The PILGRIM Study: In Silico Modeling of a Predictive Low Glucose Management System and Feasibility in Youth with Type 1 Diabetes During Exercise

Danne, Thomas; Tsioli, Christiana; Kordonouri, Olga; Blaesig, Sarah; Remus, K; Roy, Anirban; Keenan, Barry; Lee, Scott W.; Kaufman, Francine

doi:10.1089/dia.2013.0327

Cited by 85 publications

(48 citation statements)

References 37 publications

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“…The PLGM system in this study is the same system used in the PILGRIM study. 16 The PILGRIM study used computer simulation under in silico conditions and demonstrated a significant reduction in the time spent hypoglycemic with PLGM than with LGS when virtual participants were administered a manual insulin bolus to induce hypoglycemia. The study also demonstrated the effect of the algorithm with exercise in real patients in in-clinic conditions although in the absence of a control arm.…”

Section: Discussionmentioning

confidence: 99%

Prevention of Insulin-Induced Hypoglycemia in Type 1 Diabetes with Predictive Low Glucose Management System

Abraham

Bock

Paramalingam

et al. 2016

Diabetes Technology & Therapeutics

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Section: Discussionmentioning

confidence: 99%

Prevention of Insulin-Induced Hypoglycemia in Type 1 Diabetes with Predictive Low Glucose Management System

Abraham

Bock

Paramalingam

et al. 2016

Diabetes Technology & Therapeutics

Self Cite

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“…127 An experimental algorithm based on time series forecasting has also been evaluated by inducing hypoglycemia through exercise in a small number of patients, resulting in a reduction in occurrence of hypoglycemia. 128 Other HM approaches have incorporated insulin 129 or historical CGM 130 information and smoothly attenuate insulin delivery, rather than completely suspend/resume insulin delivery. For a more information on hypoglycemia prediction algorithms and HMs, we refer the readers to Bequette.…”

Section: Hypoglycemia Minimizermentioning

confidence: 99%

Hypo- and Hyperglycemic Alarms

Howsmon

Bequette

2015

J Diabetes Sci Technol

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Soon after the discovery that insulin regulates blood glucose by Banting and Best in 1922, the symptoms and risks associated with hypoglycemia became widely recognized. This article reviews devices to warn individuals of impending hypo-and hyperglycemia; biosignals used by these devices include electroencephalography, electrocardiography, skin galvanic resistance, diabetes alert dogs, and continuous glucose monitors (CGMs). While systems based on other technology are increasing in performance and decreasing in size, CGM technology remains the best method for both reactive and predictive alarming of hypo-or hyperglycemia.

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“…55 Similar findings were noted by other studies demonstrating that PGLS was useful in reduction of hypoglycemia in patients with T1DM. 56,57 A multicenter, multinational, randomized, crossover trial evaluated the efficacy of the CL using the fuzzy logic algorithm and changes in insulin delivery based on PLGS in 56 patients with T1DM at a diabetes camp; it was found that PLGS reduced the occurrence of hypoglycemic episodes during the night compared with sensor-augmented pump therapy without any serious adverse events. 58 In summary, clinical trials have shown that both TS and PLGS are safe and effective in reducing nocturnal hypoglycemia and time spent in hypoglycemia.…”

Section: The Current Clinical Evidence For the CL Systemmentioning

confidence: 99%

Closed-Loop System in the Management of Diabetes: Past, Present, and Future

Shah

Shoskes

Tawfik

et al. 2014

Diabetes Technology & Therapeutics

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Intensive insulin therapy (IIT) has been shown to reduce micro- and macrovascular complications in patients with type 1 diabetes mellitus (T1DM). However, IIT is associated with a significant increase in severe hypoglycemic events, resulting in increased morbidity and mortality. Optimization of glycemic control without hypoglycemia (especially nocturnal) should be the next major goal for subjects on insulin treatment. The use of insulin pumps along with continuous glucose monitors (CGMs) has made it easier but requires significant resources and patient education. Research is ongoing to close the loop by integrating the pump and the CGM using different algorithms. The currently available closed-loop system is the threshold suspend. Steps needed to achieve a near-perfect closed-loop are (1) a control-to-range system that will reduce the incidence and/or severity of hyper- and/or hypoglycemia by adjusting the insulin dose and (2) a control-to-target system, a fully automated or hybrid system that sets target glucose levels to individual needs and maintains glucose levels throughout the day using insulin (unihormonal) alone or with other hormones such as glucagon or possibly pramlintide (bihormonal). Future research is also focusing on better insulin delivery devices (pumps), more accurate CGMs, better predictive algorithms, and ultra-rapid-acting insulin analogs to make the closed-loop system as physiological as possible.

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The PILGRIM Study: In Silico Modeling of a Predictive Low Glucose Management System and Feasibility in Youth with Type 1 Diabetes During Exercise

Abstract: In silico modeling and early feasibility data demonstrate that PLGM may further reduce the severity of hypoglycemia beyond that already established for algorithms that use a threshold-based suspension.

Cited by 85 publications

References 37 publications

Prevention of Insulin-Induced Hypoglycemia in Type 1 Diabetes with Predictive Low Glucose Management System

Prevention of Insulin-Induced Hypoglycemia in Type 1 Diabetes with Predictive Low Glucose Management System

Hypo- and Hyperglycemic Alarms

Closed-Loop System in the Management of Diabetes: Past, Present, and Future

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