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. 2010 Nov 1;4(6):1532-9.
doi: 10.1177/193229681000400631.

Translation of personalized decision support into routine diabetes care

Petra Augstein  1 , Lutz Vogt, Klaus-Dieter Kohnert, Peter Heinke, Eckhard Salzsieder
Affiliations

Translation of personalized decision support into routine diabetes care

Petra Augstein et al. J Diabetes Sci Technol. .

Abstract

Objective: The aim of this study was to evaluate the impact of personalized decision support (PDS) on metabolic control in people with diabetes and cardiovascular disease.

Research design and methods: The German health insurance fund BKK TAUNUS offers to its insured people with diabetes and cardiovascular disease the possibility to participate in the Diabetiva® program, which includes PDS. Personalized decision support is generated by the expert system KADIS® using self-control data and continuous glucose monitoring (CGM) as its data source. The physician of the participating person receives the PDS once a year, decides about use or nonuse, and reports his/her decision in a questionnaire. Metabolic control of participants treated by use or nonuse of PDS for one year and receiving CGM twice was analyzed in a retrospective observational study. The primary outcome was hemoglobin A1c (HbA1c); secondary outcomes were mean sensor glucose (MSG), glucose variability, and hypoglycemia.

Results: A total of 323 subjects received CGM twice, 289 had complete data sets, 97% (280/289) were type 2 diabetes patients, and 74% (214/289) were treated using PDS, resulting in a decrease in HbA1c [7.10±1.06 to 6.73±0.82%; p<.01; change in HbA1ct0-t12 months -0.37 (95% confidence interval -0.46 to -0.28)] and MSG (7.7±1.6 versus 7.4±1.2 mmol/liter; p=.003) within one year. Glucose variability was also reduced, as indicated by lower high blood glucose index (p=.001), Glycemic Risk Assessment Diabetes Equation (p=.009), and time of hyper-glycemia (p=.003). Low blood glucose index and time spent in hypoglycemia were not affected. In contrast, nonuse of PDS (75/289) resulted in increased HbA1c (p<.001). Diabetiva outcome was strongly related to baseline HbA1c (HbA1ct0; p<.01) and use of PDS (p<.01). Acceptance of PDS was dependent on HbA1ct0 (p=.049).

Conclusions: Personalized decision support has potential to improve metabolic outcome in routine diabetes care.

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Figures

Figure 1
Figure 1
Design of the observational study of the Diabetiva program. To evaluate the impacted of PDS, the metabolic outcome was analyzed and compared retrospectively in patients receiving CGM twice, one at baseline and one after 12 months, and grouped in user or nonuser of PDS.
Figure 2
Figure 2
Comparison of HbA1ct0 and 12 months later in patients (A) treated with PDS and (B) treated conventional without use of PDS. For each patient, HbA1ct0 (x axis) is plotted versus HbA1c after one year (y axis) treated by use or nonuse of KADIS-based PDS. The diagonal area represents HbA1ct0 ± 0.5%. Green dots are patients in whom the HbA1c decreased over 0.5%. Red dots represent patients were the HbA1c increased >0.5%.
Figure 3
Figure 3
Outcome of the KADIS-based PDS in dependence of HbA1ct0. Comparison is given for patients with use (left) or nonuse (right) of PDS. HbA1ct0 is grouped into <6.5, 6.5–7.0, 7.0–7.5, 7.5–8.0, and >8.0. The asterisks represent p < .05 versus HbA1ct0.
See this image and copyright information in PMC

References

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