Impact of Carbohydrate Counting Error on Glycemic Control in Open-Loop Management of Type 1 Diabetes: Quantitative Assessment Through an In Silico Trial

Assessing Carbohydrate Counting Accuracy: Current Limitations and Future Directions

Diabetes mellitus is a prevalent chronic autoimmune disease with a high impact on global health, affecting millions of adults and resulting in significant morbidity and mortality. Achieving optimal blood glucose levels is crucial for diabetes management to prevent acute and long-term complications. Carbohydrate counting (CC) is widely used by patients with type 1 diabetes to adjust prandial insulin bolus doses based on estimated carbohydrate content, contributing to better glycemic control and improved quality of life. However, accurately estimating the carbohydrate content of meals remains challenging for patients, leading to errors in bolus insulin dosing. This review explores the current limitations and challenges in CC accuracy and emphasizes the importance of personalized educational programs to enhance patients' abilities in carbohydrate estimation. Existing tools for assessing patient learning outcomes in CC are discussed, highlighting the need for individualized approaches tailored to each patient's needs. A comprehensive review of the relevant literature was conducted to identify educational programs and assessment tools dedicated to training diabetes patients on carbohydrate counting. The research aims to provide insights into the benefits and limitations of existing tools and identifies future research directions to advance personalized CC training approaches. By adopting a personalized approach to CC education and assessment, healthcare professionals can empower patients to achieve better glycemic control and improve diabetes management. Moreover, this review identifies potential avenues for future research, paving the way for advancements in personalized CC training and assessment approaches and further enhancing diabetes management strategies.

An automatic deep reinforcement learning bolus calculator for automated insulin delivery systems

In hybrid automatic insulin delivery (HAID) systems, meal disturbance is compensated by feedforward control, which requires the announcement of the meal by the patient with type 1 diabetes (DM1) to achieve the desired glycemic control performance. The calculation of insulin bolus in the HAID system is based on the amount of carbohydrates (CHO) in the meal and patient-specific parameters, i.e. carbohydrate-to-insulin ratio (CR) and insulin sensitivity-related correction factor (CF). The estimation of CHO in a meal is prone to errors and is burdensome for patients. This study proposes a fully automatic insulin delivery (FAID) system that eliminates patient intervention by compensating for unannounced meals. This study exploits the deep reinforcement learning (DRL) algorithm to calculate insulin bolus for unannounced meals without utilizing the information on CHO content. The DRL bolus calculator is integrated with a closed-loop controller and a meal detector (both previously developed by our group) to implement the FAID system. An adult cohort of 68 virtual patients based on the modified UVa/Padova simulator was used for in-silico trials. The percentage of the overall duration spent in the target range of 70-180 mg/dL was 71.2 % and 76.2 % , < 70 mg/dL was 0.9 % and 0.1 % , and > 180 mg/dL was 26.7 % and 21.1 % , respectively, for the FAID system and HAID system utilizing a standard bolus calculator (SBC) including CHO misestimation. The proposed algorithm can be exploited to realize FAID systems in the future.

An ensemble machine learning approach for the detection of unannounced meals to enhance postprandial glucose control

BACKGROUND

Hybrid automated insulin delivery systems enhance postprandial glucose control in type 1 diabetes, however, meal announcements are burdensome. To overcome this, we propose a machine learning-based automated meal detection approach; METHODS:: A heterogeneous ensemble method combining an artificial neural network, random forest, and logistic regression was employed. Trained and tested on data from two in-silico cohorts comprising 20 and 47 patients. It accounted for various meal sizes (moderate to high) and glucose appearance rates (slow and rapid absorbing). To produce an optimal prediction model, three ensemble configurations were used: logical AND, majority voting, and logical OR. In addition to the in-silico data, the proposed meal detector was also trained and tested using the OhioT1DM dataset. Finally, the meal detector is combined with a bolus insulin compensation scheme; RESULTS:: The ensemble majority voting obtained the best meal detector results for both the in-silico and OhioT1DM cohorts with a sensitivity of 77%, 94%, 61%, precision of 96%, 89%, 72%, F1-score of 85%, 91%, 66%, and with false positives per day values of 0.05, 0.19, 0.17, respectively. Automatic meal detection with insulin compensation has been performed in open-loop insulin therapy using the AND ensemble, chosen for its lower false positive rate. Time-in-range has significantly increased 10.48% and 16.03%, time above range was reduced by 5.16% and 11.85%, with a minimal time below range increase of 0.35% and 2.69% for both in-silico cohorts, respectively, compared to the results without a meal detector; CONCLUSION:: To increase the overall accuracy and robustness of the predictions, this ensemble methodology aims to take advantage of each base model's strengths. All of the results point to the potential application of the proposed meal detector as a separate module for the detection of meals in automated insulin delivery systems to achieve improved glycemic control.

Assessment of clinical impact of SARS-CoV-2 in people with type 1 diabetes: A cohort study

OBJECTIVES

During the coronavirus 2019 pandemic, there had been more than 758 million COVID-19 cases as of February 13, 2023, and it is the main cause of death in many countries. Due to the variation in disease presentation, scientists determined that people living with type 2 diabetes mellitus were at higher risk of mortality. However, people living with type 1 diabetes have not been thoroughly studied, especially in extreme regions of developing countries. The objective of this study was to analyze the effects of SARS-CoV-2 pandemic restrictions on different variables in a cohort with type 1 diabetes.

METHODS

This cohort-type study included pediatric and adult patients with type 1 diabetes at Regional Hospital Dr. Juan Noé Crevani in Arica, Chile. Biosocial and anthropometric factors, clinical history, self-care activities, and biochemical parameters were assessed and compared using analysis of variance and paired t tests between March 2020 and March 2021.

RESULTS

A total of 150 patients were assessed during the SARS-CoV-2 pandemic in Arica, Chile. One year after the pandemic struck, the main causes for metabolic deterioration were a reduction of carbohydrate counting by an average of 8.67% (P = 0.000), a reduction of adherence to treatment by an average of 25% (P = 0.000), and a shift to telemedicine as a main health care service (P = 0.023); these factors raised hemoglobin A1c (HbA1c) levels by 1.81%, 1.78% and 0.075%, respectively. The participants' average body mass index (BMI) increased by 1.26 kg/m2 and HbA1c levels increased by 0.16% during the first year of the pandemic. Also, hospitalizations increased about 2% (P = 0.984), and there was a significant increase in carbohydrate and snack intake (P = 0.330 and P = 0.811, respectively). Children's linear growth decreased by a standard deviation of 0.035 (P = 0.648), and their physical activity decreased by 12.67% (P = 0.383).

CONCLUSIONS

This study found that adherence to diabetes care was reduced during the pandemic owing to a variety of behavioral reasons and environmental changes (e.g., quarantines and food security). This affected this population's HbA1c levels, BMI, linear growth, and number of hospitalizations as main consequences. Telemedicine remains an important tool, but it must be reconsidered among all different age groups.

A Markov Model of Gap Occurrence in Continuous Glucose Monitoring Data for Realistic in Silico Clinical Trials

BACKGROUND AND OBJECTIVE

Continuous glucose monitoring (CGM) sensors measure interstitial glucose concentration every 1-5 min for days or weeks. New CGM-based diabetes therapies are often tested in in silico clinical trials (ISCTs) using diabetes simulators. Accurate models of CGM sensor inaccuracies and failures could help improve the realism of ISCTs. However, the modeling of CGM failures has not yet been fully addressed in the literature. This work aims to develop a mathematical model of CGM gaps, i.e., occasional portions of missing data generated by temporary sensor errors (e.g., excessive noise or artifacts).

METHODS

Two datasets containing CGM traces collected in 167 adults and 205 children, respectively, using the Dexcom G6 sensor (Dexcom Inc., San Diego, CA) were used. Four Markov models, of increasing complexity, were designed to describe three main characteristics: number of gaps for each sensor, gap distribution in the monitoring days, and gap duration. Each model was identified on a portion of each dataset (training set). The remaining portion of each dataset (real test set) was used to evaluate model performance through a Monte Carlo simulation approach. Each model was used to generate 100 simulated test sets with the same size as the real test set. The distributions of gap characteristics on the simulated test sets were compared with those observed on the real test set, using the two-sample Kolmogorov-Smirnov test and the Jensen-Shannon divergence.

RESULTS

A six-state Markov model, having two states to describe normal sensor operation and four states to describe gap occurrence, achieved the best results. For this model, the Kolmogorov-Smirnov test found no significant differences between the distribution of simulated and real gap characteristics. Moreover, this model obtained significantly lower Jensen-Shannon divergence values than the other models.

CONCLUSIONS

A Markov model describing CGM gaps was developed and validated on two real datasets. The model describes well the number of gaps for each sensor, the gap distribution over monitoring days, and the gap durations. Such a model can be integrated into existing diabetes simulators to realistically simulate CGM gaps in ISCTs and thus enable the development of more effective and robust diabetes management strategies.

Development and Theoretical Underpinnings of the PRIORITY Intervention: A Parenting Intervention to Prevent Disordered Eating in Children and Young People With Type 1 Diabetes

Children and young people (CYP) with type 1 diabetes (T1D) are twice as likely to develop disordered eating (T1DE) and clinical eating disorders than those without. This has significant implications for physical and mental health, with some eating disorders associated with repeated diabetic ketoacidosis and higher HbA1c levels, both of which are life threatening. There is currently limited psychological support for CYP and families with T1D but increasingly, policy and practice are suggesting disordered eating in T1D may be effectively prevented through psychological intervention. We describe the development and theoretical underpinnings of a preventative psychological intervention for parents of CYP aged 11-14, with T1D. The intervention was informed by psychological theory, notably the Information Motivation Behaviour Skills model and Behaviour Change Technique Taxonomy. The intervention was co-developed with an expert advisory group of clinicians, and families with T1D. The manualised intervention includes two online group workshops, and supplementary online materials. The intervention continues to evolve, and feasibility findings will inform how best to align the intervention with routine care in NHS diabetes teams. Early detection and intervention are crucial in preventing T1DE, and it is hoped that the current intervention can contribute to improving the psychological and physical wellbeing of young people and families managing T1D.