Safety of Outpatient Closed-Loop Control: First Randomized Crossover Trials of a Wearable Artificial Pancreas

Kovatchev, Boris; Renard, Éric; Cobelli, Claudio; Zisser, Howard; Keith-Hynes, Patrick; Anderson, Stacey M.; Brown, Sue A.; Cherñavvsky, Daniel R.; Breton, Marc D.; Mize, Lloyd B.; Farret, Anne; Place, Jérôme; Bruttomesso, Daniela; Favero, Simone Del; Boscari, Federico; Galasso, Silvia; Avogaro, Angelo; Magni, Lalo; Palma, Federico Di; Toffanin, Chiara; Messori, Mirko; Dassau, Eyal; Doyle, Francis J.

doi:10.2337/dc13-2076

Cited by 169 publications

(118 citation statements)

References 23 publications

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“…49 The superior glucose control achieved by these trials suggests that when CGM information is processed appropriately by advanced algorithms, the clinical outcomes are better, even than those achieved by state-of-the art sensoraugmented insulin pump therapy. 50,51 Thus, CGM data contain valuable information reaching beyond the episodic BG determinations provided by SMBG. At a certain level of CGM accuracy, this information should permit effective ''non-adjunct'' use of CGM as a replacement for SMBG.…”

Section: Utility Of Cgm Informationmentioning

confidence: 99%

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Kovatchev

Patek

Ortiz

et al. 2015

Diabetes Technology & Therapeutics

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191

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Background: The level of continuous glucose monitoring (CGM) accuracy needed for insulin dosing using sensor values (i.e., the level of accuracy permitting non-adjunct CGM use) is a topic of ongoing debate. Assessment of this level in clinical experiments is virtually impossible because the magnitude of CGM errors cannot be manipulated and related prospectively to clinical outcomes. Materials and Methods: A combination of archival data (parallel CGM, insulin pump, self-monitoring of blood glucose [SMBG] records, and meals for 56 pump users with type 1 diabetes) and in silico experiments was used to ''replay'' real-life treatment scenarios and relate sensor error to glycemic outcomes. Nominal blood glucose (BG) traces were extracted using a mathematical model, yielding 2,082 BG segments each initiated by insulin bolus and confirmed by SMBG. These segments were replayed at seven sensor accuracy levels (mean absolute relative differences [MARDs] of 3-22%) testing six scenarios: insulin dosing using sensor values, threshold, and predictive alarms, each without or with considering CGM trend arrows. Results: In all six scenarios, the occurrence of hypoglycemia (frequency of BG levels £ 50 mg/dL and BG levels £ 39 mg/dL) increased with sensor error, displaying an abrupt slope change at MARD = 10%. Similarly, hyperglycemia (frequency of BG levels ‡ 250 mg/dL and BG levels ‡ 400 mg/dL) increased and displayed an abrupt slope change at MARD = 10%. When added to insulin dosing decisions, information from CGM trend arrows, threshold, and predictive alarms resulted in improvement in average glycemia by 1.86, 8.17, and 8.88 mg/dL, respectively. Conclusions: Using CGM for insulin dosing decisions is feasible below a certain level of sensor error, estimated in silico at MARD = 10%. In our experiments, further accuracy improvement did not contribute substantively to better glycemic outcomes.

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Section: Utility Of Cgm Informationmentioning

confidence: 99%

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Kovatchev

Patek

Ortiz

et al. 2015

Diabetes Technology & Therapeutics

Self Cite

191

154

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“…The AP system architecture has been clinically evaluated for three potential configurations, based on the hardware platform where the controller was implemented, specifically: computer/laptop [7,[16][17][18], tablet [8,19,20] and smartphone [21]. In this study, we examine two possible AP hardware configurations; we refer to them as Configuration A and Configuration B.…”

Section: Hardware Configurations Of the Ap Systemmentioning

confidence: 99%

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

et al. 2016

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Significant increases in processing power, coupled with the miniaturization of processing units operating at low power levels, has motivated the embedding of modern control systems into medical devices. The design of such embedded decision-making strategies for medical applications is driven by multiple crucial factors, such as: (i) guaranteed safety in the presence of exogenous disturbances and unexpected system failures; (ii) constraints on computing resources; (iii) portability and longevity in terms of size and power consumption; and (iv) constraints on manufacturing and maintenance costs. Embedded control systems are especially compelling in the context of modern artificial pancreas systems (AP) used in glucose regulation for patients with type 1 diabetes mellitus (T1DM). Herein, a review of potential embedded control strategies that can be leveraged in a fully-automated and portable AP is presented. Amongst competing controllers, emphasis is provided on model predictive control (MPC), since it has been established as a very promising control strategy for glucose regulation using the AP. Challenges involved in the design, implementation and validation of safety-critical embedded model predictive controllers for the AP application are discussed in detail. Additionally, the computational expenditure inherent to MPC strategies is investigated, and a comparative study of runtime performances and storage requirements among modern quadratic programming solvers is reported for a desktop environment and a prototype hardware platform.

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“…Further advances in both sensor performance and algorithms incrementally demonstrated improved protection against hypoglycemia, reduced variability, and decreased mean glucose levels in controlled inpatient settings (11)(12)(13) as well as in outpatient studies (2,4,6). The strategies for different control algorithms are well described in several recent reviews (14,15).…”

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confidence: 99%

Day and Night Closed-Loop Control Using the Integrated Medtronic Hybrid Closed-Loop System in Type 1 Diabetes at Diabetes Camp

et al. 2015

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OBJECTIVETo evaluate the feasibility and efficacy of a fully integrated hybrid closed-loop (HCL) system (Medtronic MiniMed Inc., Northridge, CA), in day and night closedloop control in subjects with type 1 diabetes, both in an inpatient setting and during 6 days at diabetes camp. RESEARCH DESIGN AND METHODSThe Medtronic MiniMed HCL system consists of a fourth generation (4S) glucose sensor, a sensor transmitter, and an insulin pump using a modified proportionalintegral-derivative (PID) insulin feedback algorithm with safety constraints. Eight subjects were studied over 48 h in an inpatient setting. This was followed by a study of 21 subjects for 6 days at diabetes camp, randomized to either the closedloop control group using the HCL system or to the group using the Medtronic MiniMed 530G with threshold suspend (control group). RESULTSThe overall mean sensor glucose percent time in range 70-180 mg/dL was similar between the groups (73.1% vs. 69.9%, control vs. HCL, respectively) (P = 0.580).Meter glucose values between 70 and 180 mg/dL were also similar between the groups (73.6% vs. 63.2%, control vs. HCL, respectively) (P = 0.086). The mean absolute relative difference of the 4S sensor was 10.8 6 10.2%, when compared with plasma glucose values in the inpatient setting, and 12.6 6 11.0% compared with capillary Bayer CONTOUR NEXT LINK glucose meter values during 6 days at camp. CONCLUSIONSIn the first clinical study of this fully integrated system using an investigational PID algorithm, the system did not demonstrate improved glucose control compared with sensor-augmented pump therapy alone. The system demonstrated good connectivity and improved sensor performance.There have been a number of advances in developing automated insulin delivery systems for optimizing glucose control in patients with type 1 diabetes with the ultimate aim of reducing the burden of care for this condition (1-7). Early studies (8-10) demonstrated the feasibility of automated insulin modulation using

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Safety of Outpatient Closed-Loop Control: First Randomized Crossover Trials of a Wearable Artificial Pancreas

Cited by 169 publications

References 23 publications

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Assessing Sensor Accuracy for Non-Adjunct Use of Continuous Glucose Monitoring

Embedded Control in Wearable Medical Devices: Application to the Artificial Pancreas

Day and Night Closed-Loop Control Using the Integrated Medtronic Hybrid Closed-Loop System in Type 1 Diabetes at Diabetes Camp

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