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Cited by 26

Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs)

Howsmon, Daniel, Cameron, Faye, Baysal, Nihat, Ly, Trang, Forlenza, Gregory, Maahs, David, Buckingham, Bruce, Hahn, Juergen +1 more·2017·Sensors
DOI 10.3390/s17010161PMC PMC5298734
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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.

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  • The retrospective SAP sensitivity of 73.3% with 0.27 FPs/day agrees closely with previous results.

    “agreeing closely with previous results 22 (Table 3)”

    From Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas (2018) · cites this paper as [22]

  • Previous studies in the literature have retrospectively evaluated SF detection in both open-loop and closed-loop scenarios, primarily in simulation.

    “Previous studies in the literature have retrospectively evaluated SF detection in both open-loop and closed-loop scenarios, primarily in simulation.”

    From Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas (2018) · cites this paper as [22]

  • The similarity in the results from the real-time analysis presented herein and the retrospective analysis conducted previously helps to mitigate concerns surrounding the retrospective determination of the start of SFs.

    “The similarity in the results from the real-time analysis presented herein and the retrospective analysis conducted previously 22 helps to mitigate concerns surrounding the retrospective determination of the start of SFs.”

    From Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas (2018) · cites this paper as [22]

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Research works printed but not linked: 22

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Authors
Howsmon, D., Cameron, F., Baysal, N., Ly, T., Forlenza, G., Maahs, D., Buckingham, B., Hahn, J., & Bequette, B.
Affiliations
  • Department of Chemical & Biological Engineering, Rensselaer Polytechnic Institute, Troy, NY 12180, USA
  • Stanford University School of Medicine, Stanford, CA 94305, USA
  • Barbara Davis Center for Childhood Diabetes, University of Colorado Denver, Aurora, CO 80045, USA
ORCID
Venue
Sensors
Publisher
MDPI AG
Published
2017-01-15
Volume
17
Issue
1
Pages
161
ISSN
1424-8220
DOI
10.3390/s17010161
PMC
PMC5298734
Cited by
26
References
3
Language
en
Licence
creativecommons.org/licenses/by/4.0/ (vor)

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