Susceptibility of interstitial continuous glucose monitor performance to sleeping position

Fault detection and safety in closed-loop artificial pancreas systems

Continuous subcutaneous insulin infusion pumps and continuous glucose monitors enable individuals with type 1 diabetes to achieve tighter blood glucose control and are critical components in a closed-loop artificial pancreas. Insulin infusion sets can fail and continuous glucose monitor sensor signals can suffer from a variety of anomalies, including signal dropout and pressure-induced sensor attenuations. In addition to hardware-based failures, software and human-induced errors can cause safety-related problems. Techniques for fault detection, safety analyses, and remote monitoring techniques that have been applied in other industries and applications, such as chemical process plants and commercial aircraft, are discussed and placed in the context of a closed-loop artificial pancreas.

A novel method to detect pressure-induced sensor attenuations (PISA) in an artificial pancreas

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.

Glucose sensing in the peritoneal space offers faster kinetics than sensing in the subcutaneous space

The paramount goal in the treatment of type 1 diabetes is the maintenance of normoglycemia. Continuous glucose monitoring (CGM) technologies enable frequent sensing of glucose to inform exogenous insulin delivery timing and dosages. The most commonly available CGMs are limited by the physiology of the subcutaneous space in which they reside. The very same advantages of this minimally invasive approach are disadvantages with respect to speed. Because subcutaneous blood flow is sensitive to local fluctuations (e.g., temperature, mechanical pressure), subcutaneous sensing can be slow and variable. We propose the use of a more central, physiologically stable body space for CGM: the intraperitoneal space. We compared the temporal response characteristics of simultaneously placed subcutaneous and intraperitoneal sensors during intravenous glucose tolerance tests in eight swine. Using compartmental modeling based on simultaneous intravenous sensing, blood draws, and intraarterial sensing, we found that intraperitoneal kinetics were more than twice as fast as subcutaneous kinetics (mean time constant of 5.6 min for intraperitoneal vs. 12.4 min for subcutaneous). Combined with the known faster kinetics of intraperitoneal insulin delivery over subcutaneous delivery, our findings suggest that artificial pancreas technologies may be optimized by sensing glucose and delivering insulin in the intraperitoneal space.