Enhanced 911/global position system wizard: a telemedicine application for the prevention of severe hypoglycemia--monitor, alert, and locate

(Hybrid) Closed-Loop Systems: From Announced to Unannounced Exercise

Physical activity and exercise have many beneficial effects on general and type 1 diabetes (T1D) specific health and are recommended for individuals with T1D. Despite these health benefits, many people with T1D still avoid exercise since glycemic management during physical activity poses substantial glycemic and psychological challenges - which hold particularly true for unannounced exercise when using an AID system. Automated insulin delivery (AID) systems have demonstrated their efficacy in improving overall glycemia and in managing announced exercise in numerous studies. They are proven to increase time in range (70-180 mg/dL) and can especially counteract nocturnal hypoglycemia, even when evening exercise was performed. AID-systems consist of a pump administering insulin as well as a CGM sensor (plus transmitter), both communicating with a control algorithm integrated into a device (insulin pump, mobile phone/smart watch). Nevertheless, without manual pre-exercise adaptions, these systems still face a significant challenge around physical activity. Automatically adapting to the rapidly changing insulin requirements during unannounced exercise and physical activity is still the Achilles' heel of current AID systems. There is an urgent need for improving current AID-systems to safely and automatically maintain glucose management without causing derailments - so that going forward, exercise announcements will not be necessary in the future. Therefore, this narrative literature review aimed to discuss technological strategies to how current AID-systems can be improved in the future and become more proficient in overcoming the hurdle of unannounced exercise. For this purpose, the current state-of-the-art therapy recommendations for AID and exercise as well as novel research approaches are presented along with potential future solutions - in order to rectify their deficiencies in the endeavor to achieve fully automated AID-systems even around unannounced exercise.

Design and Clinical Evaluation of the Interoperable Artificial Pancreas System (iAPS) Smartphone App: Interoperable Components with Modular Design for Progressive Artificial Pancreas Research and Development

BACKGROUND

There is an unmet need for a modular artificial pancreas (AP) system for clinical trials within the existing regulatory framework to further AP research projects from both academia and industry. We designed, developed, and tested the interoperable artificial pancreas system (iAPS) smartphone app that can interface wirelessly with leading continuous glucose monitors (CGM), insulin pump devices, and decision-making algorithms while running on an unlocked smartphone.

METHODS

After algorithm verification, hazard and mitigation analysis, and complete system verification of iAPS, six adults with type 1 diabetes completed 1 week of sensor-augmented pump (SAP) use followed by 48 h of AP use with the iAPS, a Dexcom G5 CGM, and either a Tandem or Insulet insulin pump in an investigational device exemption study. The AP system was challenged by participants performing extensive walking without exercise announcement to the controller, multiple large meals eaten out at restaurants, two overnight periods, and multiple intentional connectivity interruptions.

RESULTS

Even with these intentional challenges, comparison of the SAP phase with the AP study showed a trend toward improved time in target glucose range 70-180 mg/dL (78.8% vs. 83.1%; P = 0.31), and a statistically significant reduction in time below 70 mg/dL (6.1% vs. 2.2%; P = 0.03). The iAPS system performed reliably and showed robust connectivity with the peripheral devices (99.8% time connected to CGM and 94.3% time in closed loop) while requiring limited user intervention.

CONCLUSIONS

The iAPS system was safe and effective in regulating glucose levels under challenging conditions and is suitable for use in unconstrained environments.

Variables to Be Monitored via Biomedical Sensors for Complete Type 1 Diabetes Mellitus Management: An Extension of the "On-Board" Concept

Type 1 diabetes mellitus (DM1) is a growing disease, and a deep understanding of the patient is required to prescribe the most appropriate treatment, adjusted to the patient's habits and characteristics. Before now, knowledge regarding each patient has been incomplete, discontinuous, and partial. However, the recent development of continuous glucose monitoring (CGM) and new biomedical sensors/gadgets, based on automatic continuous monitoring, offers a new perspective on DM1 management, since these innovative devices allow the collection of 24-hour biomedical data in addition to blood glucose levels. With this, it is possible to deeply characterize a diabetic person, offering a better understanding of his or her illness evolution, and, going further, develop new strategies to manage DM1. This new and global monitoring makes it possible to extend the "on-board" concept to other features. This well-known approach to the processing of variable "insulin" describes some inertias and aggregated/remaining effects. In this work, such analysis is carried out along with a thorough study of the significant variables to be taken into account/monitored-and how to arrange them-for a deep characterization of diabetic patients. Lastly, we present a case study evaluating the experience of the continuous and comprehensive monitoring of a diabetic patient, concluding that the huge potential of this new perspective could provide an acute insight into the patient's status and extract the maximum amount of knowledge, thus improving the DM1 management system in order to be fully functional.

Continuous glucose monitoring: A review of the technology and clinical use

Continuous glucose monitoring (CGM) is an increasingly adopted technology for insulin-requiring patients that provides insights into glycemic fluctuations. CGM can assist patients in managing their diabetes with lifestyle and medication adjustments. This article provides an overview of the technical and clinical features of CGM based on a review of articles in PubMed on CGM from 1999 through January 31, 2017. A detailed description is presented of three professional (retrospective), three personal (real-time) continuous glucose monitors, and three sensor integrated pumps (consisting of a sensor and pump that communicate with each other to determine an optimal insulin dose and adjust the delivery of insulin) that are currently available in United States. We have reviewed outpatient CGM outcomes, focusing on hemoglobin A1c (A1C), hypoglycemia, and quality of life. Issues affecting accuracy, detection of glycemic variability, strategies for optimal use, as well as cybersecurity and future directions for sensor design and use are discussed. In conclusion, CGM is an important tool for monitoring diabetes that has been shown to improve outcomes in patients with type 1 diabetes mellitus. Given currently available data and technological developments, we believe that with appropriate patient education, CGM can also be considered for other patient populations.

A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps

The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes "connected" technologies--such as smartphone apps, and wearable devices and sensors--which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in "the cloud" for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care.

Design and in silico evaluation of an intraperitoneal-subcutaneous (IP-SC) artificial pancreas

Prandial glucose regulation is a major challenge for the artificial pancreas using subcutaneous insulin (without a feedforward bolus) due to insulin's slow absorption-peak (50-60 min). Intraperitoneal insulin, with a fast absorption peak (20-25 min), has been suggested as an alternative to address these limitations. An artificial pancreas using intraperitoneal insulin was designed and evaluated on 100 in silico subjects and compared with two designs using subcutaneous insulin with and without a feedforward bolus, following the three meal (40-70 g-carbohydrates) evaluation protocol. The design using intraperitoneal insulin resulted in a significantly lower postprandial blood glucose peak (38 mg/dL) and longer time in the clinically accepted region (13%) compared to the design using subcutaneous insulin without a feedforward bolus and comparable results to a subcutaneous feedforward bolus design. This superior regulation with minimal user interaction may improve the quality of life for people with type 1 diabetes mellitus.

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 remote monitoring system for artificial pancreas support is safe, reliable, and user friendly

INTRODUCTION

We developed a unique remote monitoring and control diabetes management system (MDRS) supporting the use of an artificial pancreas (AP) at home. In this study, we aimed at assessing the efficacy and safety of the MDRS and its ability to prevent or shorten nocturnal hypoglycemia episodes without the use of an AP, as well as evaluating parental attitudes toward the use of such a system in the future.

MATERIALS AND METHODS

This was a prospective, case control, randomized study. Eighteen patients treated nightly over a 2-week period by the MDRS were compared with 19 patients treated with sensor-augmented pump (SAP) therapy. We analyzed the performance of the MDRS, the number and duration of hypo- or hyperglycemia episodes, and the efficacy of the MDRS in detecting them. Parents of the study participants completed the hypoglycemia fear survey, attitude, and satisfaction questionnaires.

RESULTS

The MDRS allows continuous monitoring of the patients using it. Without the use of an AP, the MDRS did not significantly prevent nocturnal hypoglycemia episodes. The patients and their parents found the system reliable and user-friendly. The overall impression of the MDRS users was favorable, with a keen interest of the patients and their caregivers to use such a system in the future.

CONCLUSIONS

The MDRS is reliable and safe remote monitoring system for AP at-home systems. Remote monitoring of type 1 diabetes patients treated with SAP therapy is a promising and feasible task that is highly anticipated by patients and their caregivers.

The artificial pancreas: current status and future prospects in the management of diabetes

Recent advances in insulins, insulin pumps, continuous glucose-monitoring systems, and control algorithms have resulted in an acceleration of progress in the development of artificial pancreas devices. This review discusses progress in the development of external systems that are based on subcutaneous drug delivery and subcutaneous continuous glucose monitoring. There are two major system-level approaches to achieving closed-loop control of blood glucose in diabetic individuals. The unihormonal approach uses insulin to reduce blood glucose and relies on complex safety mitigation algorithms to reduce the risk of hypoglycemia. The bihormonal approach uses both insulin to lower blood glucose and glucagon to raise blood glucose, and also relies on complex algorithms to provide for safety of the user. There are several major strategies for the design of control algorithms and supervision control for application to the artificial pancreas: proportional-integral-derivative, model predictive control, fuzzy logic, and safety supervision designs. Advances in artificial pancreas research in the first decade of this century were based on the ongoing computer revolution and miniaturization of electronic technology. The advent of modern smartphones has created the ability to utilize smartphone technology as the engineering centerpiece of an artificial pancreas. With these advances, an artificial or bionic pancreas is within reach.

Closed-loop artificial pancreas systems: engineering the algorithms

In this two-part Bench to Clinic narrative, recent advances in both the preclinical and clinical aspects of artificial pancreas (AP) development are described. In the preceding Bench narrative, Kudva and colleagues provide an in-depth understanding of the modified glucoregulatory physiology of type 1 diabetes that will help refine future AP algorithms. In the Clinic narrative presented here, we compare and evaluate AP technology to gain further momentum toward outpatient trials and eventual approval for widespread use. We enumerate the design objectives, variables, and challenges involved in AP development, concluding with a discussion of recent clinical advancements. Thanks to the effective integration of engineering and medicine, the dream of automated glucose regulation is nearing reality. Consistent and methodical presentation of results will accelerate this success, allowing head-to-head comparisons that will facilitate adoption of the AP as a standard therapy for type 1 diabetes.

In silico evaluation of an artificial pancreas combining exogenous ultrafast-acting technosphere insulin with zone model predictive control

BACKGROUND

Because of the slow pharmacokinetics of subcutaneous (SC) insulin, avoiding postprandial hyperglycemia has been a major challenge for an artificial pancreas (AP) using SC insulin without a meal announcement.

METHODS

A semiautomated AP with Technosphere® Insulin (TI; MannKind Corporation, Valencia, CA) was designed to combine pulmonary and SC insulin. Manual inhalation of 10 U ultrafast-absorbing TI at mealtime delivers the first, or cephalic, phase of insulin, and an SC insulin pump controlled by zone model predictive controller delivers second-phase and basal insulin. This AP design was evaluated on 100 in silico subjects from the University of Virginia/Padova metabolic simulator using a protocol of two 50 g carbohydrate (CHO) meals and two 15 g CHO snacks.

RESULTS

Simulation analysis shows that the semiautomated AP with TI provides 32% and 16% more time in the controller target zone (80-140 mg/dl) during the 4 h postprandial period, with 39 and 20 mg/dl lower postprandial blood glucose peak on average than the pure feedback AP and the AP with manual feed-forward SC bolus, respectively. No severe hypoglycemia (<50 mg/dl) was observed in any cases.

CONCLUSIONS

The semiautomated AP with TI provides maximum time in the clinically accepted region when compared with pure feedback AP and AP with manual feed-forward SC bolus. Furthermore, the semiautomated AP with TI provides a flexible operation (optional TI inhalation) with minimal user interaction, where the controller design can be tailored to specific user needs and abilities to interact with the device.

Design of the health monitoring system for the artificial pancreas: low glucose prediction module

BACKGROUND

The purpose of this study was to design and evaluate a safety system for the artificial pancreas device system (APDS). Safe operation of the APDS is a critical task, where the safety system is engaged only as needed to ensure reliable operation without positive feedback to the controller.

METHODS

The Health Monitoring System (HMS) was designed as a modular system to ensure the safety of the APDS and the user. It was designed using a large set of ambulatory data and evaluated in silico by inducing hypoglycemia with a missed meal [bolus for a 65 g carbohydrate (CHO) meal] and administering rescue CHOs per HMS alerting. The HMS was validated in-clinic with a real-life challenge of a subject who overdosed insulin prior to admission.

RESULTS

The HMS was evaluated for clinical use with a 15 min prediction horizon. Retrospectively, 93.5% of episodes were detected with 2.9 false alarms per day. During in silico evaluation, the HMS reduced the time spent <70 mg/dl from 15% to 3%. When the HMS was first tested in-clinic, the subject overdosed ~3 U of insulin prior to her arrival to a closed-loop session (against protocol). The controller reduced insulin delivery, and the HMS gave four alerts that were successfully received via clinical software and text and multimedia messages. Even with insulin reduction and CHO supplements, hypoglycemia was unavoidable but manageable due to the HMS, confirming that a safety system to detect adverse events is an essential part of the APDS.

CONCLUSIONS

The ability of the HMS to be an effective alert system that provides a safety layer to the APDS controller has been demonstrated in a clinical setting.

Closing the loop

Closed-loop algorithms can be found in every aspect of everyday modern life. Automation and control are used constantly to provide safety and to improve quality of life. Closed-loop systems and algorithms can be found in home appliances, automobiles, aviation and more. Can one imagine nowadays driving a car without ABS, cruise control or even anti-sliding control? Similar principles of automation and control can be used in the management of diabetes mellitus (DM). The idea of an algorithmic/technological way to control glycaemia is not new and has been researched for more than four decades. However, recent improvements in both glucose-sensing technology and insulin delivery together with advanced control and systems engineering made this dream of an artificial pancreas possible. The artificial pancreas may be the next big step in the treatment of DM since the use of insulin analogues. An artificial pancreas can be described as internal or external devices that use continuous glucose measurements to automatically manage exogenous insulin delivery with or without other hormones in an attempt to restore glucose regulation in individuals with DM using a control algorithm. This device as described can be internal or external; can use different types of control algorithms with bi-hormonal or uni-hormonal design; and can utilise different ways to administer them. The different designs and implementations have transitioned recently from in silico simulations to clinical evaluation stage with practical applications in mind. This may mark the beginning of a new era in diabetes management with the introduction of semi-closed-loop systems that can prevent or minimise nocturnal hypoglycaemia, to hybrid systems that will manage blood glucose (BG) levels with minimal user intervention to finally fully automated systems that will take the user out of the loop. More and more clinical trials will be needed for the artificial pancreas to become a reality but initial encouraging results are proof that we are on the right track. We attempted to select recent publications that will present these current achievements in the quest for the artificial pancreas and that will inspire others to continue to progress this field of research.

Clinical hurdles and possible solutions in the implementation of closed-loop control in type 1 diabetes mellitus

From an engineering perspective, controlling blood glucose appears to be a fairly straightforward single input (glucose), single output (insulin) control problem. Unfortunately, mimicking Mother Nature turns out to be a complex endeavor. The primary hurdle in developing a useful, safe closed-loop control algorithm for an artificial pancreas is the time delays associated with current continuous glucose monitors and subcutaneously delivered insulins. This article will provide a brief history of the artificial pancreas, outline the main clinical hurdles restricting its current implementation, and list possible solutions for success.

Innovations in technology for the treatment of diabetes: clinical development of the artificial pancreas (an autonomous system)

The Food and Drug Administration in collaboration with the National Institutes of Health presented a public workshop to facilitate medical device innovation in the development of the artificial pancreas (or autonomous system) for the treatment of diabetes mellitus on November 10, 2010 in Gaithersburg, Maryland. The purpose of the workshop was to discuss four aspects of artificial pancreas research and development, including: (1) the current state of device systems for autonomous systems for the treatment of diabetes mellitus; (2) challenges in developing this expert device system using existing technology; (3) clinical expectations for these systems; and (4) development plans for the transition of this device system toward an outpatient setting. The patients discussed how clinical science, system components, and regulatory policies will all need to harmonize in order to achieve the goal of seeing an AP product brought forward to the marketplace for patients to use.

Closed-loop insulin delivery: from bench to clinical practice

Automated closed-loop insulin delivery, also referred to as the 'artificial pancreas', has been an important but elusive goal of diabetes treatment for many decades. Research milestones include the conception of continuous glucose monitoring in the early 1960s, followed by the production of the first commercial hospital-based artificial pancreas in the late 1970s that combined intravenous glucose sensing and insulin delivery. In the past 10 years, research into the artificial pancreas has gained substantial momentum and focused on the subcutaneous route for glucose measurement and insulin delivery, which reflects technological advances in interstitial glucose monitoring and the increasing use of the continuous subcutaneous insulin infusion. This Review discusses the design of an artificial pancreas, its components and clinical results, as well as the advantages and disadvantages of different types of automated closed-loop systems and potential future advances. The introduction of the artificial pancreas into clinical practice will probably occur gradually, starting with simpler approaches, such as overnight control of blood glucose concentration and temporary pump shut-off, that are adapted to more complex situations, such as glycemic control during meals and exercise.

Quest for the artificial pancreas: combining technology with treatment

The various components of the artificial pancreas puzzle are being put into place. Features such as communication, control, modeling, and learning are being realized presently. Steps have been set in motion to carry the conceptual design through simulation to clinical implementation. The challenging pieces still to be addressed include stress and exercise; as integral parts of the ultimate goal, effort has begun to shift toward overcoming the remaining hurdles to the full artificial pancreas. The artificial pancreas is close to becoming a reality, driven by technology, and the expectation that lives will be improved.

Real-Time hypoglycemia prediction suite using continuous glucose monitoring: a safety net for the artificial pancreas

OBJECTIVE

The purpose of this study was to develop an advanced algorithm that detects pending hypoglycemia and then suspends basal insulin delivery. This approach can provide a solution to the problem of nocturnal hypoglycemia, a major concern of patients with diabetes.

RESEARCH DESIGN AND METHODS

This real-time hypoglycemia prediction algorithm (HPA) combines five individual algorithms, all based on continuous glucose monitoring 1-min data. A predictive alarm is issued by a voting algorithm when a hypoglycemic event is predicted to occur in the next 35 min. The HPA system was developed using data derived from 21 Navigator studies that assessed Navigator function over 24 h in children with type 1 diabetes. We confirmed the function of the HPA using a separate dataset from 22 admissions of type 1 diabetic subjects. During these admissions, hypoglycemia was induced by gradual increases in the basal insulin infusion rate up to 180% from the subject's own baseline infusion rate. RESULTS Using a prediction horizon of 35 min, a glucose threshold of 80 mg/dl, and a voting threshold of three of five algorithms to predict hypoglycemia (defined as a FreeStyle plasma glucose readings <60 mg/dl), the HPA predicted 91% of the hypoglycemic events. When four of five algorithms were required to be positive, then 82% of the events were predicted.

CONCLUSIONS

The HPA will enable automated insulin-pump suspension in response to a pending event that has been detected prior to severe immediate complications.

The role of technology and the chronic care model

Innovative technological approaches offer great promise for enhancing the quality of care and improved access. A chronic care model has been shown repeatedly to improve outcomes. The elements of the model include the health system, community, self-management support, decision support, clinical information systems, and delivery system redesign. Understanding opportunities to apply technology to the chronic care model is critically important as the rates of diabetes escalate and quality care becomes a priority for health systems.