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Deshpande S, Doyle FJ, Dassau E. Glucose Rate-of-Change and Insulin-on-Board Jointly Weighted Zone Model Predictive Control. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY : A PUBLICATION OF THE IEEE CONTROL SYSTEMS SOCIETY 2023; 31:2261-2274. [PMID: 38525198 PMCID: PMC10958373 DOI: 10.1109/tcst.2023.3291573] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 03/26/2024]
Abstract
We present design and evaluation of closed-loop insulin delivery using zone model predictive control (MPC) featuring an adaptive weighting scheme to address prolonged hyperglycemia due to changes in insulin sensitivity, underdelivery from profile mismatch, and meal composition. In the MPC cost function, the penalty on predicted glucose deviation from the upper zone boundary is weighted by a joint function of predicted glucose rate-of-change (ROC) and insulin-on-board (IOB). The asymmetric weighting gradually increases when glucose ROC and IOB were jointly low, independent of glucose magnitude, to limit hyperglycemia while aggressively reduces for negative glucose ROC to avoid hypoglycemia. The proposed controller was evaluated using two simulation scenarios: an induced resistance scenario and a nominal scenario to highlight the performance over a reference zone MPC with glucose ROC weighting only. The continuous adaption scheme resulted in consistent improvement for the entire glucose range without incurring additional risk of hypoglycemia. For the induced resistance and no feedforward bolus scenario, the percent time in 70-180 mg/dL was higher (53.5% versus 48.9%, p<0.001) with larger improvement in the overnight percent time in tighter glucose range 70-140 mg/dL (70.9% versus 52.9%, p<0.001). The results from extensive simulations, as well as clinical validation in three different outpatient studies demonstrate the utility and safety of the proposed zone MPC.
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Affiliation(s)
- Sunil Deshpande
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, USA
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Kang SL, Hwang YN, Kwon JY, Kim SM. Effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes (T1D): systematic review and meta-analysis. Diabetol Metab Syndr 2022; 14:187. [PMID: 36494830 PMCID: PMC9733359 DOI: 10.1186/s13098-022-00962-2] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 12/02/2022] [Indexed: 12/13/2022] Open
Abstract
BACKGROUND The purpose of this study was to assess the effectiveness and safety of a model predictive control (MPC) algorithm for an artificial pancreas system in outpatients with type 1 diabetes. METHODS We searched PubMed, EMBASE, Cochrane Central, and the Web of Science to December 2021. The eligibility criteria for study selection were randomized controlled trials comparing artificial pancreas systems (MPC, PID, and fuzzy algorithms) with conventional insulin therapy in type 1 diabetes patients. The heterogeneity of the overall results was identified by subgroup analysis of two factors including the intervention duration (overnight and 24 h) and the follow-up periods (< 1 week, 1 week to 1 month, and > 1 month). RESULTS The meta-analysis included a total of 41 studies. Considering the effect on the percentage of time maintained in the target range between the MPC-based artificial pancreas and conventional insulin therapy, the results showed a statistically significantly higher percentage of time maintained in the target range in overnight use (10.03%, 95% CI [7.50, 12.56] p < 0.00001). When the follow-up period was considered, in overnight use, the MPC-based algorithm showed a statistically significantly lower percentage of time maintained in the hypoglycemic range (-1.34%, 95% CI [-1.87, -0.81] p < 0.00001) over a long period of use (> 1 month). CONCLUSIONS Overnight use of the MPC-based artificial pancreas system statistically significantly improved glucose control while increasing time maintained in the target range for outpatients with type 1 diabetes. Results of subgroup analysis revealed that MPC algorithm-based artificial pancreas system was safe while reducing the time maintained in the hypoglycemic range after an overnight intervention with a long follow-up period (more than 1 month).
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Affiliation(s)
- Su Lim Kang
- Department of Medical Device and Healthcare, Dongguk University-Seoul, 26, Pil-Dong 3-Ga, Seoul, Jung-Gu 04620 Republic of Korea
| | - Yoo Na Hwang
- Department of Medical Device and Healthcare, Dongguk University-Seoul, 26, Pil-Dong 3-Ga, Seoul, Jung-Gu 04620 Republic of Korea
| | - Ji Yean Kwon
- Department of Medical Device and Healthcare, Dongguk University-Seoul, 26, Pil-Dong 3-Ga, Seoul, Jung-Gu 04620 Republic of Korea
| | - Sung Min Kim
- Department of Medical Device and Healthcare, Dongguk University-Seoul, 26, Pil-Dong 3-Ga, Seoul, Jung-Gu 04620 Republic of Korea
- Department of Medical Device Regulatory Science, Dongguk University-Seoul, 26, Pil-dong 3-Ga, Seoul, Jung-Gu 04620 Republic of Korea
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Ozaslan B, Levy CJ, Kudva YC, Pinsker JE, O'Malley G, Kaur RJ, Castorino K, Levister C, Trinidad MC, Desjardins D, Church MM, Plesser M, McCrady-Spitzer S, Ogyaadu S, Nelson K, Reid C, Deshpande S, Kremers WK, Doyle FJ, Rosenn B, Dassau E. Feasibility of Closed-Loop Insulin Delivery with a Pregnancy-Specific Zone Model Predictive Control Algorithm. Diabetes Technol Ther 2022; 24:471-480. [PMID: 35230138 PMCID: PMC9464083 DOI: 10.1089/dia.2021.0521] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/05/2023]
Abstract
Objective: Evaluating the feasibility of closed-loop insulin delivery with a zone model predictive control (zone-MPC) algorithm designed for pregnancy complicated by type 1 diabetes (T1D). Research Design and Methods: Pregnant women with T1D from 14 to 32 weeks gestation already using continuous glucose monitor (CGM) augmented pump therapy were enrolled in a 2-day multicenter supervised outpatient study evaluating pregnancy-specific zone-MPC based closed-loop control (CLC) with the interoperable artificial pancreas system (iAPS) running on an unlocked smartphone. Meals and activities were unrestricted. The primary outcome was the CGM percentage of time between 63 and 140 mg/dL compared with participants' 1-week run-in period. Early (2-h) postprandial glucose control was also evaluated. Results: Eleven participants completed the study (age: 30.6 ± 4.1 years; gestational age: 20.7 ± 3.5 weeks; weight: 76.5 ± 15.3 kg; hemoglobin A1c: 5.6% ± 0.5% at enrollment). No serious adverse events occurred. Compared with the 1-week run-in, there was an increased percentage of time in 63-140 mg/dL during supervised CLC (CLC: 81.5%, run-in: 64%, P = 0.007) with less time >140 mg/dL (CLC: 16.5%, run-in: 30.8%, P = 0.029) and time <63 mg/dL (CLC: 2.0%, run-in:5.2%, P = 0.039). There was also less time <54 mg/dL (CLC: 0.7%, run-in:1.6%, P = 0.030) and >180 mg/dL (CLC: 4.9%, run-in: 13.1%, P = 0.032). Overnight glucose control was comparable, except for less time >250 mg/dL (CLC: 0%, run-in:3.9%, P = 0.030) and lower glucose standard deviation (CLC: 23.8 mg/dL, run-in:42.8 mg/dL, P = 0.007) during CLC. Conclusion: In this pilot study, use of the pregnancy-specific zone-MPC was feasible in pregnant women with T1D. Although the duration of our study was short and the number of participants was small, our findings add to the limited data available on the use of CLC systems during pregnancy (NCT04492566).
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Affiliation(s)
- Basak Ozaslan
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Carol J. Levy
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Grenye O'Malley
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Camilla Levister
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | | | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | - Mitchell Plesser
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | | | - Selassie Ogyaadu
- Icahn School of Medicine at Mount Sinai, New York, New York, USA
| | - Kristen Nelson
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | | | - Sunil Deshpande
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
- Sansum Diabetes Research Institute, Santa Barbara, California, USA
| | | | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
| | - Barak Rosenn
- Robert Wood Johnson Barnabas Health, New Brunswick, New Jersey, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, Massachusetts, USA
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Ozaslan B, Deshpande S, Doyle FJ, Dassau E. Zone-MPC Automated Insulin Delivery Algorithm Tuned for Pregnancy Complicated by Type 1 Diabetes. Front Endocrinol (Lausanne) 2022; 12:768639. [PMID: 35392357 PMCID: PMC8982146 DOI: 10.3389/fendo.2021.768639] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/31/2021] [Accepted: 12/30/2021] [Indexed: 01/13/2023] Open
Abstract
Type 1 diabetes (T1D) increases the risk for pregnancy complications. Increased time in the pregnancy glucose target range (63-140 mg/dL as suggested by clinical guidelines) is associated with improved pregnancy outcomes that underscores the need for tight glycemic control. While closed-loop control is highly effective in regulating blood glucose levels in individuals with T1D, its use during pregnancy requires adjustments to meet the tight glycemic control and changing insulin requirements with advancing gestation. In this paper, we tailor a zone model predictive controller (zone-MPC), an optimization-based control strategy that uses model predictions, for use during pregnancy and verify its robustness in-silico through a broad range of scenarios. We customize the existing zone-MPC to satisfy pregnancy-specific glucose control objectives by having (i) lower target glycemic zones (i.e., 80-110 mg/dL daytime and 80-100 mg/dL overnight), (ii) more assertive correction bolus for hyperglycemia, and (iii) a control strategy that results in more aggressive postprandial insulin delivery to keep glucose within the target zone. The emphasis is on leveraging the flexible design of zone-MPC to obtain a controller that satisfies glycemic outcomes recommended for pregnancy based on clinical insight. To verify this pregnancy-specific zone-MPC design, we use the UVA/Padova simulator and conduct in-silico experiments on 10 subjects over 13 scenarios ranging from scenarios with ideal metabolic and treatment parameters for pregnancy to extreme scenarios with such parameters that are highly deviant from the ideal. All scenarios had three meals per day and each meal had 40 grams of carbohydrates. Across 13 scenarios, pregnancy-specific zone-MPC led to a 10.3 ± 5.3% increase in the time in pregnancy target range (baseline zone-MPC: 70.6 ± 15.0%, pregnancy-specific zone-MPC: 80.8 ± 11.3%, p < 0.001) and a 10.7 ± 4.8% reduction in the time above the target range (baseline zone-MPC: 29.0 ± 15.4%, pregnancy-specific zone-MPC: 18.3 ± 12.0, p < 0.001). There was no significant difference in the time below range between the controllers (baseline zone-MPC: 0.5 ± 1.2%, pregnancy-specific zone-MPC: 3.5 ± 1.9%, p = 0.1). The extensive simulation results show improved performance in the pregnancy target range with pregnancy-specific zone MPC, suggest robustness of the zone-MPC in tight glucose control scenarios, and emphasize the need for customized glucose control systems for pregnancy.
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Affiliation(s)
| | | | | | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Boston, MA, United States
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Colmegna P, Cengiz E, Garcia-Tirado J, Kraemer K, Breton MD. Impact of Accelerating Insulin on an Artificial Pancreas System Without Meal Announcement: An In Silico Examination. J Diabetes Sci Technol 2021; 15:833-841. [PMID: 32546001 PMCID: PMC8258534 DOI: 10.1177/1932296820928067] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Controlling postprandial blood glucose without the benefit of an appropriately sized premeal insulin bolus has been challenging given the delays in absorption and action of subcutaneously injected insulin during conventional and artificial pancreas (AP) system diabetes treatment. We aim to understand the impact of accelerating insulin and increasing aggressiveness of the AP controller as potential solutions to address the postprandial hyperglycemia challenge posed by unannounced meals through a simulation study. METHODS Accelerated rapid-acting insulin analogue is modeled within the UVA/Padova simulation platform by uniformly reducing its pharmacokinetic time constants (α multiplier) and used with a model predictive control, where the controller's aggressiveness depends on α. Two sets of single-meal simulations were performed: (1) where we only tune the controller's aggressiveness and (2) where we also accelerate insulin absorption and action to assess postprandial glycemic control during each intervention. RESULTS Mean percent of time spent within the 70 to 180 mg/dL postprandial glycemic range is significantly higher in set (2) than in set (1): 79.9, 95% confidence interval [77.0, 82.7] vs 88.8 [86.8, 90.9] ([Note to typesetter: Set all unnecessary math in text format and insert appropriate spaces between operators.] P < .05) for α = 2, and 81.4 [78.6, 84.3] vs 94.1 [92.6, 95.6] (P < .05) for α = 3. A decrease in percent of time below 70 mg/dL is also detected: 0.9 [0.4, 2.2] vs 0.6 [0.2, 1.4] (P = .23) for α = 2 and 1.4 [0.7, 2.8] vs 0.4 [0.1, 1.4] (P < .05) for α = 3. CONCLUSION These proof-of-concept simulations suggest that an AP without prandial insulin boluses combined with significantly faster insulin analogues could match the glycemic performance obtained with an optimal hybrid AP.
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Affiliation(s)
- Patricio Colmegna
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Patricio Colmegna, PhD, Center for Diabetes Technology, University of Virginia, 560 Ray C Hunt Dr, Charlottesville, VA 22903, USA.
| | - Eda Cengiz
- Division of Pediatric Endocrinology and Diabetes, Yale University School of Medicine, New Haven, CT, USA
- Bahcesehir University School of Medicine, Istanbul, Turkey
| | - Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
| | - Kristen Kraemer
- Division of Pediatric Endocrinology and Diabetes, Yale University School of Medicine, New Haven, CT, USA
| | - Marc D. Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, USA
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Sevil M, Rashid M, Hajizadeh I, Park M, Quinn L, Cinar A. Physical Activity and Psychological Stress Detection and Assessment of Their Effects on Glucose Concentration Predictions in Diabetes Management. IEEE Trans Biomed Eng 2021; 68:2251-2260. [PMID: 33400644 PMCID: PMC8265613 DOI: 10.1109/tbme.2020.3049109] [Citation(s) in RCA: 17] [Impact Index Per Article: 5.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/23/2022]
Abstract
OBJECTIVE Continuous glucose monitoring (CGM) enables prediction of the future glucose concentration (GC) trajectory for making informed diabetes management decisions. The glucose concentration values are affected by various physiological and metabolic variations, such as physical activity (PA) and acute psychological stress (APS), in addition to meals and insulin. In this work, we extend our adaptive glucose modeling framework to incorporate the effects of PA and APS on the GC predictions. METHODS A wristband conducive of use by free-living ambulatory people is used. The measured physiological variables are analyzed to generate new quantifiable input features for PA and APS. Machine learning techniques estimate the type and intensity of the PA and APS when they occur individually and concurrently. Variables quantifying the characteristics of both PA and APS are integrated as exogenous inputs in an adaptive system identification technique for enhancing the accuracy of GC predictions. Data from clinical experiments illustrate the improvement in GC prediction accuracy. RESULTS The average mean absolute error (MAE) of one-hour-ahead GC predictions with testing data decreases from 35.1 to 31.9 mg/dL (p-value = 0.01) with the inclusion of PA information, and it decreases from 16.9 to 14.2 mg/dL (p-value = 0.006) with the inclusion of PA and APS information. CONCLUSION The first-ever glucose prediction model is developed that incorporates measures of physical activity and acute psychological stress to improve GC prediction accuracy. SIGNIFICANCE Modeling the effects of physical activity and acute psychological stress on glucose concentration values will improve diabetes management and enable informed meal, activity and insulin dosing decisions.
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Zhu T, Li K, Herrero P, Georgiou P. Basal Glucose Control in Type 1 Diabetes Using Deep Reinforcement Learning: An In Silico Validation. IEEE J Biomed Health Inform 2021; 25:1223-1232. [PMID: 32755873 DOI: 10.1109/jbhi.2020.3014556] [Citation(s) in RCA: 24] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/10/2022]
Abstract
People with Type 1 diabetes (T1D) require regular exogenous infusion of insulin to maintain their blood glucose concentration in a therapeutically adequate target range. Although the artificial pancreas and continuous glucose monitoring have been proven to be effective in achieving closed-loop control, significant challenges still remain due to the high complexity of glucose dynamics and limitations in the technology. In this work, we propose a novel deep reinforcement learning model for single-hormone (insulin) and dual-hormone (insulin and glucagon) delivery. In particular, the delivery strategies are developed by double Q-learning with dilated recurrent neural networks. For designing and testing purposes, the FDA-accepted UVA/Padova Type 1 simulator was employed. First, we performed long-term generalized training to obtain a population model. Then, this model was personalized with a small data-set of subject-specific data. In silico results show that the single and dual-hormone delivery strategies achieve good glucose control when compared to a standard basal-bolus therapy with low-glucose insulin suspension. Specifically, in the adult cohort (n = 10), percentage time in target range 70, 180 mg/dL improved from 77.6% to 80.9% with single-hormone control, and to 85.6% with dual-hormone control. In the adolescent cohort (n = 10), percentage time in target range improved from 55.5% to [Formula: see text] with single-hormone control, and to 78.8% with dual-hormone control. In all scenarios, a significant decrease in hypoglycemia was observed. These results show that the use of deep reinforcement learning is a viable approach for closed-loop glucose control in T1D.
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Chakrabarty A, Healey E, Shi D, Zavitsanou S, Doyle FJ, Dassau E. Embedded Model Predictive Control for a Wearable Artificial Pancreas. IEEE TRANSACTIONS ON CONTROL SYSTEMS TECHNOLOGY : A PUBLICATION OF THE IEEE CONTROL SYSTEMS SOCIETY 2020; 28:2600-2607. [PMID: 33762804 PMCID: PMC7983018 DOI: 10.1109/tcst.2019.2939122] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/12/2023]
Abstract
While artificial pancreas (AP) systems are expected to improve the quality of life among people with type 1 diabetes mellitus (T1DM), the design of convenient systems that optimize the user experience, especially for those with active lifestyles, such as children and adolescents, still remains an open research question. In this work, we introduce an embeddable design and implementation of model predictive control (MPC) of AP systems for people with T1DM that significantly reduces the weight and on-body footprint of the AP system. The embeddable controller is based on a zone MPC that has been evaluated in multiple clinical studies. The proposed embedded zone MPC features a simpler design of the periodic safe zone in the cost function and the utilization of state-of-the-art alternating minimization algorithms for solving the convex programming problems inherent to MPC with linear models subject to convex constraints. Off-line closed-loop data generated by the FDA-accepted UVA/Padova simulator is used to select an optimization algorithm and corresponding tuning parameters. Through hardware-in-the-loop in silico results on a limited-resource Arduino Zero (Feather M0) platform, we demonstrate the potential of the proposed embedded MPC. In spite of resource limitations, our embedded zone MPC manages to achieve comparable performance of that of the full-version zone MPC implemented in a 64-bit desktop for scenarios with/without meal-disturbance compensations. Metrics for performance comparison included median percent time in the euglycemic ([70, 180] mg/dL range) of 84.3% vs. 83.1% for announced meals, with an equivalence test yielding p = 0.0013 and 66.2% vs. 66.0% for unannounced meals with p = 0.0028.
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Affiliation(s)
- Ankush Chakrabarty
- Control and Dynamical Systems Group, Mitsubishi Electric Research Laboratories, Cambridge, MA, USA
| | - Elizabeth Healey
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Dawei Shi
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Stamatina Zavitsanou
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, MA 02138, USA
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Song L, Liu C, Yang W, Zhang J, Kong X, Zhang B, Chen X, Wang N, Shen D, Li Z, Jin X, Shuai Y, Wang Y. Glucose outcomes of a learning-type artificial pancreas with an unannounced meal in type 1 diabetes. COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE 2020; 191:105416. [PMID: 32146213 DOI: 10.1016/j.cmpb.2020.105416] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/05/2020] [Revised: 02/19/2020] [Accepted: 02/22/2020] [Indexed: 06/10/2023]
Abstract
BACKGROUND AND OBJECTIVES Glycemic control with unannounced meals is the major challenge for artificial pancreas. In this study, we described the performance and safety of learning-type model predictive control (L-MPC) for artificial pancreas challenged by an unannounced meal in type 1 diabetes (T1D). METHODS This closed-loop (CL) system was tested in 29 T1D patients at one site in a 4 h inpatient open-label study. Participants used an L-MPC CL system for 6 days after 2-day system identification using open-loop (OL) insulin system. During the CL period, the L-MPC system was started from 8:00 am to noon each day. At 9:00 am, each participant consumed 50 g of carbohydrates with no prandial insulin bolus. At 9:30 am on CL-Day 4 or CL-Day 6, participants rode bicycles for 20 minutes or drank 50 ml of beer, in a random order. RESULTS As the primary outcome, TIR on CL-Day 3 was 65.2±23.3%, which was 9.8 points higher (95% CI 1.8 to 17.8; P = 0.019) than that on CL-Day 1. The time of glucose >10 mmol/L was decreased by 11.0% (95% CI -18.7 to 3.3; P = 0.007), and mean glucose level was decreased by 1.1 mmol/L (95% CI -1.1 to 0.5; P = 0.000). The total daily insulin dosage showed no significant difference (-0.1U, 95% CI -1.34 to 1.32; P = 0.982). Compared with OL-Day1 with a postprandial bolus, the TIR was increased by 13.7 points (95% CI 1.4 to 26.0; P = 0.030), the time of glucose >10 mmol/L and the mean glucose level were also decreased. Compared with the exercise day (CL-Day E, 62.0 ± 23.3%; P = 0.347) or alcohol day (CL-Day A, 64.0 ± 23.6%; P = 0.756), there was no statistically significant difference in terms of TIR, time of glucose >10 mmol/L and mean glucose level. No severe hypoglycemic events occurred and hypoglycemic episodes were not increased by using closed-loop insulin system. CONCLUSION The L-MPC CL insulin system achieved good glycemic control challenged by an unannounced meal.
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Affiliation(s)
- Lulu Song
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Changqing Liu
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Wenying Yang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Jinping Zhang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Xiaomu Kong
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Bo Zhang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Xiaoping Chen
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Na Wang
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Dong Shen
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China
| | - Zhaoqing Li
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Xian Jin
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Ying Shuai
- Department of Endocrinology, China-Japan Friendship Hospital, Beijing, China
| | - Youqing Wang
- College of Information Science and Technology, Beijing University of Chemical Technology, Beijing 100029, China.
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Abstract
Regular self-monitoring of blood glucose levels, and ketones when indicated, is an essential component of type 1 diabetes (T1D) management. Although fingerstick blood glucose monitoring has been the standard of care for decades, ongoing rapid technological developments have resulted in increasingly widespread use of continuous glucose monitoring (CGM). This article reviews recommendations for self-monitoring of glucose and ketones in pediatric T1D with particular emphasis on CGM and factors that impact the accuracy and real-world use of this technology.
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Affiliation(s)
- Brynn E. Marks
- Division of Endocrinology and Diabetes, Children's National Hospital, Washington, DC, United States
- *Correspondence: Brynn E. Marks
| | - Joseph I. Wolfsdorf
- Division of Endocrinology, Boston Children's Hospital, Boston, MA, United States
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Hajizadeh I, Hobbs N, Samadi S, Sevil M, Rashid M, Brandt R, Askari MR, Maloney Z, Cinar A. Controlling the AP Controller: Controller Performance Assessment and Modification. J Diabetes Sci Technol 2019; 13:1091-1104. [PMID: 31561714 PMCID: PMC6835190 DOI: 10.1177/1932296819877217] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/15/2022]
Abstract
BACKGROUND Despite recent advances in closed-loop control of blood glucose concentration (BGC) in people with type 1 diabetes (T1D), online performance assessment and modification of artificial pancreas (AP) control systems remain a challenge as the metabolic characteristics of users change over time. METHODS A controller performance assessment and modification system (CPAMS) analyzes the glucose concentration variations and controller behavior, and modifies the parameters of the control system used in the multivariable AP system. Various indices are defined to quantitatively evaluate the controller performance in real time. Controller performance assessment and modification system also incorporates online learning from historical data to anticipate impending disturbances and proactively counteract their effects. RESULTS Using a multivariable simulation platform for T1D, the CPAMS is used to enhance the BGC regulation in people with T1D by means of automated insulin delivery with an adaptive learning predictive controller. Controller performance assessment and modification system increases the percentage of time in the target range (70-180) mg/dL by 52.3% without causing any hypoglycemia and hyperglycemia events. CONCLUSIONS The results demonstrate a significant improvement in the multivariable AP controller performance by using CPAMS.
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Affiliation(s)
- Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Rachel Brandt
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mohammad Reza Askari
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
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12
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Fushimi E, Colmegna P, De Battista H, Garelli F, Sánchez-Peña R. Artificial Pancreas: Evaluating the ARG Algorithm Without Meal Announcement. J Diabetes Sci Technol 2019; 13:1035-1043. [PMID: 31339059 PMCID: PMC6835180 DOI: 10.1177/1932296819864585] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
BACKGROUND Either under standard basal-bolus treatment or hybrid closed-loop control, subjects with type 1 diabetes are required to count carbohydrates (CHOs). However, CHO counting is not only burdensome but also prone to errors. Recently, an artificial pancreas algorithm that does not require premeal insulin boluses-the so-called automatic regulation of glucose (ARG)-was introduced. In its first pilot clinical study, although the exact CHO counting was not required, subjects still needed to announce the meal time and classify the meal size. METHOD An automatic switching signal generator (SSG) is proposed in this work to remove the manual mealtime announcement from the control strategy. The SSG is based on a Kalman filter and works with continuous glucose monitoring readings only. RESULTS The ARG algorithm with unannounced meals (ARGum) was tested in silico under the effect of different types of mixed meals and intrapatient variability, and contrasted with the ARG algorithm with announced meals (ARGam). Simulations reveal that, for slow-absorbing meals, the time in the euglycemic range, [70-180] mg/dL, increases using the unannounced strategy (ARGam: 78.1 [68.6-80.2]% (median [IQR]) and ARGum: 87.8 [84.5-90.6]%), while similar results were found with fast-absorbing meals (ARGam: 87.4 [86.0-88.9]% and ARGum: 87.6 [86.1-88.8]%). On the other hand, when intrapatient variability is considered, time in euglycemia is also comparable (ARGam: 81.4 [75.4-83.5]% and ARGum: 80.9 [77.0-85.1]%). CONCLUSION In silico results indicate that it is feasible to perform an in vivo evaluation of the ARG algorithm with unannounced meals.
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Affiliation(s)
- Emilia Fushimi
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- Emilia Fushimi. Instituto LEICI (Grupo de Control Aplicado), Depto. Electrotecnia, Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP),, Calle 48 y116, La Plata 1900, Argentina.
| | - Patricio Colmegna
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- University of Virginia (UVA), Center for Diabetes Technology, Charlottesville, VA, USA
- Universidad Nacional de Quilmes (UNQ), Argentina
| | - Hernán De Battista
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
| | - Fabricio Garelli
- Grupo de Control Aplicado (GCA), Instituto LEICI (UNLP-CONICET), Facultad de Ingeniería, Universidad Nacional de La Plata (UNLP), Argentina
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
| | - Ricardo Sánchez-Peña
- Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET) Argentina
- Universidad Nacional de Quilmes (UNQ), Argentina
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13
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Garcia-Tirado J, Corbett JP, Boiroux D, Jørgensen JB, Breton MD. Closed-Loop Control with Unannounced Exercise for Adults with Type 1 Diabetes using the Ensemble Model Predictive Control. JOURNAL OF PROCESS CONTROL 2019; 80:202-210. [PMID: 32831483 PMCID: PMC7437946 DOI: 10.1016/j.jprocont.2019.05.017] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/11/2023]
Abstract
This paper presents an individualized Ensemble Model Predictive Control (EnMPC) algorithm for blood glucose (BG) stabilization and hypoglycemia prevention in people with type 1 diabetes (T1D) who exercise regularly. The EnMPC formulation can be regarded as a simplified multi-stage MPC allowing for the consideration of N en scenarios gathered from the patient's recent behavior. The patient's physical activity behavior is characterized by an exercise-specific input signal derived from the deconvolution of the patient's continuous glucose monitor (CGM), accounting for known inputs such as meal, and insulin pump records. The EnMPC controller was tested in a cohort of in silico patients with representative inter-subject and intra-subject variability from the FDA-accepted UVA/Padova simulation platform. Results show a significant improvement on hypoglycemia prevention after 30 min of mild to moderate exercise in comparison to a similarly tuned baseline controller (rMPC); with a reduction in hypoglycemia occurrences (< 70 mg/dL), from 3.08% ± 3.55 with rMPC to 0.78% ± 2.04 with EnMPC (P < 0.05).
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Affiliation(s)
- Jose Garcia-Tirado
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
| | - John P. Corbett
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
- Department of Systems and Information Engineering, University of Virginia, Charlottesville, VA
| | - Dimitri Boiroux
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
- Danish Diabetes Academy, DK-5000 Odense, Denmark
| | - John Bagterp Jørgensen
- Department of Applied Mathematics and Computer Science, Technical University of Denmark, DK-2800 Kgs. Lyngby, Denmark
| | - Marc D Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA
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14
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Artificial Pancreas: Current Progress and Future Outlook in the Treatment of Type 1 Diabetes. Drugs 2019; 79:1089-1101. [DOI: 10.1007/s40265-019-01149-2] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.6] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/12/2022]
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15
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Benhamou PY, Franc S, Reznik Y, Thivolet C, Schaepelynck P, Renard E, Guerci B, Chaillous L, Lukas-Croisier C, Jeandidier N, Hanaire H, Borot S, Doron M, Jallon P, Xhaard I, Melki V, Meyer L, Delemer B, Guillouche M, Schoumacker-Ley L, Farret A, Raccah D, Lablanche S, Joubert M, Penfornis A, Charpentier G. Closed-loop insulin delivery in adults with type 1 diabetes in real-life conditions: a 12-week multicentre, open-label randomised controlled crossover trial. LANCET DIGITAL HEALTH 2019; 1:e17-e25. [DOI: 10.1016/s2589-7500(19)30003-2] [Citation(s) in RCA: 62] [Impact Index Per Article: 12.4] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Received: 01/30/2019] [Revised: 03/01/2019] [Accepted: 03/06/2019] [Indexed: 12/31/2022]
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16
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Forlenza GP, Kushner T, Messer LH, Wadwa RP, Sankaranarayanan S. Factory-Calibrated Continuous Glucose Monitoring: How and Why It Works, and the Dangers of Reuse Beyond Approved Duration of Wear. Diabetes Technol Ther 2019; 21:222-229. [PMID: 30817171 PMCID: PMC6477582 DOI: 10.1089/dia.2018.0401] [Citation(s) in RCA: 18] [Impact Index Per Article: 3.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/21/2023]
Abstract
Continuous glucose monitors (CGM) display real-time glucose values enabling greater glycemic awareness with reduced management burden. Factory-calibrated CGM systems allow for glycemic assessment without the pain and inconvenience of fingerstick glucose testing. Advances in sensor chemistry and CGM algorithms have enabled factory-calibrated systems to have greater accuracy than previous generations of CGM technology. Despite these advances many patients and providers are hesitant about the idea of removing fingerstick testing from their diabetes care. In this commentary, we aim to review the clinical trials on factory-calibrated CGM systems, present the algorithms which facilitate factory-calibrated CGMs to improve accuracy, discuss clinical use of factory-calibrated CGMs, and finally present two cases demonstrating the dangers of utilizing exploits in commercial systems to prolong sensor life.
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Affiliation(s)
- Gregory P. Forlenza
- Department of Pediatric Endocrinology, Barbara Davis Center, University of Colorado Denver, Aurora, Colorado
- Address correspondence to: Gregory P. Forlenza, MD, Department of Pediatric Endocrinology, Barbara Davis Center, University of Colorado Denver, 1775 Aurora CT, MS A140, Aurora, CO 80045
| | - Taisa Kushner
- Department of Computer Science, University of Colorado, Boulder, Colorado
| | - Laurel H. Messer
- Department of Pediatric Endocrinology, Barbara Davis Center, University of Colorado Denver, Aurora, Colorado
| | - R. Paul Wadwa
- Department of Pediatric Endocrinology, Barbara Davis Center, University of Colorado Denver, Aurora, Colorado
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17
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Deshpande S, Pinsker JE, Zavitsanou S, Shi D, Tompot R, Church MM, Andre C, Doyle FJ, Dassau E. 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. Diabetes Technol Ther 2019; 21:35-43. [PMID: 30547670 PMCID: PMC6350072 DOI: 10.1089/dia.2018.0278] [Citation(s) in RCA: 28] [Impact Index Per Article: 5.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/10/2023]
Abstract
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.
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Affiliation(s)
- Sunil Deshpande
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
| | | | - Stamatina Zavitsanou
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Dawei Shi
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
| | | | - Mei Mei Church
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Camille Andre
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Francis J. Doyle
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, Cambridge, Massachusetts
- Sansum Diabetes Research Institute, Santa Barbara, California
- Joslin Diabetes Center, Boston, Massachusetts
- Address correspondence to: Eyal Dassau, PhD, Harvard John A. Paulson School of Engineering and Applied Sciences, Harvard University, 29 Oxford Street, Room 317, Cambridge, MA 02138
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18
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Sherr JL, Tauschmann M, Battelino T, de Bock M, Forlenza G, Roman R, Hood KK, Maahs DM. ISPAD Clinical Practice Consensus Guidelines 2018: Diabetes technologies. Pediatr Diabetes 2018; 19 Suppl 27:302-325. [PMID: 30039513 DOI: 10.1111/pedi.12731] [Citation(s) in RCA: 128] [Impact Index Per Article: 21.3] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2018] [Accepted: 07/10/2018] [Indexed: 12/12/2022] Open
Affiliation(s)
- Jennifer L Sherr
- Department of Pediatrics, Yale School of Medicine, Yale University, New Haven, Connecticut
| | - Martin Tauschmann
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, UK.,Department of Paediatrics, University of Cambridge, Cambridge, UK
| | - Tadej Battelino
- UMC-University Children's Hospital, Ljubljana, Slovenia.,Faculty of Medicine, University of Ljubljana, Ljubljana, Slovenia
| | - Martin de Bock
- Department of Paediatrics, University of Otago, Christchurch, New Zealand
| | - Gregory Forlenza
- University of Colorado Denver, Barbara Davis Center, Aurora, Colorado
| | - Rossana Roman
- Medical Sciences Department, University of Antofagasta and Antofagasta Regional Hospital, Antofagasta, Chile
| | - Korey K Hood
- Department of Psychiatry & Behavioral Sciences, Stanford University School of Medicine, Palo Alto, California
| | - David M Maahs
- Department of Pediatrics, Stanford University School of Medicine, Palo Alto, California
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19
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Sánchez-Peña R, Colmegna P, Garelli F, De Battista H, García-Violini D, Moscoso-Vásquez M, Rosales N, Fushimi E, Campos-Náñez E, Breton M, Beruto V, Scibona P, Rodriguez C, Giunta J, Simonovich V, Belloso WH, Cherñavvsky D, Grosembacher L. Artificial Pancreas: Clinical Study in Latin America Without Premeal Insulin Boluses. J Diabetes Sci Technol 2018; 12:914-925. [PMID: 29998754 PMCID: PMC6134619 DOI: 10.1177/1932296818786488] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/18/2022]
Abstract
BACKGROUND Emerging therapies such as closed-loop (CL) glucose control, also known as artificial pancreas (AP) systems, have shown significant improvement in type 1 diabetes mellitus (T1DM) management. However, demanding patient intervention is still required, particularly at meal times. To reduce treatment burden, the automatic regulation of glucose (ARG) algorithm mitigates postprandial glucose excursions without feedforward insulin boluses. This work assesses feasibility of this new strategy in a clinical trial. METHODS A 36-hour pilot study was performed on five T1DM subjects to validate the ARG algorithm. Subjects wore a subcutaneous continuous glucose monitor (CGM) and an insulin pump. Insulin delivery was solely commanded by the ARG algorithm, without premeal insulin boluses. This was the first clinical trial in Latin America to validate an AP controller. RESULTS For the total 36-hour period, results were as follows: average time of CGM readings in range 70-250 mg/dl: 88.6%, in range 70-180 mg/dl: 74.7%, <70 mg/dl: 5.8%, and <50 mg/dl: 0.8%. Results improved analyzing the final 15-hour period of this trial. In that case, the time spent in range was 70-250 mg/dl: 94.7%, in range 70-180 mg/dl: 82.6%, <70 mg/dl: 4.1%, and <50 mg/dl: 0.2%. During the last night the time spent in range was 70-250 mg/dl: 95%, in range 70-180 mg/dl: 87.7%, <70 mg/dl: 5.0%, and <50 mg/dl: 0.0%. No severe hypoglycemia occurred. No serious adverse events were reported. CONCLUSIONS The ARG algorithm was successfully validated in a pilot clinical trial, encouraging further tests with a larger number of patients and in outpatient settings.
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Affiliation(s)
- Ricardo Sánchez-Peña
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Ricardo Sánchez-Peña, PhD, National Scientific and Technical Research Council (CONICET), Instituto Tecnológico de Buenos Aires (ITBA), Av Madero 399, Buenos Aires, C1106ACD, Argentina.
| | - Patricio Colmegna
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- University of Virginia, Charlottesville, VA, USA
- Universidad Nacional de Quilmes, Bernal, Buenos Aires, Argentina
| | - Fabricio Garelli
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Universidad Nacional de La Plata, La Plata, Buenos Aires Argentina
| | - Hernán De Battista
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Universidad Nacional de La Plata, La Plata, Buenos Aires Argentina
| | - Demián García-Violini
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Marcela Moscoso-Vásquez
- Instituto Tecnológico de Buenos Aires, Buenos Aires, Argentina
- National Scientific and Technical Research Council, Buenos Aires, Argentina
| | - Nicolás Rosales
- Universidad Nacional de La Plata, La Plata, Buenos Aires Argentina
| | - Emilia Fushimi
- National Scientific and Technical Research Council, Buenos Aires, Argentina
- Universidad Nacional de La Plata, La Plata, Buenos Aires Argentina
| | | | - Marc Breton
- University of Virginia, Charlottesville, VA, USA
| | - Valeria Beruto
- Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | - Paula Scibona
- Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
| | | | - Javier Giunta
- Hospital Italiano de Buenos Aires, Buenos Aires, Argentina
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Hajizadeh I, Rashid M, Turksoy K, Samadi S, Feng J, Sevil M, Hobbs N, Lazaro C, Maloney Z, Littlejohn E, Cinar A. Incorporating Unannounced Meals and Exercise in Adaptive Learning of Personalized Models for Multivariable Artificial Pancreas Systems. J Diabetes Sci Technol 2018; 12:953-966. [PMID: 30060699 PMCID: PMC6134614 DOI: 10.1177/1932296818789951] [Citation(s) in RCA: 32] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
BACKGROUND Despite the recent advancements in the modeling of glycemic dynamics for type 1 diabetes mellitus, automatically considering unannounced meals and exercise without manual user inputs remains challenging. METHOD An adaptive model identification technique that incorporates exercise information and estimates of the effects of unannounced meals obtained automatically without user input is proposed in this work. The effects of the unknown consumed carbohydrates are estimated using an individualized unscented Kalman filtering algorithm employing an augmented glucose-insulin dynamic model, and exercise information is acquired from noninvasive physiological measurements. The additional information on meals and exercise is incorporated with personalized estimates of plasma insulin concentration and glucose measurement data in an adaptive model identification algorithm. RESULTS The efficacy of the proposed personalized and adaptive modeling algorithm is demonstrated using clinical data involving closed-loop experiments of the artificial pancreas system, and the results demonstrate accurate glycemic modeling with the average root-mean-square error (mean absolute error) of 25.50 mg/dL (18.18 mg/dL) for six-step (30 minutes ahead) predictions. CONCLUSIONS The approach presented is able to identify reliable time-varying individualized glucose-insulin models.
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Affiliation(s)
- Iman Hajizadeh
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mudassir Rashid
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Kamuran Turksoy
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Sediqeh Samadi
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Jianyuan Feng
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Mert Sevil
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Nicole Hobbs
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Caterina Lazaro
- Department of Electrical and Computer Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Zacharie Maloney
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
| | - Elizabeth Littlejohn
- Department of Pediatrics and Medicine, Section of Endocrinology, Kovler Diabetes Center, University of Chicago, Chicago, IL, USA
| | - Ali Cinar
- Department of Chemical and Biological Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Department of Biomedical Engineering, Illinois Institute of Technology, Chicago, IL, USA
- Ali Cinar, PhD, Illinois Institute of Technology, Department of Chemical and Biological Engineering, 10 W 33rd St, Chicago, IL 60616, USA.
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Shi D, Dassau E, Doyle FJ. Adaptive Zone Model Predictive Control of Artificial Pancreas Based on Glucose- and Velocity-Dependent Control Penalties. IEEE Trans Biomed Eng 2018; 66:1045-1054. [PMID: 30142748 DOI: 10.1109/tbme.2018.2866392] [Citation(s) in RCA: 25] [Impact Index Per Article: 4.2] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/27/2023]
Abstract
OBJECTIVE Zone model predictive control (MPC) has been proven to be an efficient approach to closed-loop insulin delivery in clinical studies. In this paper, we aim to safely reduce mean glucose levels by proposing control penalty adaptation in the cost function of zone MPC. METHODS A zone MPC method with a dynamic cost function that updates its control penalty parameters in real time according to the predicted glucose and its rate of change is developed. The proposed method is evaluated on the entire 100-adult cohort of the FDA-accepted UVA/Padova T1DM simulator and compared with the zone MPC tested in an extended outpatient study. RESULTS For unannounced meals, the proposed method leads to statistically significant improvements in terms of mean glucose (153.8 mg/dL vs. 159.0 mg/dL; ) and percentage time in [70, 180] mg/dL ([Formula: see text] vs. [Formula: see text]; ) without increasing the risk of hypoglycemia. Performance for announced meals is similar to that obtained without adaptation. The proposed method also behaves properly and safely for scenarios of moderate meal-bolus and basal rate mismatches, as well as simulated unannounced exercise. Advisory-mode analysis based on clinical data indicates that the method can reduce glucose levels through suggesting additional safe amounts of insulin on top of those suggested by the zone MPC used in the study. CONCLUSION The proposed method leads to improved glucose control without increasing hypoglycemia risks. SIGNIFICANCE The results validate the feasibility of improving glucose regulation through glucose- and velocity-dependent control penalty adaptation in MPC design.
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Majeed W, Thabit H. Closed-loop insulin delivery: current status of diabetes technologies and future prospects. Expert Rev Med Devices 2018; 15:579-590. [PMID: 30027775 DOI: 10.1080/17434440.2018.1503530] [Citation(s) in RCA: 12] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 02/07/2023]
Abstract
INTRODUCTION Type 1 diabetes is characterised by destruction of pancreatic beta cells, leading to insulin deficiency and hyperglycaemia. The mainstay of treatment remains lifelong insulin therapy as a sustainable cure has as yet proven elusive. The burden of daily management of type 1 diabetes has contributed to suboptimal outcomes for people living with the condition. Innovative technological approaches have been shown to improve glycaemic and patient-related outcomes. AREAS COVERED We discuss recent advances in technologies in type 1 diabetes including closed-loop systems, also known as the 'artificial pancreas. Its various components, technical aspects and limitations are reviewed. We also discuss its advent into clinical practice, and other systems in development. Evidence from clinical studies are summarised. EXPERT COMMENTARY The recent approval of a hybrid closed-loop system for clinical use highlights the significant progress made in this field. Results from clinical studies have shown safety and glycaemic benefit, however challenges remain around improving performance and acceptability. More data is required to establish long-term clinical efficacy and cost-effectiveness, to fulfil the expectations of people with type 1 diabetes.
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Affiliation(s)
- Waseem Majeed
- a Manchester Academic Health Science Centre , Manchester University Hospitals NHS Foundation Trust , Manchester , UK
| | - Hood Thabit
- a Manchester Academic Health Science Centre , Manchester University Hospitals NHS Foundation Trust , Manchester , UK.,b Division of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health , University of Manchester , Manchester , UK
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23
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Forlenza GP, Cameron FM, Ly TT, Lam D, Howsmon DP, Baysal N, Kulina G, Messer L, Clinton P, Levister C, Patek SD, Levy CJ, Wadwa RP, Maahs DM, Bequette BW, Buckingham BA. Fully Closed-Loop Multiple Model Probabilistic Predictive Controller Artificial Pancreas Performance in Adolescents and Adults in a Supervised Hotel Setting. Diabetes Technol Ther 2018; 20:335-343. [PMID: 29658779 PMCID: PMC5963546 DOI: 10.1089/dia.2017.0424] [Citation(s) in RCA: 52] [Impact Index Per Article: 8.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/12/2022]
Abstract
BACKGROUND Initial Food and Drug Administration-approved artificial pancreas (AP) systems will be hybrid closed-loop systems that require prandial meal announcements and will not eliminate the burden of premeal insulin dosing. Multiple model probabilistic predictive control (MMPPC) is a fully closed-loop system that uses probabilistic estimation of meals to allow for automated meal detection. In this study, we describe the safety and performance of the MMPPC system with announced and unannounced meals in a supervised hotel setting. RESEARCH DESIGN AND METHODS The Android phone-based AP system with remote monitoring was tested for 72 h in six adults and four adolescents across three clinical sites with daily exercise and meal challenges involving both three announced (manual bolus by patient) and six unannounced (no bolus by patient) meals. Safety criteria were predefined. Controller aggressiveness was adapted daily based on prior hypoglycemic events. RESULTS Mean 24-h continuous glucose monitor (CGM) was 157.4 ± 14.4 mg/dL, with 63.6 ± 9.2% of readings between 70 and 180 mg/dL, 2.9 ± 2.3% of readings <70 mg/dL, and 9.0 ± 3.9% of readings >250 mg/dL. Moderate hyperglycemia was relatively common with 24.6 ± 6.2% of readings between 180 and 250 mg/dL, primarily within 3 h after a meal. Overnight mean CGM was 139.6 ± 27.6 mg/dL, with 77.9 ± 16.4% between 70 and 180 mg/dL, 3.0 ± 4.5% <70 mg/dL, 17.1 ± 14.9% between 180 and 250 mg/dL, and 2.0 ± 4.5%> 250 mg/dL. Postprandial hyperglycemia was more common for unannounced meals compared with announced meals (4-h postmeal CGM 197.8 ± 44.1 vs. 140.6 ± 35.0 mg/dL; P < 0.001). No participants met safety stopping criteria. CONCLUSIONS MMPPC was safe in a supervised setting despite meal and exercise challenges. Further studies are needed in a less supervised environment.
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Affiliation(s)
| | - Faye M. Cameron
- Department of Chemical and Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | - Trang T. Ly
- Division of Pediatric Endocrinology, Stanford University, Palo Alto, California
| | - David Lam
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Daniel P. Howsmon
- Department of Chemical and Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | - Nihat Baysal
- Department of Chemical and Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | - Georgia Kulina
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Laurel Messer
- Division of Pediatric Endocrinology, Barbara Davis Center, Aurora, Colorado
| | - Paula Clinton
- Division of Pediatric Endocrinology, Stanford University, Palo Alto, California
| | - Camilla Levister
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - Stephen D. Patek
- Center for Diabetes Technology, University of Virginia, Charlottesville, Virginia
| | - Carol J. Levy
- Division of Endocrinology, Icahn School of Medicine at Mount Sinai, New York City, New York
| | - R. Paul Wadwa
- Division of Pediatric Endocrinology, Barbara Davis Center, Aurora, Colorado
| | - David M. Maahs
- Division of Pediatric Endocrinology, Barbara Davis Center, Aurora, Colorado
- Division of Pediatric Endocrinology, Stanford University, Palo Alto, California
| | - B. Wayne Bequette
- Department of Chemical and Biomedical Engineering, Rensselaer Polytechnic Institute, Troy, New York
| | - Bruce A. Buckingham
- Division of Pediatric Endocrinology, Stanford University, Palo Alto, California
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24
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Howsmon DP, Baysal N, Buckingham BA, Forlenza GP, Ly TT, Maahs DM, Marcal T, Towers L, Mauritzen E, Deshpande S, Huyett LM, Pinsker JE, Gondhalekar R, Doyle FJ, Dassau E, Hahn J, Bequette BW. Real-Time Detection of Infusion Site Failures in a Closed-Loop Artificial Pancreas. J Diabetes Sci Technol 2018; 12:599-607. [PMID: 29390915 PMCID: PMC6154252 DOI: 10.1177/1932296818755173] [Citation(s) in RCA: 13] [Impact Index Per Article: 2.2] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
BACKGROUND As evidence emerges that artificial pancreas systems improve clinical outcomes for patients with type 1 diabetes, the burden of this disease will hopefully begin to be alleviated for many patients and caregivers. However, reliance on automated insulin delivery potentially means patients will be slower to act when devices stop functioning appropriately. One such scenario involves an insulin infusion site failure, where the insulin that is recorded as delivered fails to affect the patient's glucose as expected. Alerting patients to these events in real time would potentially reduce hyperglycemia and ketosis associated with infusion site failures. METHODS An infusion site failure detection algorithm was deployed in a randomized crossover study with artificial pancreas and sensor-augmented pump arms in an outpatient setting. Each arm lasted two weeks. Nineteen participants wore infusion sets for up to 7 days. Clinicians contacted patients to confirm infusion site failures detected by the algorithm and instructed on set replacement if failure was confirmed. RESULTS In real time and under zone model predictive control, the infusion site failure detection algorithm achieved a sensitivity of 88.0% (n = 25) while issuing only 0.22 false positives per day, compared with a sensitivity of 73.3% (n = 15) and 0.27 false positives per day in the SAP arm (as indicated by retrospective analysis). No association between intervention strategy and duration of infusion sets was observed ( P = .58). CONCLUSIONS As patient burden is reduced by each generation of advanced diabetes technology, fault detection algorithms will help ensure that patients are alerted when they need to manually intervene. Clinical Trial Identifier: www.clinicaltrials.gov,NCT02773875.
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Affiliation(s)
- Daniel P. Howsmon
- Department of Chemical & Biological
Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Nihat Baysal
- Department of Chemical & Biological
Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
| | - Bruce A. Buckingham
- Department of Pediatrics, Division of
Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA
| | | | - Trang T. Ly
- Department of Pediatrics, Division of
Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA
| | - David M. Maahs
- Department of Pediatrics, Division of
Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA
| | - Tatiana Marcal
- Department of Pediatrics, Division of
Pediatric Endocrinology, Stanford University, Palo Alto, CA, USA
| | - Lindsey Towers
- Barbara Davis Center, University of
Colorado Denver, Denver, CO, USA
| | - Eric Mauritzen
- Department of Computer Science and
Engineering, University of California, San Diego, San Diego, CA, USA
| | - Sunil Deshpande
- Harvard John A. Paulson School of
Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Sansum Diabetes Research Institute,
Santa Barbara, CA, USA
| | - Lauren M. Huyett
- Sansum Diabetes Research Institute,
Santa Barbara, CA, USA
- Department of Chemical Engineering,
University of California, Santa Barbara, Santa Barbara, CA, USA
| | | | - Ravi Gondhalekar
- Harvard John A. Paulson School of
Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Sansum Diabetes Research Institute,
Santa Barbara, CA, USA
| | - Francis J. Doyle
- Harvard John A. Paulson School of
Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Sansum Diabetes Research Institute,
Santa Barbara, CA, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of
Engineering and Applied Sciences, Harvard University, Cambridge, MA, USA
- Sansum Diabetes Research Institute,
Santa Barbara, CA, USA
| | - Juergen Hahn
- Department of Chemical & Biological
Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
- Department of Biomedical Engineering,
Rensselaer Polytechnic Institute, Troy, NY, USA
| | - B. Wayne Bequette
- Department of Chemical & Biological
Engineering, Rensselaer Polytechnic Institute, Troy, NY, USA
- B. Wayne Bequette, PhD, Chemical &
Biological Engineering, Rensselaer Polytechnic Institute, 110 8th St, Ricketts
Building, Troy, NY 12180, USA.
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25
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Gondhalekar R, Dassau E, Doyle FJ. Velocity-weighting & velocity-penalty MPC of an artificial pancreas: Improved safety & performance. AUTOMATICA : THE JOURNAL OF IFAC, THE INTERNATIONAL FEDERATION OF AUTOMATIC CONTROL 2018; 91:105-117. [PMID: 30034017 PMCID: PMC6051553 DOI: 10.1016/j.automatica.2018.01.025] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/21/2023]
Abstract
A novel Model Predictive Control (MPC) law for the closed-loop operation of an Artificial Pancreas (AP) to treat type 1 diabetes is proposed. The contribution of this paper is to simultaneously enhance both the safety and performance of an AP, by reducing the incidence of controller-induced hypoglycemia, and by promoting assertive hyperglycemia correction. This is achieved by integrating two MPC features separately introduced by the authors previously to independently improve the control performance with respect to these two coupled issues. Velocity-weighting MPC reduces the occurrence of controller-induced hypoglycemia. Velocity-penalty MPC yields more effective hyperglycemia correction. Benefits of the proposed MPC law over the MPC strategy deployed in the authors' previous clinical trial campaign are demonstrated via a comprehensive in-silico analysis. The proposed MPC law was deployed in four distinct US Food & Drug Administration approved clinical trial campaigns, the most extensive of which involved 29 subjects each spending three months in closed-loop. The paper includes implementation details, an explanation of the state-dependent cost functions required for velocity-weighting and penalties, a discussion of the resulting nonlinear optimization problem, a description of the four clinical trial campaigns, and control-related trial highlights.
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Affiliation(s)
- Ravi Gondhalekar
- Harvard John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Eyal Dassau
- Harvard John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA
| | - Francis J Doyle
- Harvard John A. Paulson School of Engineering & Applied Sciences, Harvard University, Cambridge, MA, USA
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26
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Bekiari E, Kitsios K, Thabit H, Tauschmann M, Athanasiadou E, Karagiannis T, Haidich AB, Hovorka R, Tsapas A. Artificial pancreas treatment for outpatients with type 1 diabetes: systematic review and meta-analysis. BMJ 2018; 361:k1310. [PMID: 29669716 PMCID: PMC5902803 DOI: 10.1136/bmj.k1310] [Citation(s) in RCA: 246] [Impact Index Per Article: 41.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/20/2022]
Abstract
OBJECTIVE To evaluate the efficacy and safety of artificial pancreas treatment in non-pregnant outpatients with type 1 diabetes. DESIGN Systematic review and meta-analysis of randomised controlled trials. DATA SOURCES Medline, Embase, Cochrane Library, and grey literature up to 2 February 2018. ELIGIBILITY CRITERIA FOR SELECTING STUDIES Randomised controlled trials in non-pregnant outpatients with type 1 diabetes that compared the use of any artificial pancreas system with any type of insulin based treatment. Primary outcome was proportion (%) of time that sensor glucose level was within the near normoglycaemic range (3.9-10 mmol/L). Secondary outcomes included proportion (%) of time that sensor glucose level was above 10 mmol/L or below 3.9 mmol/L, low blood glucose index overnight, mean sensor glucose level, total daily insulin needs, and glycated haemoglobin. The Cochrane Collaboration risk of bias tool was used to assess study quality. RESULTS 40 studies (1027 participants with data for 44 comparisons) were included in the meta-analysis. 35 comparisons assessed a single hormone artificial pancreas system, whereas nine comparisons assessed a dual hormone system. Only nine studies were at low risk of bias. Proportion of time in the near normoglycaemic range (3.9-10.0 mmol/L) was significantly higher with artificial pancreas use, both overnight (weighted mean difference 15.15%, 95% confidence interval 12.21% to 18.09%) and over a 24 hour period (9.62%, 7.54% to 11.7%). Artificial pancreas systems had a favourable effect on the proportion of time with sensor glucose level above 10 mmol/L (-8.52%, -11.14% to -5.9%) or below 3.9 mmol/L (-1.49%, -1.86% to -1.11%) over 24 hours, compared with control treatment. Robustness of findings for the primary outcome was verified in sensitivity analyses, by including only trials at low risk of bias (11.64%, 9.1% to 14.18%) or trials under unsupervised, normal living conditions (10.42%, 8.63% to 12.2%). Results were consistent in a subgroup analysis both for single hormone and dual hormone artificial pancreas systems. CONCLUSIONS Artificial pancreas systems are an efficacious and safe approach for treating outpatients with type 1 diabetes. The main limitations of current research evidence on artificial pancreas systems are related to inconsistency in outcome reporting, small sample size, and short follow-up duration of individual trials.
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Affiliation(s)
- Eleni Bekiari
- Clinical Research and Evidence Based Medicine Unit, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece
| | - Konstantinos Kitsios
- Diabetes Centre, Second Medical Department, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Hood Thabit
- Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Martin Tauschmann
- Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Eleni Athanasiadou
- Clinical Research and Evidence Based Medicine Unit, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece
| | - Thomas Karagiannis
- Clinical Research and Evidence Based Medicine Unit, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece
| | - Anna-Bettina Haidich
- Department of Hygiene and Epidemiology, Medical School, Aristotle University of Thessaloniki, Thessaloniki, Greece
| | - Roman Hovorka
- Wellcome Trust-Medical Research Council Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Apostolos Tsapas
- Clinical Research and Evidence Based Medicine Unit, Aristotle University of Thessaloniki, 54642 Thessaloniki, Greece
- Harris Manchester College, University of Oxford, Oxford, UK
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Visentin R, Campos-Náñez E, Schiavon M, Lv D, Vettoretti M, Breton M, Kovatchev BP, Dalla Man C, Cobelli C. The UVA/Padova Type 1 Diabetes Simulator Goes From Single Meal to Single Day. J Diabetes Sci Technol 2018; 12:273-281. [PMID: 29451021 PMCID: PMC5851236 DOI: 10.1177/1932296818757747] [Citation(s) in RCA: 99] [Impact Index Per Article: 16.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/24/2022]
Abstract
BACKGROUND A new version of the UVA/Padova Type 1 Diabetes (T1D) Simulator is presented which provides a more realistic testing scenario. The upgrades to the previous simulator, which was accepted by the Food and Drug Administration in 2013, are described. METHOD Intraday variability of insulin sensitivity (SI) has been modeled, based on clinical T1D data, accounting for both intra- and intersubject variability of daily SI. Thus, time-varying distributions of both subject's basal insulin infusion and insulin-to-carbohydrate ratio were calculated and made available to the user. A model of "dawn" phenomenon based on clinical T1D data has been also included. Moreover, the model of subcutaneous insulin delivery has been updated with a recently developed model of commercially available fast-acting insulin analogs. Models of both intradermal and inhaled insulin pharmacokinetics have been included. Finally, new models of error affecting continuous glucose monitoring and self-monitoring of blood glucose devices have been added. RESULTS One hundred in silico adults, adolescent, and children have been generated according to the above modifications. The new simulator reproduces the intraday glucose variability observed in clinical data, also describing the nocturnal glucose increase, and the simulated insulin profiles reflect real life data. CONCLUSIONS The new modifications introduced in the T1D simulator allow to extend its domain of validity from "single-meal" to "single-day" scenarios, thus enabling a more realistic framework for in silico testing of advanced diabetes technologies including glucose sensors, new insulin molecules and artificial pancreas.
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Affiliation(s)
- Roberto Visentin
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Enrique Campos-Náñez
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Michele Schiavon
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Dayu Lv
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Martina Vettoretti
- Department of Information Engineering, University of Padova, Padova, Italy
| | - Marc Breton
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Boris P. Kovatchev
- Center for Diabetes Technology, University of Virginia, Charlottesville, VA, USA
| | - Chiara Dalla Man
- Department of Information Engineering, University of Padova, Padova, Italy
- Chiara Dalla Man, PhD, Department of Information Engineering, University of Padova, Via Gradenigo 6/b, 35131 Padova, Italy.
| | - Claudio Cobelli
- Department of Information Engineering, University of Padova, Padova, Italy
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28
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Bally L, Thabit H, Hovorka R. Glucose-responsive insulin delivery for type 1 diabetes: The artificial pancreas story. Int J Pharm 2017; 544:309-318. [PMID: 29258910 DOI: 10.1016/j.ijpharm.2017.12.022] [Citation(s) in RCA: 21] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/04/2017] [Revised: 12/04/2017] [Accepted: 12/10/2017] [Indexed: 12/20/2022]
Abstract
Insulin replacement therapy is integral to the management of type 1 diabetes, which is characterised by absolute insulin deficiency. Optimal glycaemic control, as assessed by glycated haemoglobin, and avoidance of hyper- and hypoglycaemic excursions have been shown to prevent diabetes-related complications. Insulin pump use has increased considerably over the past decade with beneficial effects on glycaemic control, quality of life and treatment satisfaction. The advent and progress of ambulatory glucose sensor technology has enabled continuous glucose monitoring based on real-time glucose levels to be integrated with insulin therapy. Low glucose and predictive low glucose suspend systems are currently used in clinical practice to mitigate against hypoglycaemia, and provide the first step towards feedback glucose control. The more advanced technology approach, an artificial pancreas or a closed-loop system, gradually increases and decreases insulin delivery in a glucose-responsive fashion to mitigate against hyper- and hypoglycaemia. Randomised outpatient clinical trials over the past 5 years have demonstrated the feasibility, safety and efficacy of the approach, and the recent FDA approval of the first single hormone closed-loop system establishes a new standard of care for people with type 1 diabetes.
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Affiliation(s)
- Lia Bally
- Department of Diabetes, Endocrinology Clinical Nutrition & Metabolism, Inselspital, Bern University Hospital, University of Bern, Switzerland; Department of General Internal Medicine, Inselspital, Bern University Hospital, University of Bern, Switzerland.
| | - Hood Thabit
- Central Manchester University Hospitals NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, United Kingdom; Division of Diabetes, Endocrinology and Gastroenterology, Faculty of Biology, Medicine and Health, University of Manchester, United Kingdom
| | - Roman Hovorka
- Wellcome Trust-MRC Institute of Metabolic Science, University of Cambridge, Cambridge, United Kingdom; Department of Paediatrics, University of Cambridge, Cambridge, United Kingdom
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29
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Cao Z, Gondhalekar R, Dassau E, Doyle FJ. Extremum Seeking Control for Personalized Zone Adaptation in Model Predictive Control for Type 1 Diabetes. IEEE Trans Biomed Eng 2017; 65:1859-1870. [PMID: 29989925 DOI: 10.1109/tbme.2017.2783238] [Citation(s) in RCA: 16] [Impact Index Per Article: 2.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/06/2022]
Abstract
Zone model predictive control has proven to be an effective closed-loop method to regulate blood glucose for people with type 1 diabetes (T1D). In this paper, we present a universal model-free optimization scheme for adapting the zone for T1D patients individually. The adaptation is based on a clinical glycemic risk index named relative regularized glycemic penalty index (rrGPI), which is calculated from glucose measurements by a continuous glucose monitor. The scheme's objective is to minimize rrGPI by simultaneously modulating a controller's blood glucose target zone's upper bound and lower bound. The adaptation mechanism is based on extremum seeking control, in which the zone boundaries are driven by gradient estimation obtained by continuously sinusoidally modulating and demodulating the rrGPI readings. To improve the adaptation method's robustness against uncertainties, a decaying feedback gain and a vanishing dither signal are employed. in-silico trials suggested that the personalized optimized zone can be reached within a week of adaptation. Both for announced and unannounced meals, the proposed method outperforms the fixed zone [80, 140] mg/dL, which has been employed in the authors' clinical trials. It is also shown that the developed method has strong robustness against real-life uncertainties.
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