Background and Aims

Patients with type 1 diabetes (T1D) make their decisions for insulin delivery from available blood glucose (BG) data and the expected effects on BG of forthcoming meals and activities according to education rules and their own experience. Enriched information on predicted BG glucose evolution could help them in better tuning insulin therapy. CDDIAB study’s objective was to evaluate the relevance of predicted BG trends in the decision-making process of the patient.

Methods

Eight patients (8F/6M, age: 51+/-15, T1D duration: 26+/-17 years, HbA1c: 7.09+/-0.82%) volunteered to track BG using a CGM device, meal intakes and insulin doses in real life conditions. The study ran over 30 days, and no specific intervention on the usual treatment was undertaken. Collected data has been used to train predictive models for each patient, in order to estimate future BG fluctuations up to 90 minutes. For each patient, low and high BG events were extracted, preditions were computed and patients were asked to make therapeutic decisions.

Results

Results were analysed by diabetologists of Montpellier Hospital, in order to evaluate the relevance of patients’ therapeutic decisions with prediction versus without:

Conclusions

Results show that in 84% of the cases presented to patients, prediction data drives better decision-making, and give insight into the benefits of implementing this technology in an open-loop system: predicted BG curve is a relevant and easy-to-read information to support decision making process.

The next step will be to test a decision support system, based on our prediction algorithms, which will provide therapeutic advices directly to the patient.