title: Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs).
abstract: Reliable continuous glucose monitoring (CGM) enables a variety of advanced technology for the treatment of type 1 diabetes. In addition to artificial pancreas algorithms that use CGM to automate continuous subcutaneous insulin infusion (CSII), CGM can also inform fault detection algorithms that alert patients to problems in CGM or CSII. Losses in infusion set actuation (LISAs) can adversely affect clinical outcomes, resulting in hyperglycemia due to impaired insulin delivery. Prolonged hyperglycemia may lead to diabetic ketoacidosis-a serious metabolic complication in type 1 diabetes. Therefore, an algorithm for the detection of LISAs based on CGM and CSII signals was developed to improve patient safety. The LISA detection algorithm is trained retrospectively on data from 62 infusion set insertions from 20 patients. The algorithm collects glucose and insulin data, and computes relevant fault metrics over two different sliding windows; an alarm sounds when these fault metrics are exceeded. With the chosen algorithm parameters, the LISA detection strategy achieved a sensitivity of 71.8% and issued 0.28 false positives per day on the training data. Validation on two independent data sets confirmed that similar performance is seen on data that was not used for training. The developed algorithm is able to effectively alert patients to possible infusion set failures in open-loop scenarios, with limited evidence of its extension to closed-loop scenarios.
authors.affiliation: Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. howsmd@rpi.edu.
authors.forename: Daniel P
authors.initials: DP
authors.lastname: Howsmon
authors.affiliation: Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. fmccamer@gmail.com.
authors.forename: Faye
authors.initials: F
authors.lastname: Cameron
authors.affiliation: Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. baysan@rpi.edu.
authors.forename: Nihat
authors.initials: N
authors.lastname: Baysal
authors.affiliation: Stanford University School of Medicine, Stanford, CA 94305, USA. tly@insulet.com.
authors.forename: Trang T
authors.initials: TT
authors.lastname: Ly
authors.affiliation: Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO 80045, USA. gregory.forlenza@ucdenver.edu.
authors.forename: Gregory P
authors.initials: GP
authors.lastname: Forlenza
authors.affiliation: Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO 80045, USA. dmaahs@stanford.edu.
authors.forename: David M
authors.initials: DM
authors.lastname: Maahs
authors.affiliation: Stanford University School of Medicine, Stanford, CA 94305, USA. buckingham@stanford.edu.
authors.forename: Bruce A
authors.initials: BA
authors.lastname: Buckingham
authors.affiliation: Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. hahnj@rpi.edu.|Department of Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. hahnj@rpi.edu.
authors.forename: Juergen
authors.initials: J
authors.lastname: Hahn
authors.affiliation: Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA. bequette@rpi.edu.
authors.forename: B Wayne
authors.initials: BW
authors.lastname: Bequette
mesh_terms: D001786:Blood Glucose; D015190:Blood Glucose Self-Monitoring; D003922:Diabetes Mellitus, Type 1; D006801:Humans; D007004:Hypoglycemic Agents; D007328:Insulin; D007332:Insulin Infusion Systems
chemical_list: D001786:Blood Glucose; D007004:Hypoglycemic Agents; D007328:Insulin
keywords: continuous subcutaneous insulin infusion; fault detection; sensor-augmented pump; type 1 diabetes