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Risk analysis of blood glucose data: A quantitative approach to optimizing the control of insulin dependent diabetes. (English) Zbl 0981.92013

Summary: Patients with Insulin-Dependent Diabetes are continuously involved in a clinical optimization process: to maintain strict glycemic control without increasing their risk for hypoglycemia. This study offers quantitative tools for on-line assessment of the quality of this optimization, based on self-monitoring of blood glucose (SMBG). Ninety-six adults with Insulin Dependent Diabetes Mellitus (IDDM), age \(35\pm 8\) yrs., duration of diabetes \(16\pm 10\) yrs., \(\text{HbA}_{\text{1c}}\) \(8.6\pm 1.8\%\), 43 of whom had a recent history of severe hypoglycemia (SH), while 53 did not, used Lifescan One Touch It meters for \(135\pm 53\) SMBG readings over a month. For the following six months the subjects recorded occurrence of SH. The two patient groups, with and without a history of SH, did not differ in age, duration of diabetes, \(\text{HbA}_{\text{1c}}\), insulin units/day, average BG or BG variability.
We suggest a computational procedure based on a symmetrization of the BG measurement scale and on a superimposed BG risk function, that allows for computation of two glycemic control markers: the Low BG Index (LBGI) and the High BG Index (HBGI). The LBGI is associated with SH: the LBGI and the rate of change of the BG risk, classified correctly 77% of the subjects with vs. without a history of SH and accounted for 46% of the variance of future SH. The HBGI, in combination with age, duration of diabetes and daily insulin dose, accounted for 57% of the variance of patients’ glycosylated hemoglobin. We conclude that the LBGI and the HBGI are accurate on-line SMBG measures for patients’ glycemic control.

MSC:

92C50 Medical applications (general)
92-08 Computational methods for problems pertaining to biology
93C95 Application models in control theory
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