In-home nighttime predictive low glucose suspend experience in children and adults with type 1 diabetes

Prevalence of dermatitis including allergic contact dermatitis from medical devices used by children and adults with Type 1 diabetes mellitus: A systematic review and questionnaire study

Use of medical devices (MDs), that is, glucose sensors and insulin pumps, in patients with Type 1 diabetes mellitus (T1D) has proven an enormous advantage for disease control. Adverse skin reactions from these MDs may however hamper compliance. The objective of this study was to systematically review and analyse studies assessing the prevalence and incidence of dermatitis, including allergic contact dermatitis (ACD) related to MDs used in patients with T1D and to compare referral routes and the clinical investigation routines between clinics being part of the European Environmental and Contact Dermatitis Research Group (EECDRG). A systematic search of PubMed, EMBASE, CINAHL and Cochrane databases of full-text studies reporting incidence and prevalence of dermatitis in persons with T1D using MDs was conducted until December 2021. The Newcastle-Ottawa Scale was used to assess study quality. The inventory performed at EECRDG clinics focused on referral routes, patient numbers and the diagnostic process. Among the 3145 screened abstracts, 39 studies fulfilled the inclusion criteria. Sixteen studies included data on children only, 14 studies were on adults and nine studies reported data on both children and adults. Participants were exposed to a broad range of devices. Skin reactions were rarely specified. It was found that both the diagnostic process and referral routes differ in different centres. Further data on the prevalence of skin reactions related to MDs in individuals with T1D is needed and particularly studies where the skin reactions are correctly diagnosed. A correct diagnosis is delayed or hampered by the fact that, at present, the actual substances within the MDs are not declared, are changed without notice and the commercially available test materials are not adequately updated. Within Europe, routines for referral should be made more standardized to improve the diagnostic procedure when investigating patients with possible ACD from MDs.

Feature-Based Machine Learning Model for Real-Time Hypoglycemia Prediction

BACKGROUND

Hypoglycemia is a serious health concern in youth with type 1 diabetes (T1D). Real-time data from continuous glucose monitoring (CGM) can be used to predict hypoglycemic risk, allowing patients to take timely intervention measures.

METHODS

A machine learning model is developed for probabilistic prediction of hypoglycemia (<70 mg/dL) in 30- and 60-minute time horizons based on CGM datasets obtained from 112 patients over a range of 90 days consisting of over 1.6 million CGM values under normal living conditions. A comprehensive set of features relevant for hypoglycemia are developed and a parsimonious subset with most influence on predicting hypoglycemic risk is identified. Model performance is evaluated both with and without contextual information on insulin and carbohydrate intake.

RESULTS

The model predicted hypoglycemia with >91% sensitivity for 30- and 60-minute prediction horizons while maintaining specificity >90%. Inclusion of insulin and carbohydrate data yielded performance improvement for 60-minute but not for 30-minute predictions. Model performance was highest for nocturnal hypoglycemia (~95% sensitivity). Shortterm (less than one hour) and medium-term (one to four hours) features for good prediction performance are identified.

CONCLUSIONS

Innovative feature identification facilitated high performance for hypoglycemia risk prediction in pediatric youth with T1D. Timely alerts of impending hypoglycemia may enable proactive measures to avoid severe hypoglycemia and achieve optimal glycemic control. The model will be deployed on a patient-facing smartphone application in an upcoming pilot study.

Technological Ecological Momentary Assessment Tools to Study Type 1 Diabetes in Youth: Viewpoint of Methodologies

Type 1 diabetes (T1D) is one of the most common chronic childhood diseases, and its prevalence is rapidly increasing. The management of glucose in T1D is challenging, as youth must consider a myriad of factors when making diabetes care decisions. This task often leads to significant hyperglycemia, hypoglycemia, and glucose variability throughout the day, which have been associated with short- and long-term medical complications. At present, most of what is known about each of these complications and the health behaviors that may lead to them have been uncovered in the clinical setting or in laboratory-based research. However, the tools often used in these settings are limited in their ability to capture the dynamic behaviors, feelings, and physiological changes associated with T1D that fluctuate from moment to moment throughout the day. A better understanding of T1D in daily life could potentially aid in the development of interventions to improve diabetes care and mitigate the negative medical consequences associated with it. Therefore, there is a need to measure repeated, real-time, and real-world features of this disease in youth. This approach is known as ecological momentary assessment (EMA), and it has considerable advantages to in-lab research. Thus, this viewpoint aims to describe EMA tools that have been used to collect data in the daily lives of youth with T1D and discuss studies that explored the nuances of T1D in daily life using these methods. This viewpoint focuses on the following EMA methods: continuous glucose monitoring, actigraphy, ambulatory blood pressure monitoring, personal digital assistants, smartphones, and phone-based systems. The viewpoint also discusses the benefits of using EMA methods to collect important data that might not otherwise be collected in the laboratory and the limitations of each tool, future directions of the field, and possible clinical implications for their use.

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

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.

Insulin delivery and nocturnal glucose control in children and adolescents with type 1 diabetes

Introduction: Nocturnal glucose control remains challenging in children and adolescents with type 1 diabetes due to highly variable overnight insulin requirements. The issue may be addressed by glucose responsive insulin delivery based on real-time continuous glucose measurements.

Areas covered: This review outlines recent developments of glucose responsive insulin delivery systems from a paediatric perspective. We cover threshold-based suspend application, predictive low glucose suspend, and more advanced single hormone and dual-hormone closed-loop systems. Approaches are evaluated in relation to nocturnal glucose control particularly during outpatient randomised controlled trials.

Expert opinion: Significant progress translating research from controlled clinical centre settings to free-living unsupervised home studies have been achieved over the past decade. Nocturnal glycaemic control can be improved whilst reducing the risk of hypoglycaemia with closed-loop systems. Following the US regulatory approval of the first hybrid closed-loop system in non-paediatric population, large multinational closed-loop clinical trials and pivotal studies including paediatric populations are underway or in preparation to facilitate the use of closed-loop systems in clinical practice.

"Let the Algorithm Do the Work": Reduction of Hypoglycemia Using Sensor-Augmented Pump Therapy with Predictive Insulin Suspension (SmartGuard) in Pediatric Type 1 Diabetes Patients

BACKGROUND

A sensor-augmented insulin pump (SAP) using the MiniMed^® 640G system with SmartGuard™ technology allows an automatic stop of insulin delivery based on prediction of low glucose levels. Since pediatric patients are particularly prone to hypoglycemia, this device may offer additional protection beyond conventional sensor-augmented therapy.

METHODS

This prospective, pediatric multicenter user evaluation assessed 6 weeks of SAP with SmartGuard (threshold setting for hypoglycemia: 70 mg/dL) compared to a preceding period of 2 weeks with SAP only. The primary outcome was the potential reduction in the frequency of hypoglycemic episodes and hypoglycemic intensity (area under the curve [AUC] and time <70 mg/dL).

RESULTS

The study included 24 patients with at least 3 months of insulin pump use (average age: 11.6 ± 5.1 years, 15 female, average type 1 diabetes duration: 7.5 ± 4.2 years, mean ± SD) who had on average 3.2 ± 1.0 predictive suspensions/patient/day. The mean sensor glucose minimum during suspension was 78 ± 6 mg/dL and the average suspension time was 155 ± 47 min/day. Use of SmartGuard in patients treated as per the protocol (n = 18) reduced the number of instances in which the glucose level was <70 mg/dL (1.02 ± 0.52 to 0.72 ± 0.36; P = 0.027), as well as AUC <70 mg/dL (0.76 ± 0.73 to 0.38 ± 0.24; P = 0.027) and the time/day the level fell below 70 mg/dL (73 ± 56 to 31 ± 22 min). The reduction of hypoglycemia was not associated with a significant change in mean glucose concentration (171 ± 26 to 180 ± 19 mg/dL, P = 0.111) and HbA1c (7.5% ± 0.5% to 7.6% ± 0.7%, (P = 0.329). Manual resumption of insulin delivery followed by carbohydrate intake resulted in significantly higher glucose levels 1 h after suspension compared to SmartGuard suspensions with automatic resume (190.8 ± 26.5 vs. 138.7 ± 10.3 mg/dL; P < 0.001).

CONCLUSIONS

SmartGuard technology significantly reduced the risk for hypoglycemia in pediatric type 1 diabetes patients without increasing HbA1c. Patients must be educated that when using combining predictive low-glucose insulin suspension technology, extra carbohydrate intake in response to an alarm combined with manual resumption is likely to cause rebound hyperglycemia. The best results were achieved when the user did not interfere with pump operation.