ArticleSensors (Basel, Switzerland)2017
Continuous Glucose Monitoring Enables the Detection of Losses in Infusion Set Actuation (LISAs).
Article in Sensors (Basel, Switzerland), 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 10 papers.
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The trial behind it
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Who cites it
10 citing papers in PubMed, 24 citations in OpenAlex.
- Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas.Journal of diabetes science and technology · 2018Trial
- Application of Zone Model Predictive Control Artificial Pancreas During Extended Use of Infusion Set and Sensor: A Randomized Crossover-Controlled Home-Use Trial.Diabetes care · 2017Trial
- Machine Learning-Based Anomaly Detection Algorithms to Alert Patients Using Sensor Augmented Pump of Infusion Site Failures.Journal of diabetes science and technology · 2022Article
- Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives.Journal of diabetes science and technology · 2021Review
- A Hybrid Automata Approach for Monitoring the Patient in the Loop in Artificial Pancreas Systems.Sensors (Basel, Switzerland) · 2021Article
- Realizing a Closed-Loop (Artificial Pancreas) System for the Treatment of Type 1 Diabetes.Endocrine reviews · 2019Review
- Detection of Insulin Pump Malfunctioning to Improve Safety in Artificial Pancreas Using Unsupervised Algorithms.Journal of diabetes science and technology · 2019Article
- Enabling the Internet of Mobile Crowdsourcing Health Things: A Mobile Fog Computing, Blockchain and IoT Based Continuous Glucose Monitoring System for Diabetes Mellitus Research and Care.Sensors (Basel, Switzerland) · 2019Article
- Classification of Postprandial Glycemic Status with Application to Insulin Dosing in Type 1 Diabetes-An In Silico Proof-of-Concept.Sensors (Basel, Switzerland) · 2019Article
- Insulin Infusion Sets and Continuous Glucose Monitoring Sensors: Where the Artificial Pancreas Meets the Patient.Diabetes technology & therapeutics · 2017Article
Corrections and comments
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Authors and funding
9 authors at 3 institutions in 1 country.
Funding
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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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.