Marzia Cescon1, Daniel J DeSalvo2, Trang T Ly3, David M Maahs4, Laurel H Messer4, Bruce A Buckingham3, Francis J Doyle1,5, Eyal Dassau6,5. 1. Department Chemical Engineering & Institute for Collaborative Biotechnologies, University of California, Santa Barbara, Santa Barbara, CA, USA. 2. Pediatric Diabetes and Endocrinology, Baylor College of Medicine, Texas Children's Hospital, Houston, TX, USA. 3. Division of Pediatric Endocrinology, Stanford School of Medicine, Stanford, CA, USA. 4. Barbara Davis Center for Diabetes, University of Colorado Denver, Aurora, CO, USA. 5. Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA. 6. Department Chemical Engineering & Institute for Collaborative Biotechnologies, University of California, Santa Barbara, Santa Barbara, CA, USA dassau@seas.harvard.edu.
Abstract
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.
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 belongingto 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.
Authors: Tao Li; Allan T Evans; Srinivas Chiravuri; Roma Y Gianchandani; Yogesh B Gianchandani Journal: Adv Drug Deliv Rev Date: 2012-05-08 Impact factor: 15.470
Authors: I Guilhem; B Balkau; F Lecordier; J-M Malécot; S Elbadii; A-M Leguerrier; J-Y Poirier; C Derrien; F Bonnet Journal: Diabetologia Date: 2009-10-16 Impact factor: 10.122
Authors: Andrea Facchinetti; Simone Del Favero; Giovanni Sparacino; Claudio Cobelli Journal: IEEE Trans Biomed Eng Date: 2012-11-15 Impact factor: 4.538
Authors: Daniel P Howsmon; Faye Cameron; Nihat Baysal; Trang T Ly; Gregory P Forlenza; David M Maahs; Bruce A Buckingham; Juergen Hahn; B Wayne Bequette Journal: Sensors (Basel) Date: 2017-01-15 Impact factor: 3.576