Hypo- and Hyperglycemic Alarms

Howsmon, Daniel P.; Bequette, B. Wayne

doi:10.1177/1932296815583507

Cited by 26 publications

(7 citation statements)

References 121 publications

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“…A comparative analysis of different hypoglycemia prediction methodologies can be found in the literature [ 11 , 20 , 59 , 60 ]. A straightforward comparison between different hypoglycemia prediction studies is complicated due to differences in CGM sensors used, sampling intervals, and data collection (synthetically generated, controlled study, free-living conditions).…”

Section: Discussionmentioning

confidence: 99%

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

et al. 2021

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Background Predictive alerts for impending hypoglycemic events enable persons with type 1 diabetes to take preventive actions and avoid serious consequences. Objective This study aimed to develop a prediction model for hypoglycemic events with a low false alert rate, high sensitivity and specificity, and good generalizability to new patients and time periods. Methods Performance improvement by focusing on sustained hypoglycemic events, defined as glucose values less than 70 mg/dL for at least 15 minutes, was explored. Two different modeling approaches were considered: (1) a classification-based method to directly predict sustained hypoglycemic events, and (2) a regression-based prediction of glucose at multiple time points in the prediction horizon and subsequent inference of sustained hypoglycemia. To address the generalizability and robustness of the model, two different validation mechanisms were considered: (1) patient-based validation (model performance was evaluated on new patients), and (2) time-based validation (model performance was evaluated on new time periods). Results This study utilized data from 110 patients over 30-90 days comprising 1.6 million continuous glucose monitoring values under normal living conditions. The model accurately predicted sustained events with >97% sensitivity and specificity for both 30- and 60-minute prediction horizons. The false alert rate was kept to <25%. The results were consistent across patient- and time-based validation strategies. Conclusions Providing alerts focused on sustained events instead of all hypoglycemic events reduces the false alert rate and improves sensitivity and specificity. It also results in models that have better generalizability to new patients and time periods.

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Section: Discussionmentioning

confidence: 99%

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

et al. 2021

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“…Although the volume of popular press stories and public interest in DADs continues to rise, diabetes providers are unable to provide informed advice regarding their use. 8-11,13 We studied the reliability of DADs to alert to hypoglycemia in their human companions with type 1 diabetes under real-life conditions using capillary blood glucose (CBG) and blinded continuous glucose monitoring (CGM) as comparators.…”

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confidence: 99%

Reliability of Trained Dogs to Alert to Hypoglycemia in Patients With Type 1 Diabetes

Los

Ramsey

Guttmann‐Bauman

et al. 2016

J Diabetes Sci Technol

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Background: We examined the reliability of trained dogs to alert to hypoglycemia in individuals with type 1 diabetes. Methods: Patients with type 1 diabetes who currently used diabetes alert dogs participated in this exploratory study. Subjects reported satisfaction, perceived dog glucose sensing ability and reasons for obtaining a trained dog. Reliability of dog alerts was assessed using capillary blood glucose (CBG) and blinded continuous glucose monitoring (CGM) as comparators in 8 subjects (age 4-48). Hypoglycemia was defined as CBG or CGM <70 mg/dL. Results: Dog users were very satisfied (8.9/10 on a Likert-type scale) and largely confident (7.9/10) in their dog’s ability to detect hypoglycemia. Detection of hypoglycemia was the primary reason for obtaining a trained dog. During hypoglycemia, spontaneous dog alerts occurred at a rate 3.2 (2.0-5.2, 95% CI) times higher than during euglycemia (70-179 mg/dL). Dogs provided timely alerts in 36% (sensitivity) of all hypoglycemia events (n = 45). Due to inappropriate alerts, the PPV of a dog alert for hypoglycemia was 12%. When there was concurrence of a hypoglycemic event between the dog alert and CGM (n = 30), CGM would have alerted prior to the dog in 73% of events (median 22-minute difference). Conclusions: This is the first study evaluating reliability of trained dogs to alert to hypoglycemia under real-life conditions. Trained dogs often alert a human companion to otherwise unknown hypoglycemia; however due to high false-positive rate, a dog alert alone is unlikely to be helpful in differentiating hypo-/hyper-/euglycemia. CGM often detects hypoglycemia before a trained dog by a clinically significant margin.

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“…Before CGMs were available, there were several devices developed to warn individuals of impending hypo-and hyperglycemia. Howsmon and Bequette 85 review devices that use a variety of biosignals, including electroencephalography, electrocardiography, skin galvanic resistance, diabetes alert dogs, and CGMs.…”

Section: Other Signals For Fault Tolerant Controlmentioning

confidence: 99%

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

Beneyto

Bequette

Vehı́

2021

J Diabetes Sci Technol

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Automated Insulin Delivery (AID) are systems developed for daily use by people with type 1 diabetes (T1D). To ensure the safety of users, it is essential to consider how the human factor affects the performance and safety of these devices. While there are numerous publications on hardware-related failures of AID systems, there are few studies on the human component of the system. From a control point of view, people with T1D using AID systems are at the same time the plant to be controlled and the plant operator. Therefore, users may induce faults in the controller, sensors, actuators, and the plant itself. Strategies to cope with the human interaction in AID systems are needed for further development of the technology. In this paper, we present an analysis of potential faults introduced by AID users when the system is under normal operation. This is followed by a review of current fault tolerant control (FTC) approaches to identify missing areas of research. The paper concludes with a discussion on future directions for the new generation of FTC AID systems.

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Hypo- and Hyperglycemic Alarms

Cited by 26 publications

References 121 publications

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

Improved Low-Glucose Predictive Alerts Based on Sustained Hypoglycemia: Model Development and Validation Study

Reliability of Trained Dogs to Alert to Hypoglycemia in Patients With Type 1 Diabetes

Fault Tolerant Strategies for Automated Insulin Delivery Considering the Human Component: Current and Future Perspectives

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