Intermittent Use of Continuous Glucose Monitoring: Expanding the Clinical Value of CGM

Ziegler, Ralph; Heinemann, Lutz; Freckmann, Guido; Schnell, Oliver; Hinzmann, Rolf; Kulzer, Bernd

doi:10.1177/1932296820905577

Cited by 16 publications

(10 citation statements)

References 113 publications

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“…First, the detailed analysis of CGM data provides an extension of the digital phenotype for both the disease and the individual and allows for the targeting of interventions to times when they will achieve the optimum effect. Second, patients and parents are not passive bystanders in the management of HI, and an analysis of how they interact with and respond to the technology further extends the digital phenotype [ 40 ], as well as enabling and enhancing behavior change [ 41 ]. Knowledge of an extended digital phenotype will not, in and of itself, improve outcomes but does improve understanding of how future interventions can be adapted to achieve the most significant and lasting behavior change [ 42 ].…”

Section: Introductionmentioning

confidence: 99%

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Worth¹,

Harper²,

Salomon‐Estebanez³

et al. 2021

J Med Internet Res

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Background Hyperinsulinism (HI) due to excess and dysregulated insulin secretion is the most common cause of severe and recurrent hypoglycemia in childhood. High cerebral glucose use in the early hours results in a high risk of hypoglycemia in people with diabetes and carries a significant risk of brain injury. Prevention of hypoglycemia is the cornerstone of the management of HI, but the risk of hypoglycemia at night or the timing of hypoglycemia in children with HI has not been studied; thus, the digital phenotype remains incomplete and management suboptimal. Objective This study aims to quantify the timing of hypoglycemia in patients with HI to describe glycemic variability and to extend the digital phenotype. This will facilitate future work using computational modeling to enable behavior change and reduce exposure of patients with HI to injurious hypoglycemic events. Methods Patients underwent continuous glucose monitoring (CGM) with a Dexcom G4 or G6 CGM device as part of their clinical assessment for either HI (N=23) or idiopathic ketotic hypoglycemia (IKH; N=24). The CGM data were analyzed for temporal trends. Hypoglycemia was defined as glucose levels <3.5 mmol/L. Results A total of 449 hypoglycemic events totaling 15,610 minutes were captured over 237 days from 47 patients (29 males; mean age 70 months, SD 53). The mean length of hypoglycemic events was 35 minutes. There was a clear tendency for hypoglycemia in the early hours (3-7 AM), particularly for patients with HI older than 10 months who experienced hypoglycemia 7.6% (1480/19,370 minutes) of time in this period compared with 2.6% (2405/92,840 minutes) of time outside this period (P<.001). This tendency was less pronounced in patients with HI who were younger than 10 months, patients with a negative genetic test result, and patients with IKH. Despite real-time CGM, there were 42 hypoglycemic events from 13 separate patients with HI lasting >30 minutes. Conclusions This is the first study to have taken the first step in extending the digital phenotype of HI by describing the glycemic trends and identifying the timing of hypoglycemia measured by CGM. We have identified the early hours as a time of high hypoglycemia risk for patients with HI and demonstrated that simple provision of CGM data to patients is not sufficient to eliminate hypoglycemia. Future work in HI should concentrate on the early hours as a period of high risk for hypoglycemia and must target personalized hypoglycemia predictions. Focus must move to the human-computer interaction as an aspect of the digital phenotype that is susceptible to change rather than simple mathematical modeling to produce small improvements in hypoglycemia prediction accuracy.

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Section: Introductionmentioning

confidence: 99%

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Worth¹,

Harper²,

Salomon‐Estebanez³

et al. 2021

J Med Internet Res

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“…However, this study, to our knowledge, is the first report that differences in glucose variability can be observed from CGM obtained during free living. A few others have investigated the potential of using CGM in assessing diabetic gastroparesis; 25 however, this is an underexposed field of research, and there is a need for more knowledge on the relationship between gastroparesis and the effect on glucose variability.…”

Section: Discussionmentioning

confidence: 99%

Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study

Cichosz

Hejlesen

2021

J Diabetes Sci Technol

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Background and Objective: Delayed gastric emptying is a substantial challenge for people with diabetes, affecting quality of life and blood glucose regulation. The complication is underdiagnosed, and current diagnostic tests are expensive or time consuming or have modest accuracy. The assessment of glycemic variations has potential use in gastroparesis screening. The aim of this study was to investigate the differences in glycemic variability between type 1 diabetes patients with gastroparesis and without a diagnosis of gastroparesis and the potential for using a classification model to differentiate between groups. Methods: Continuous glucose monitoring (CGM) from 425 patients with diabetes was included in the analytic cohort, including 16 patients with a diagnosis of gastroparesis and 409 without a known gastroparesis diagnosis. Sixteen features (9 daytime features and 7 nighttime features) describing glucose dynamics were extracted to assess differences between patients with and without a diagnosis of gastroparesis. A logistic regression model was trained using forward selection and cross-validation. Results: In total, 3 features were included in the model utilizing forward selection of features and cross-validation: mean absolute glucose (MAG), span, and standard deviation during the night. The Receiver operating characteristic (ROC) AUC for the classification model was 0.76. Conclusions: Gastroparesis seems to have an impact on glucose variability, especially during the night. Moreover, CGM could possibly be used as a part of the screening process for delayed gastric emptying, but more studies are needed to determine a realistic accuracy.

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“…Regardless of whether using realtime or blinded modes, the clinician can use the data collected to assess current glycemic status and variability, enable a conversation to ground and advance education on certain topics of diabetes management, and determine how to optimize treatment whether through behavioral modifications or through adjustments in the medications used or doses prescribed to achieve more targeted glycemia. 13 The use of diagnostic/professional CGM may provide a means to familiarize persons with diabetes with CGM wear and may lead to the integration of personal CGM.…”

Section: Diagnostic/professional/blinded Continuous Glucose Monitoringmentioning

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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Intermittent Use of Continuous Glucose Monitoring: Expanding the Clinical Value of CGM

Cited by 16 publications

References 113 publications

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Clustering of Hypoglycemia Events in Patients With Hyperinsulinism: Extension of the Digital Phenotype Through Retrospective Data Analysis

Classification of Gastroparesis from Glycemic Variability in Type 1 Diabetes: A Proof-of-Concept Study

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

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