A Review of Predictive Low Glucose Suspend and Its Effectiveness in Preventing Nocturnal Hypoglycemia

Chen, Ethan; King, Fraya; Kohn, Michael A.; Spanakis, Elias K.; Breton, Marc D.; Klonoff, David C.

doi:10.1089/dia.2019.0119

Cited by 21 publications

(18 citation statements)

References 29 publications

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“…T1D patients using MDI therapy are more exposed to NH than SAP users [29,30], thus more effort should be directed towards this group. The present work evaluates the feasibility of machine-learning-based prediction models to forecast the occurrence of NH, considering data related with T1D management under MDI therapy and also physical activity tracking.…”

Section: Introductionmentioning

confidence: 99%

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

Bertachi

Viñals

Biagi

et al. 2020

Sensors

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(1) Background: nocturnal hypoglycemia (NH) is one of the most challenging side effects of multiple doses of insulin (MDI) therapy in type 1 diabetes (T1D). This work aimed to investigate the feasibility of a machine-learning-based prediction model to anticipate NH in T1D patients on MDI. (2) Methods: ten T1D adults were studied during 12 weeks. Information regarding T1D management, continuous glucose monitoring (CGM), and from a physical activity tracker were obtained under free-living conditions at home. Supervised machine-learning algorithms were applied to the data, and prediction models were created to forecast the occurrence of NH. Individualized prediction models were generated using multilayer perceptron (MLP) and a support vector machine (SVM). (3) Results: population outcomes indicated that more than 70% of the NH may be avoided with the proposed methodology. The predictions performed by the SVM achieved the best population outcomes, with a sensitivity and specificity of 78.75% and 82.15%, respectively. (4) Conclusions: our study supports the feasibility of using ML techniques to address the prediction of nocturnal hypoglycemia in the daily life of patients with T1D on MDI, using CGM and a physical activity tracker.

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Section: Introductionmentioning

confidence: 99%

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

Bertachi

Viñals

Biagi

et al. 2020

Sensors

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“…74 Today's more advanced integrated systems with LGS, PLGS, and HCL technologies have demonstrated both clinical efficacy and cost-effectiveness in T1D compared with insulin pump therapy alone due to improved glycemic control and reductions in hypoglycemia. 72,75,77,80,81,95,96 A 2019 cost-effectiveness model among 2 T1D cohorts in the Netherlands showed that among participants with suboptimal glycemic control, LGS improved qualityadjusted life expectancy by 1.77 QALYs versus insulin pump therapy alone, with higher lifetime costs (EUR 189 855 vs EUR 150 366), resulting in an ICER of EUR 22 325 per QALY gained 81 For those with an increased risk for hypoglycemia, the use of the system was associated with a 2.16 increase in QALYs with higher lifetime costs (EUR 204 013 vs EUR 171 032), leading to an ICER of EUR 15 243 per QALY gained. 81 The investigators concluded that among individuals with suboptimal glycemic control and/or higher risk for hypoglycemia, switching from CSII without CGM to an integrated system with LGS is cost-effective and will likely result in long-term clinical gains.…”

Section: Continuous Glucose Monitoring Technologiesmentioning

confidence: 99%

“…Pooled estimates from a recent meta-analysis showed that overnight periods of PLGS use were associated with an 8.8% lower risk of nocturnal hypoglycemia than non-PLGS use, which would likely lead to significantly reduced costs. 95 With HCL systems, cost benefits are more definitive. As demonstrated in a 2019 Swedish study, the use of the only HCL system commercially available was associated with a QALY gain of 1.90.…”

Section: Continuous Glucose Monitoring Technologiesmentioning

confidence: 99%

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

et al. 2021

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automated insulin dosing continuous glucose monitoring continuous subcutaneous insulin infusion diabetes diabetes technology glucose sensors hybrid closed loop insulin pumps sensor-augmented pump low-glucose suspend predictive low-glucose suspend a b s t r a c t Objective: To provide evidence-based recommendations regarding the use of advanced technology in the management of persons with diabetes mellitus to clinicians, diabetes-care teams, health care professionals, and other stakeholders. Methods: The American Association of Clinical Endocrinology (AACE) conducted literature searches for relevant articles published from 2012 to 2021. A task force of medical experts developed evidence-based guideline recommendations based on a review of clinical evidence, expertise, and informal consensus, according to established AACE protocol for guideline development. Main Outcome Measures: Primary outcomes of interest included hemoglobin A1C, rates and severity of hypoglycemia, time in range, time above range, and time below range. Results: This guideline includes 37 evidence-based clinical practice recommendations for advanced diabetes technology and contains 357 citations that inform the evidence base.Recommendations: Evidence-based recommendations were developed regarding the efficacy and safety of devices for the management of persons with diabetes mellitus, metrics used to aide with the assessment of advanced diabetes technology, and standards for the implementation of this technology. Conclusions: Advanced diabetes technology can assist persons with diabetes to safely and effectively achieve glycemic targets, improve quality of life, add greater convenience, potentially reduce burden of Disclaimer: The American Association of Clinical Endocrinology medical guidelines for clinical practice are systematically developed statements to assist health care professionals in medical decision-making for specific clinical conditions. Most of the content herein is based on clinical evidence. In areas of uncertainty, or when clarification is required, expert opinion and professional judgment were applied.This guideline is a working document that reflects the state of the field at the time of publication. Because rapid changes are expected in this area, periodic revisions are inevitable. We encourage medical professionals to use this information in conjunction with their best clinical judgment. The presented recommendations may not be appropriate in all situations. Any decision by practitioners to apply these guidelines must be made considering local resources and individual patient circumstances.

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“…97 No studies have been performed to assess the efficacy of these features in reducing the frequency and severity of hypoglycaemia in T2D; studies are limited to the T1D population. 98,99 With the recognition that postprandial glycaemic excursions are particularly challenging, the next phase of development was a "hybrid" closed-loop system, which requires meal announcements and initiation of a pump-delivered meal bolus by the user. The basal rate is automated for the remainder of the time between meals.…”

Section: Glucose-responsive Insulin Deliverymentioning

confidence: 99%

“…Earlier features of glucose responsive insulin delivery systems include the low glucose suspend feature which interrupts insulin delivery below a defined glucose threshold, and predictive low glucose suspend which predicts pending hypoglycaemia and suspends insulin delivery in advance 93 . No studies have been performed to assess the efficacy of these features in reducing frequency and severity of hypoglycaemia in T2D, studies are limited to the T1D population 94 , 95 …”

Section: Glucose Responsive Insulin Deliverymentioning

confidence: 99%

Technology in the management of type 2 diabetes: Present status and future prospects

Daly

Hovorka

2021

Diabetes Obesity Metabolism

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The growing incidence of type 2 diabetes (T2D) is a significant health concern, representing 90% of diabetes cases worldwide. As the disease progresses, resultant insulin deficiency and hyperglycaemia necessitates insulin therapy in many cases. It has been recognized that a significant number of people who have a clinical requirement for insulin therapy, as well as their healthcare professionals, are reluctant to intensify treatment with insulin due to fear of hypoglycaemia, poor understanding of treatment regimens or lack of engagement, and are therefore at higher risk of developing complications from poor glycaemic control. Over the past decade, the rise of diabetes technologies, including dosing advisors, continuous glucose monitoring systems, insulin pumps and automated insulin delivery systems, has led to great improvements in the therapies available, particularly to those requiring insulin. Although the focus has

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A Review of Predictive Low Glucose Suspend and Its Effectiveness in Preventing Nocturnal Hypoglycemia

Cited by 21 publications

References 29 publications

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

Prediction of Nocturnal Hypoglycemia in Adults with Type 1 Diabetes under Multiple Daily Injections Using Continuous Glucose Monitoring and Physical Activity Monitor

American Association of Clinical Endocrinology Clinical Practice Guideline: The Use of Advanced Technology in the Management of Persons With Diabetes Mellitus

Technology in the management of type 2 diabetes: Present status and future prospects

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