title: How good is your glucose control?
abstract: BACKGROUND
  Glycemic control is fundamental to the management of diabetes and maintenance of health. Popular measures of performance in glycemic control include A1c and self-monitoring of blood glucose (SMBG). As measures of performance, A1c has perspective, but it fails to recognize hypoglycemia, while SMBG lacking overall perspective finds use mainly by patients to simply evaluate their glycemic status and current response to therapy. An additional, preferably visual, measure of performance in diabetes management in general and glycemic control in particular is needed.
  
  
  METHODS
  To form a visual measure of performance, a graphical method of analysis from the statistician's toolbox (known as the lag plot) was adapted. It can utilize SMBG data sets from any source, including memory meters and registry databases in call centers. Data are retrieved, processed, formatted, and then plotted on a PC screen or printer. The resulting lag plots visually characterize the performance of glucose control achieved over periods (selectable by the user) from days to months. Supporting numerical statistics provide rigorous outcome measures that correlate with glycated hemoglobin.
  
  
  RESULTS
  Clinical use of the lag plot is illustrated in seven case studies spanning the range from no diabetes, through glucose intolerance, early-onset type 2 diabetes mellitus, type 1 diabetes, intensified therapy, pump therapy, and finally islet cell transplantation. Visual comparisons before and after action/referral show impacts of interventions, incidences of hypoglycemia, and changes in the polyglycemia of unstable diabetes. Statistical significance of observed changes are quantified.
  
  
  CONCLUSIONS
  The simple lag plot can empower patients and their providers to identify problems in glycemic control, seek proactive action, adopt beneficial strategies, evaluate outcomes, and, most importantly, rule out interventions with no benefit.
authors.affiliation: Bioengineering Department, University of California San Diego, La Jolla, California, USA. albisser@NIDM.org
authors.forename: A Michael
authors.initials: AM
authors.lastname: Albisser
authors.forename: Rodolfo
authors.initials: R
authors.lastname: Alejandro
authors.forename: Luigi F
authors.initials: LF
authors.lastname: Meneghini
authors.forename: Camillo
authors.initials: C
authors.lastname: Ricordi
mesh_terms: D000328:Adult; D000368:Aged; D001786:Blood Glucose / Q000032:analysis / Q000378:metabolism*; D015190:Blood Glucose Self-Monitoring / Q000379:methods*; D003627:Data Interpretation, Statistical; D003920:Diabetes Mellitus / Q000097:blood* / Q000188:drug therapy; D005260:Female; D006442:Glycated Hemoglobin / Q000378:metabolism; D006801:Humans; D008297:Male; D008875:Middle Aged
chemical_list: D001786:Blood Glucose; D006442:Glycated Hemoglobin