A Novel Method to Detect Pressure-Induced Sensor Attenuations (PISA) in an Artificial Pancreas. (November 2014)

• Record Type: Journal Article
• Authors: Baysal, Nihat; Cameron, Fraser; Buckingham, Bruce A.; Wilson, Darrell M.; Chase, H. Peter; Maahs, David M.; Bequette, B. Wayne
• Other Names: Buckingham Bruce A. non-byline-author.; Wilson Darrell M. non-byline-author.; Aye Tandy non-byline-author.; Clinton Paula non-byline-author.; Harris Breanne P. non-byline-author.; Chase H. Peter non-byline-author.; Maahs David M. non-byline-author.; Slover Robert non-byline-author.; Wadwa Paul non-byline-author.; Realsen Jaime non-byline-author.; Messer Laurel non-byline-author.; Hramiak Irene non-byline-author.; Paul Terri non-byline-author.; Tereschyn Sue non-byline-author.; Driscoll Marsha non-byline-author.; Bequette B. Wayne non-byline-author.; Cameron Fraser non-byline-author.; Baysal Nihat non-byline-author.; Beck Roy W. non-byline-author.; Lum John non-byline-author.; Kollman Craig non-byline-author.; Calhoun Peter non-byline-author.; Sibayan Judy non-byline-author.; Njeru Nelly M. non-byline-author.; Sauer Werner non-byline-author.; Lott Jennifer non-byline-author.; Pickup John C. non-byline-author.; Hirsch Irl non-byline-author.; Wolpert Howard non-byline-author.
• Is Part Of: Journal of diabetes science and technology. Volume 8:Number 6(2014)
• Page Start: 1091
• Page End: 1096
• Publication Date: 2014-11
• Subjects: algorithms -- artificial pancreas -- continuous glucose monitor -- fault detection -- hypoglycemia -- sensor attenuations; Diabetes -- Periodicals; Medical technology -- Periodicals; Diabetes Mellitus -- Periodicals; 616.462005
• DOI: 10.1177/1932296814553267
• Languages: English
• ISSNs: 1932-2968

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.