglycemic Agents
Linear Models
Models, Biological
Monitoring, Physiologic
Predictive Value of Tests
Severity of Illness Index
Software
Time Factors

Chemicals

Blood Glucose
Hypoglycemic Agents

Word Cloud

Created with Highcharts 10.0.0glucosescaledaydisplaysemilogarithmicbloodSMBGCGMimprovehypoglycemicepisodesdatetimelogarithmicmg/dldatahypoglycemialogindexpotentiallyfacilitateanalysispatternsOBJECTIVE:desirableabilityphysicianspatientsidentifyviewingdisplaysweekRESEARCHDESIGNANDMETHODS:utilizedversususingrange40400SeveralplausiblealternativesconsideredtransformationRESULT:Useplottriplespercentageverticalaxisallocatedeg40-8010%301%compressinghyperglycemicregionimprovessymmetrydistributionTransformationsevaluatedcorrespondingSchlichtkrullM100valuehighindex/lowKovatchevassociatesglycemiccontroldevelopedpresentauthorGRADEscoreHillcoworkersResultssimilarfourtransformationsapproachapplicableself-monitoringcontinuousmonitoringBasedpreliminaryresultsproposedtransformmayrapidconsistentdetectionappreciationseverityconsistencyevenpresencecomplexoverlappingcommonlyobservedprofilesCONCLUSION:DisplaycanaccuracyinterpretationpopularmethodsgraphicvaluesDevicemanufacturersconsiderincludingoptionsmeterssensorssoftwareretrospectiveanalysesprovidesbalancedviewhyperglycemia

Similar Articles

Cited By (13)