ArticleJournal of diabetes science and technology2014
A novel method to detect pressure-induced sensor attenuations (PISA) in an artificial pancreas.
Article in Journal of diabetes science and technology, 2014. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers, 1 of them a synthesis that pooled it.
What it found
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The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
34 citing papers in PubMed, 1 synthesis or guideline pooled it, 74 citations in OpenAlex.
- Continuous Glucose Monitors and Automated Insulin Dosing Systems in the Hospital Consensus Guideline.Journal of diabetes science and technology · 2020Guideline
- A randomized crossover trial assessing time of day snack consumption and resulting postprandial glycemic response in a real-life setting among healthy adults.Chronobiology international · 2022Trial
- Efficacy of an Overnight Predictive Low-Glucose Suspend System in Relation to Hypoglycemia Risk Factors in Youth and Adults With Type 1 Diabetes.Journal of diabetes science and technology · 2016Trial
- Predictive Low-Glucose Insulin Suspension Reduces Duration of Nocturnal Hypoglycemia in Children Without Increasing Ketosis.Diabetes care · 2015Trial
- Article
- Metabolic Models, in Silico Trials, and Algorithms.Diabetes technology & therapeutics · 2025Review
- Metabolic Models, in Silico Trials, and Algorithms.Journal of diabetes science and technology · 2025Review
- Simulating Realistic Continuous Glucose Monitor Time Series By Data Augmentation.Journal of diabetes science and technology · 2025Article
- Continuous glucose monitoring (Dexcom g6) in people with diabetes after kidney transplantation.Renal failure · 2024Article
- Predicting Dysglycemia in Patients with Diabetes Using Electrocardiogram.Diagnostics (Basel, Switzerland) · 2024Article
- Congenital hyperinsulinism in infancy and childhood: challenges, unmet needs and the perspective of patients and families.Orphanet journal of rare diseases · 2022Review
- 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
- Distributed sensor and actuator networks for closed-loop bioelectronic medicine.Materials today (Kidlington, England) · 2021Article
- Continuous Glucose Monitoring in the Hospital.Endocrinology and metabolism (Seoul, Korea) · 2021Review
- Performance of three different continuous glucose monitoring systems in children with type 1 diabetes during a diabetes summer camp.Pediatric diabetes · 2021Article
- A Review of Continuous Glucose Monitoring Data Interpretation in the Age of Automated Insulin Delivery.Journal of diabetes science and technology · 2019Review
- Article
- Multi-level Supervision and Modification of Artificial Pancreas Control System.Computers & chemical engineering · 2018Article
- A Prospective Multicenter Evaluation of the Accuracy of a Novel Implanted Continuous Glucose Sensor: PRECISE II.Diabetes technology & therapeutics · 2018Article
Corrections and comments
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Authors and funding
8 authors at 2 institutions in 1 country.
Funding
Abstract
Continuous glucose monitors (CGMs) provide real-time interstitial glucose concentrations that are essential for automated treatment of individuals with type 1 diabetes. Miscalibration, noise spikes, dropouts, or pressure applied to the site (e.g., lying on the site while sleeping) can cause inaccurate glucose signals, which could lead to inappropriate insulin dosing decisions. These studies focus on the problem of pressure-induced sensor attenuations (PISAs) that occur overnight and can cause undesirable pump shut-offs in a predictive low glucose suspend system. The algorithm presented here uses real-time CGM readings without knowledge of meals, insulin doses, activity, sensor recalibrations, or fingerstick measurements. The real-time PISA detection technique was tested on outpatient "in-home" data from a predictive low-glucose suspend trial with over 1125 nights of data. A total of 178 sets were created by using different parameters for the PISA detection algorithm to illustrate its range of available performance. The tracings were reviewed via a web-based analysis tool by an engineer with an extensive expertise on analyzing clinical datasets and ~3% of the CGM readings were marked as PISA events which were used as the gold standard. It is shown that 88.34% of the PISAs were successfully detected by the algorithm, and the percentage of false detections could be reduced to 1.70% by altering the algorithm parameters. Use of the proposed PISA detection method can result in a significant decrease in undesirable pump suspensions overnight, and may lead to lower overnight mean glucose levels while still achieving a low risk of hypoglycemia.
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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.