Background: Insulin infusion set failure resulting in prolonged hyperglycemia or diabetic ketoacidosis can occur with pump therapy in type 1 diabetes. Set failures are frequently characterized by variable and unpredictable patterns of increasing glucose values despite increased insulin infusion. Early detection may minimize the risk of prolonged hyperglycemia, an important consideration for automated insulin delivery and closed-loop applications. Methods: A novel algorithm designed to alert the patient to the onset of infusion set failure was developed based upon continuous glucose sensor values and insulin delivered from an insulin pump. The method was calibrated on 12 weeks of infusion set wear without failures recorded by 4 patients in ambulatory conditions and prospectively validated on 18 weeks of infusion set wear with and without failures belonging to 9 other subjects in ambulatory conditions. Results: The algorithm, evaluated retrospectively, identified a failure 2.52 ± 1.91 days ahead of the actual event as recorded by the clinical team, corresponding to 50% sensitivity, 66% specificity and 55% accuracy. If set failure alarms had been activated in real time, the average time >180 mg/dl would be reduced from 82.7 ± 40.9 hours/week/subject (without alarm) to 58.8 ± 31.1 hours/week/subject (with alarm), corresponding to a potential 29% reduction in time spent >180mg/dl. Conclusion: The proposed method for early detection of infusion set failure based on glucose sensor and insulin data demonstrated favorable results on retrospective data and may be implemented as an additional safeguard in a future fully automated closed-loop system.
Diabetes Mellitus is a chronic desease characterized by the inability of the organism to autonomously regulate the blood glucose level due to insulin deficiency or resistance, thus leading to serious health damages. In order to keep tight glucose control treatment happens to be based essentially on insulin delivery. In common practise, a main limiting factor in adequate choice of insulin dosing is the lack of reliable predictions of blood glucose evolution. In this work, state-space models of various orders were investigated and evaluated for their capability of description of one diabetic subject blood glucose evolution over 24 hours. It turned out that models of low complexity are capable of detecting even abrupt changes in calibration data but they are not sufficient for validation data. Blood glucose levels could be predicted fairly well up to 30 minutes ahead on validation data.
Abstract-Using methods of system identification and prediction, we investigate near-future prediction of individualspecific T1DM blood glucose dynamics with the purpose of a decision-making tool development in diabetes treatment. Two strategies were approached: Firstly, Kalman estimators based on identified state-space models were designed; Secondly, direct identification of ARX-and ARMAX-based predictors was done. Predictions over 30 minutes look-ahead were capable to track glucose variation even in sensible ranges for estimation data, but not on validation data.
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