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  • 1
    In: Diabetes Care, American Diabetes Association, Vol. 43, No. 8 ( 2020-08-01), p. 1822-1828
    Abstract: Limited information is available about glycemic outcomes with a closed-loop control (CLC) system compared with a predictive low-glucose suspend (PLGS) system. RESEARCH DESIGN AND METHODS After 6 months of use of a CLC system in a randomized trial, 109 participants with type 1 diabetes (age range, 14–72 years; mean HbA1c, 7.1% [54 mmol/mol]) were randomly assigned to CLC (N = 54, Control-IQ) or PLGS (N = 55, Basal-IQ) groups for 3 months. The primary outcome was continuous glucose monitor (CGM)-measured time in range (TIR) for 70–180 mg/dL. Baseline CGM metrics were computed from the last 3 months of the preceding study. RESULTS All 109 participants completed the study. Mean ± SD TIR was 71.1 ± 11.2% at baseline and 67.6 ± 12.6% using intention-to-treat analysis (69.1 ± 12.2% using per-protocol analysis excluding periods of study-wide suspension of device use) over 13 weeks on CLC vs. 70.0 ± 13.6% and 60.4 ± 17.1% on PLGS (difference = 5.9%; 95% CI 3.6%, 8.3%; P & lt; 0.001). Time & gt;180 mg/dL was lower in the CLC group than PLGS group (difference = −6.0%; 95% CI −8.4%, −3.7%; P & lt; 0.001) while time & lt;54 mg/dL was similar (0.04%; 95% CI −0.05%, 0.13%; P = 0.41). HbA1c after 13 weeks was lower on CLC than PLGS (7.2% [55 mmol/mol] vs. 7.5% [56 mmol/mol] , difference −0.34% [−3.7 mmol/mol]; 95% CI −0.57% [−6.2 mmol/mol] , −0.11% [1.2 mmol/mol]; P = 0.0035). CONCLUSIONS Following 6 months of CLC, switching to PLGS reduced TIR and increased HbA1c toward their pre-CLC values, while hypoglycemia remained similarly reduced with both CLC and PLGS.
    Type of Medium: Online Resource
    ISSN: 0149-5992 , 1935-5548
    Language: English
    Publisher: American Diabetes Association
    Publication Date: 2020
    detail.hit.zdb_id: 1490520-6
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  • 2
    In: Diabetes Technology & Therapeutics, Mary Ann Liebert Inc, Vol. 23, No. 7 ( 2021-07-01), p. 475-481
    Type of Medium: Online Resource
    ISSN: 1520-9156 , 1557-8593
    Language: English
    Publisher: Mary Ann Liebert Inc
    Publication Date: 2021
    detail.hit.zdb_id: 2004914-6
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  • 3
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 17, No. 5 ( 2023-09), p. 1226-1242
    Abstract: A composite metric for the quality of glycemia from continuous glucose monitor (CGM) tracings could be useful for assisting with basic clinical interpretation of CGM data. Methods: We assembled a data set of 14-day CGM tracings from 225 insulin-treated adults with diabetes. Using a balanced incomplete block design, 330 clinicians who were highly experienced with CGM analysis and interpretation ranked the CGM tracings from best to worst quality of glycemia. We used principal component analysis and multiple regressions to develop a model to predict the clinician ranking based on seven standard metrics in an Ambulatory Glucose Profile: very low–glucose and low-glucose hypoglycemia; very high–glucose and high-glucose hyperglycemia; time in range; mean glucose; and coefficient of variation. Results: The analysis showed that clinician rankings depend on two components, one related to hypoglycemia that gives more weight to very low-glucose than to low-glucose and the other related to hyperglycemia that likewise gives greater weight to very high-glucose than to high-glucose. These two components should be calculated and displayed separately, but they can also be combined into a single Glycemia Risk Index (GRI) that corresponds closely to the clinician rankings of the overall quality of glycemia (r = 0.95). The GRI can be displayed graphically on a GRI Grid with the hypoglycemia component on the horizontal axis and the hyperglycemia component on the vertical axis. Diagonal lines divide the graph into five zones (quintiles) corresponding to the best (0th to 20th percentile) to worst (81st to 100th percentile) overall quality of glycemia. The GRI Grid enables users to track sequential changes within an individual over time and compare groups of individuals. Conclusion: The GRI is a single-number summary of the quality of glycemia. Its hypoglycemia and hyperglycemia components provide actionable scores and a graphical display (the GRI Grid) that can be used by clinicians and researchers to determine the glycemic effects of prescribed and investigational treatments.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2023
    detail.hit.zdb_id: 2467312-2
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  • 4
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 12, No. 5 ( 2018-09), p. 992-1001
    Abstract: The objective was to model clinical and economic outcomes of self-monitoring blood glucose (SMBG) devices with varying error ranges and strip prices for type 1 and insulin-treated type 2 diabetes patients in England. Methods: We programmed a simulation model that included separate risk and complication estimates by type of diabetes and evidence from in silico modeling validated by the Food and Drug Administration. Changes in SMBG error were associated with changes in hemoglobin A1c (HbA1c) and separately, changes in hypoglycemia. Markov cohort simulation estimated clinical and economic outcomes. A SMBG device with 8.4% error and strip price of £0.30 (exceeding accuracy requirements by International Organization for Standardization [ISO] 15197:2013/EN ISO 15197:2015) was compared to a device with 15% error (accuracy meeting ISO 15197:2013/EN ISO 15197:2015) and price of £0.20. Outcomes were lifetime costs, quality-adjusted life years (QALYs) and incremental cost-effectiveness ratios (ICERs). Results: With SMBG errors associated with changes in HbA1c only, the ICER was £3064 per QALY in type 1 diabetes and £264 668 per QALY in insulin-treated type 2 diabetes for an SMBG device with 8.4% versus 15% error. With SMBG errors associated with hypoglycemic events only, the device exceeding accuracy requirements was cost-saving and more effective in insulin-treated type 1 and type 2 diabetes. Conclusions: Investment in devices with higher strip prices but improved accuracy (less error) appears to be an efficient strategy for insulin-treated diabetes patients at high risk of severe hypoglycemia.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2018
    detail.hit.zdb_id: 2467312-2
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  • 5
    In: New England Journal of Medicine, Massachusetts Medical Society, Vol. 383, No. 9 ( 2020-08-27), p. 836-845
    Type of Medium: Online Resource
    ISSN: 0028-4793 , 1533-4406
    RVK:
    Language: English
    Publisher: Massachusetts Medical Society
    Publication Date: 2020
    detail.hit.zdb_id: 1468837-2
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  • 6
    In: New England Journal of Medicine, Massachusetts Medical Society, Vol. 388, No. 11 ( 2023-03-16), p. 991-1001
    Type of Medium: Online Resource
    ISSN: 0028-4793 , 1533-4406
    RVK:
    Language: English
    Publisher: Massachusetts Medical Society
    Publication Date: 2023
    detail.hit.zdb_id: 1468837-2
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  • 7
    Online Resource
    Online Resource
    Elsevier BV ; 2020
    In:  Computer Methods and Programs in Biomedicine Vol. 197 ( 2020-12), p. 105757-
    In: Computer Methods and Programs in Biomedicine, Elsevier BV, Vol. 197 ( 2020-12), p. 105757-
    Type of Medium: Online Resource
    ISSN: 0169-2607
    RVK:
    Language: English
    Publisher: Elsevier BV
    Publication Date: 2020
    detail.hit.zdb_id: 1466281-4
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  • 8
    Online Resource
    Online Resource
    SAGE Publications ; 2010
    In:  Journal of Diabetes Science and Technology Vol. 4, No. 5 ( 2010-09), p. 1146-1155
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 4, No. 5 ( 2010-09), p. 1146-1155
    Abstract: Hypoglycemia has been identified as a primary barrier to optimal management of diabetes. This observation, in conjunction with the introduction of continuous glucose monitoring (CGM) devices, has set the stage for achieving tight glycemic control with systems that adjust the insulin pump settings based on measured glucose concentrations. Because system safety and system reliability are key considerations, there is a need for algorithms that reduce the risk of hypoglycemia in closed-loop, open-loop, and advisory-mode systems. More specifically, the algorithm presented here is formulated as a component of the independent safety system module proposed in the modular control-to-range architecture. Methods: We developed two algorithms for attenuating insulin pump injections, which we refer to as Brakes and Power Brakes: Brakes is a pump attenuation function computed using CGM information only, while Power Brakes is an attenuation function in which a metabolic state observer with insulin input is used in addition to CGM information to inform the level of pump attenuation. These algorithms modulate the insulin pump delivery so that the insulin injection rate is dramatically reduced when the risk of hypoglycemia is high. Additionally, we combined these algorithms with an alert system that indicates a level of hypoglycemic risk to the user. Results: We demonstrated the effectiveness of Brakes and Power Brakes in reducing the incidence of hypoglycemia in two simulated scenarios: An elevated basal rate scenario and a scenario in which a bolus is delivered for a meal that is skipped. For these scenarios, the incidence of hypoglycemia using Power Brakes was reduced by 88 and 94%, respectively, where we defined hypoglycemia based on the American Diabetes Association guidelines for defining and reporting as 70 mg/dl. In the elevated basal rate scenario, no rebounds above 180 mg/dl (the desired upper limit of the control-to-range protocol) following hypoglycemia were shown to occur. We demonstrated the way the hypoglycemia alert system can trigger the intake of carbohydrates to reduce the incidence of hypoglycemia by 98%. Conclusions: This article offers, for the first time, a method for smoothly reducing insulin delivery rate to prevent hypoglycemia in individuals with type 1 diabetes mellitus based on a mathematically formal assessment of hypoglycemic risk. In silico, we demonstrate the way this method can prevent hypoglycemia while avoiding hyperglycemia rebounds that exceed 180 mg/dl. In conjunction with the pump attenuation algorithms, this article also proposes a system for alerting an individual of their hypoglycemic risk that contains three “levels” of alerts in the form of a traffic light. This alert system can be used in an advisory mode setting to alert the user to take action when hypoglycemia is imminent (“red” light) or in a closed-loop setting where initiation of rescue action begins when the red light alert is triggered.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2010
    detail.hit.zdb_id: 2467312-2
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  • 9
    Online Resource
    Online Resource
    SAGE Publications ; 2016
    In:  Journal of Diabetes Science and Technology Vol. 10, No. 1 ( 2016-01), p. 50-59
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 10, No. 1 ( 2016-01), p. 50-59
    Abstract: The risk of hypo- and hyperglycemia has been assessed for years by computing the well-known low blood glucose index (LBGI) and high blood glucose index (HBGI) on sparse self-monitoring blood glucose (SMBG) readings. These metrics have been shown to be predictive of future glycemic events and clinically relevant cutoff values to classify the state of a patient have been defined, but their application to continuous glucose monitoring (CGM) profiles has not been validated yet. The aim of this article is to explore the relationship between CGM-based and SMBG-based LBGI/HBGI, and provide a guideline to follow when these indices are computed on CGM time series. Methods: Twenty-eight subjects with type 1 diabetes mellitus (T1DM) were monitored in daily-life conditions for up to 4 weeks with both SMBG and CGM systems. Linear and nonlinear models were considered to describe the relationship between risk indices evaluated on SMBG and CGM data. Results: LBGI values obtained from CGM did not match closely SMBG-based values, with clear underestimation especially in the low risk range, and a linear transformation performed best to match CGM-based LBGI to SMBG-based LBGI. For HBGI, a linear model with unitary slope and no intercept was reliable, suggesting that no correction is needed to compute this index from CGM time series. Conclusions: Alternate versions of LBGI and HBGI adapted to the characteristics of CGM signals have been proposed that enable extending results obtained for SMBG data and using clinically relevant cutoff values previously defined to promptly classify the glycemic condition of a patient.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2016
    detail.hit.zdb_id: 2467312-2
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  • 10
    In: Journal of Diabetes Science and Technology, SAGE Publications, Vol. 10, No. 1 ( 2016-01), p. 104-110
    Abstract: The Predictive Hypoglycemia Minimizer System (“Hypo Minimizer”), consisting of a zone model predictive controller (the “controller”) and a safety supervision module (the “safety module”), aims to mitigate hypoglycemia by preemptively modulating insulin delivery based on continuous glucose monitor (CGM) measurements. The “aggressiveness factor,” a pivotal variable in the system, governs the speed and magnitude of the controller’s insulin dosing characteristics in response to changes in CGM levels. Methods: Twelve adults with type 1 diabetes were studied in closed-loop in a clinical research center for approximately 24 hours. This analysis focused primarily on the effect of the aggressiveness factor on the automated insulin-delivery characteristics of the controller, and secondarily on the glucose control results. Results: As aggressiveness increased from “conservative” to “medium” to “aggressive,” the controller recommended less insulin (–3.3% vs –14.4% vs –19.5% relative to basal) with a higher frequency (5.3% vs 14.4% vs 20.3%) during the critical times when the CGM was reading 90-120 mg/dl and decreasing. Blood glucose analyses indicated that the most aggressive setting resulted in the most desirable combination of the least time spent 〈 70 mg/dl and the most time spent 70-180 mg/dl, particularly in the overnight period. Hyperglycemia, diabetic ketoacidosis, or severe hypoglycemia did not occur with any of the aggressiveness values. Conclusion: The Hypo Minimizer’s controller took preemptive action to prevent hypoglycemia based on predicted changes in CGM glucose levels. The most aggressive setting was quickest to take action to reduce insulin delivery below basal and achieved the best glucose metrics.
    Type of Medium: Online Resource
    ISSN: 1932-2968 , 1932-2968
    Language: English
    Publisher: SAGE Publications
    Publication Date: 2016
    detail.hit.zdb_id: 2467312-2
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