Comparator Data Characteristics and Testing Procedures for the Clinical Performance Evaluation of Continuous Glucose Monitoring Systems.

Comparing the performance of different continuous glucose monitoring (CGM) systems is challenging due to the lack of comprehensive guidelines for clinical study design. In particular, the absence of concise requirements for the distribution of comparator (reference) blood glucose (BG) concentrations and their rate of change (RoC) that are used to evaluate CGM performance, impairs comparability. For this article, several experts in the field of CGM performance testing have collaborated to propose characteristics of the distribution of comparator measurements that should be collected during CGM performance testing.

Combining Glucose Monitoring and Insulin Infusion in an Integrated Device: A Narrative Review of Challenges and Proposed Solutions.

The introduction of automated insulin delivery (AID) systems has enabled increasing numbers of individuals with type 1 diabetes (T1D) to improve their glycemic control largely. However, use of AID systems is limited due to their complexity and costs associated. The user must wear both a continuously monitoring glucose system and an insulin infusion pump.

Clinical Performance Evaluation of Continuous Glucose Monitoring Systems: A Scoping Review and Recommendations for Reporting.

The use of different approaches for design and results presentation of studies for the clinical performance evaluation of continuous glucose monitoring (CGM) systems has long been recognized as a major challenge in comparing their results. However, a comprehensive characterization of the variability in study designs is currently unavailable. This article presents a scoping review of clinical CGM performance evaluations published between 2002 and 2022.

Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA): A Novel Approach for the Statistical Accuracy Assessment of Continuous Glucose Monitoring Systems.

Background: The accuracy of continuous glucose monitoring (CGM) systems is crucial for the management of glucose levels in individuals with diabetes mellitus. However, the discussion of CGM accuracy is challenged by an abundance of parameters and assessment methods. The aim of this article is to introduce the Continuous Glucose Deviation Interval and Variability Analysis (CG-DIVA), a new approach for a comprehensive characterization of CGM point accuracy which is based on the U.

Evaluation of type 2 diabetes care management in nine primary care practices before and after implementation of the Criteria of Good Disease Management of Diabetes established by the Swiss Society of Endocrinology and Diabetology.

Aims Of The Study: Little is known about the quality of diabetes management of patients with type 2 diabetes mellitus (T2DM) in Swiss primary care. Based on the recommendations of the National Council Quality Assurance Programme, an interprofessional working group of the Swiss Society of Endocrinology and Diabetology (SSED) established population-based national criteria for good disease management of T2DM in primary health care (the diabetes score). The objective of this study was to assess whether the implementation of these criteria improve diabetes management in primary care.

Prevalence of diabetes mellitus in the greater Bern region (Bern-Mittelland) 2010-2014.

Objective: Concerning diabetes mellitus, one of the greatest burdens in public health in the 21st century, epidemiological data in Switzerland are scarce. To address this issue, this study intended to use a little-known but convenient way to quantify the prevalence of diabetes mellitus in the Swiss region of Bern-Mittelland.

Methods: In a population of approximately 330,000 people, the prevalence for the years 2010–2014 in adult persons was estimated using the capture-recapture method based on data collected routinely at the University Hospital in Bern (Inselspital) using outpatient lists and the registry of persons insured with Helsana Insurance Group.

Standardization process of continuous glucose monitoring: Traceability and performance.

People with diabetes are required to regularly check their glucose to make therapy decisions. So far, systems for self-monitoring of blood glucose were used, but nowadays minimally invasive continuous glucose monitoring (CGM) systems are increasingly more often employed, sometimes to partially replace self-monitoring of blood glucose. Most CGM systems on the market measure glucose concentrations continuously in the interstitial fluid of the subcutaneous fatty tissue.

[The Discovery of Insulin].

The Discovery of Insulin The initiative for the work that led to the discovery of insulin in Toronto in 1921 came from Frederik G. Banting. He worked under the direction of John J.

HbA-testing: Evaluation of two point-of-care analysers.

Background: HbA is a critical parameter for the medical management of patients with diabetes mellitus. Interventions that reduce HbA levels lead to a diminution of microvascular complications. For two decades, point of care testing (POCT) methods have been regularly used to measure HbA.

A Dual Mode Adaptive Basal-Bolus Advisor Based on Reinforcement Learning.

Self-monitoring of blood glucose (SMBG) and continuous glucose monitoring (CGM) are commonly used by type 1 diabetes (T1D) patients to measure glucose concentrations. The proposed adaptive basal-bolus algorithm (ABBA) supports inputs from either SMBG or CGM devices to provide personalised suggestions for the daily basal rate and prandial insulin doses on the basis of the patients' glucose level on the previous day. The ABBA is based on reinforcement learning, a type of artificial intelligence, and was validated in silico with an FDA-accepted population of 100 adults under different realistic scenarios lasting three simulated months.

National public health system responses to diabetes and other important noncommunicable diseases : Background, goals, and results of an international workshop at the Robert Koch Institute.

Diabetes mellitus and other noncommunicable diseases (NCDs) represent an emerging global public health challenge. In Germany, about 6.7 million adults are affected by diabetes according to national health surveys, including 1.

Model-Free Machine Learning in Biomedicine: Feasibility Study in Type 1 Diabetes.

Although reinforcement learning (RL) is suitable for highly uncertain systems, the applicability of this class of algorithms to medical treatment may be limited by the patient variability which dictates individualised tuning for their usually multiple algorithmic parameters. This study explores the feasibility of RL in the framework of artificial pancreas development for type 1 diabetes (T1D). In this approach, an Actor-Critic (AC) learning algorithm is designed and developed for the optimisation of insulin infusion for personalised glucose regulation.

Carbohydrate Estimation by a Mobile Phone-Based System Versus Self-Estimations of Individuals With Type 1 Diabetes Mellitus: A Comparative Study.

Background: Diabetes mellitus is spreading throughout the world and diabetic individuals have been shown to often assess their food intake inaccurately; therefore, it is a matter of urgency to develop automated diet assessment tools. The recent availability of mobile phones with enhanced capabilities, together with the advances in computer vision, have permitted the development of image analysis apps for the automated assessment of meals. GoCARB is a mobile phone-based system designed to support individuals with type 1 diabetes during daily carbohydrate estimation.

GoCARB in the Context of an Artificial Pancreas.

Background: In an artificial pancreas (AP), the meals are either manually announced or detected and their size estimated from the blood glucose level. Both methods have limitations, which result in suboptimal postprandial glucose control. The GoCARB system is designed to provide the carbohydrate content of meals and is presented within the AP framework.

Computer vision-based carbohydrate estimation for type 1 patients with diabetes using smartphones.

Background: Individuals with type 1 diabetes (T1D) have to count the carbohydrates (CHOs) of their meal to estimate the prandial insulin dose needed to compensate for the meal's effect on blood glucose levels. CHO counting is very challenging but also crucial, since an error of 20 grams can substantially impair postprandial control.

Method: The GoCARB system is a smartphone application designed to support T1D patients with CHO counting of nonpacked foods.

Multi-model data fusion to improve an early warning system for hypo-/hyperglycemic events.

Correct predictions of future blood glucose levels in individuals with Type 1 Diabetes (T1D) can be used to provide early warning of upcoming hypo-/hyperglycemic events and thus to improve the patient's safety. To increase prediction accuracy and efficiency, various approaches have been proposed which combine multiple predictors to produce superior results compared to single predictors. Three methods for model fusion are presented and comparatively assessed.

Estimating the prevalence of comorbid conditions and their effect on health care costs in patients with diabetes mellitus in Switzerland.

Background: Estimating the prevalence of comorbidities and their associated costs in patients with diabetes is fundamental to optimizing health care management. This study assesses the prevalence and health care costs of comorbid conditions among patients with diabetes compared with patients without diabetes. Distinguishing potentially diabetes- and nondiabetes-related comorbidities in patients with diabetes, we also determined the most frequent chronic conditions and estimated their effect on costs across different health care settings in Switzerland.

A food recognition system for diabetic patients based on an optimized bag-of-features model.

Computer vision-based food recognition could be used to estimate a meal's carbohydrate content for diabetic patients. This study proposes a methodology for automatic food recognition, based on the bag-of-features (BoF) model. An extensive technical investigation was conducted for the identification and optimization of the best performing components involved in the BoF architecture, as well as the estimation of the corresponding parameters.

The role of self-monitoring of blood glucose in patients treated with SGLT-2 inhibitors: a European expert recommendation.

The role for the novel treatment approach of sodium-glucose cotransporter-2 (SGLT-2) in type 2 diabetes is increasing. Structured self-monitoring of blood glucose (SMBG), based on a less intensive and a more intensive scheme, may contribute to an optimization of SGLT-2 inhibitor based treatment. The current expert recommendation suggests individualized approaches of SMBG, using simple and clinically applicable schemes.

The effect of a single 2 h bout of aerobic exercise on ectopic lipids in skeletal muscle, liver and the myocardium.

Aims/hypothesis: Ectopic lipids are fuel stores in non-adipose tissues (skeletal muscle [intramyocellular lipids; IMCL], liver [intrahepatocellular lipids; IHCL] and heart [intracardiomyocellular lipids; ICCL]). IMCL can be depleted by physical activity. Preliminary data suggest that aerobic exercise increases IHCL.

Role of diuretics, β blockers, and statins in increasing the risk of diabetes in patients with impaired glucose tolerance: reanalysis of data from the NAVIGATOR study.

Objective: To examine the degree to which use of β blockers, statins, and diuretics in patients with impaired glucose tolerance and other cardiovascular risk factors is associated with new onset diabetes.

Design: Reanalysis of data from the Nateglinide and Valsartan in Impaired Glucose Tolerance Outcomes Research (NAVIGATOR) trial.

Setting: NAVIGATOR trial.

Personalized tuning of a reinforcement learning control algorithm for glucose regulation.

Artificial pancreas is in the forefront of research towards the automatic insulin infusion for patients with type 1 diabetes. Due to the high inter- and intra-variability of the diabetic population, the need for personalized approaches has been raised. This study presents an adaptive, patient-specific control strategy for glucose regulation based on reinforcement learning and more specifically on the Actor-Critic (AC) learning approach.