Diabetes Technological Revolution: Winners and Losers?

Over recent years there has been an explosion in availability of technical devices to support diabetes self-management. But with this technology revolution comes new hurdles. On paper, the available diabetes technologies should mean that the vast majority of people with type 1 diabetes have optimal glycemic control and are using their preferred therapy choices. Yet, it does not appear to be universally the case. In parallel, suboptimal glycemic control remains stubbornly widespread. Barriers to improvement include access to technology, access to expert diabetes health care professionals, and prohibitive insurance costs. Until access can be improved to ensure the technologies are available and usable by those that need them, there are many people with diabetes who are still losing out.

Economic Model Predictive Control of Bihormonal Artificial Pancreas System Based on Switching Control and Dynamic R-parameter.

BACKGROUND: Blood glucose (BG) regulation is a long-term task for people with diabetes. In recent years, more and more researchers have attempted to achieve automated regulation of BG using automatic control algorithms, called the artificial pancreas (AP) system. In clinical practice, it is equally important to guarantee the treatment effect and reduce the treatment costs. The main motivation of this study is to reduce the cure burden. METHODS: The dynamic R-parameter economic model predictive control (R-EMPC) is chosen to regulate the delivery rates of exogenous hormones (insulin and glucagon). It uses particle swarm optimization (PSO) to optimize the economic cost function and the switching logic between insulin delivery and glucagon delivery is designed based on switching control theory. RESULTS: The proposed method is first tested on the standard subject; the result is compared with the switching PID and the switching MPC. The effect of the dynamic R-parameter on improving the control performance is illustrated by comparing the results of the EMPC and the R-EMPC. Finally, the robustness tests on meal change (size and timing), hormone sensitivity (insulin and glucagon), and subject variability are performed. All results show that the proposed method can improve the control performance and reduce the economic costs. CONCLUSIONS: The simulation results verify the effectiveness of the proposed algorithm on improving the tracking performance, enhancing robustness, and reducing economic costs. The method proposed in this study owns great worth in practical application.

Pathway to artificial pancreas systems revisited: moving downstream.

Artificial pancreas (AP) systems, a long-sought quest to replicate mechanically islet physiology that is lost in diabetes, are reaching the clinic, and the potential of automating insulin delivery is about to be realized. Significant progress has been made, and the safety and feasibility of AP systems have been demonstrated in the clinical research center and more recently in outpatient "real-world" environments. An iterative road map to AP system development has guided AP research since 2009, but progress in the field indicates that it needs updating. While it is now clear that AP systems are technically feasible, it remains much less certain that they will be widely adopted by clinicians and patients. Ultimately, the true success of AP systems will be defined by successful integration into the diabetes health care system and by the ultimate metric: improved diabetes outcomes.

Glucagon in the artificial pancreas: supply and marketing challenges.

The use of glucagon, in conjunction with insulin, in a dual chamber pump (artificial pancreas, AP) is a working goal for multiple companies and researchers. However, capital investment to create, operate, and maintain facilities with sufficient scale to produce enough glucagon to treat millions of patients, at a level of profit that makes it feasible, will be substantial. It can be assumed that the marketplace will expect the daily cost of glucagon (to the consumer) to be similar to the daily cost of insulin. After one subtracts wholesaler and pharmacy markup, there may be very few dollars remaining for the drug company to cover profit, capital expenditures, marketing, burden, and other costs. Without the potential for adequate margins, manufacturers may not be willing to take the risk. Assuming that the projections discussed in this article are in the right ballpark, advance planning for the supply for glucagon needs to start today and not wait for the AP to come to market.

Substantial Medicare savings may result if insurers cover 'artificial pancreas' sooner for diabetes patients.

Technologies to improve diabetes care have advanced considerably with the introduction of the insulin pump and continuous glucose monitoring. These two technologies are now being joined and enhanced to create an artificial pancreas. The current study models the impact of the artificial pancreas on clinical results and costs over time, based on early results from clinical trials. The modeling shows that insurers' coverage of the cost of an artificial pancreas at a relatively early point in the life of a patient with diabetes would greatly reduce future complications of the disease and spending needed to treat such complications. Projected Medicare savings are $937 million in nominal dollars after twenty-five years. The results of this analysis support conducting a more comprehensive trial to assess the long-term impact of the artificial pancreas on glucose levels and the technology's related costs.

Effect of intensive insulin therapy using a closed-loop glycemic control system in hepatic resection patients: a prospective randomized clinical trial.

OBJECTIVE Intensive insulin therapy (IIT) reduces morbidity and mortality in patients in surgical intensive care units. The aim of this study is to assess the effect of IIT using a closed-loop system in hepatectomized patients. RESEARCH DESIGN AND METHODS Patients were randomly assigned to receive IIT using a closed-loop system: an artificial pancreas (AP group) or conventional insulin therapy using the sliding-scale method (SS group). RESULTS The incidence of surgical-site infection in the AP group was significantly lower than that in the SS group. The length of hospitalization required for patients in the AP group was significantly shorter than that in the SS group. CONCLUSIONS Total hospital costs for patients in the AP group were significantly lower than for patients in the SS group. IIT using a closed-loop system maintained near-normoglycemia and contributed to a reduction in the incidence of SSI and total hospital costs due to shortened hospitalization.