Advances in Care for Insulin-Requiring Patients Without Closed Loop

The perception of patients, mothers and healthcare professionals regarding introduction of home visits for adolescents with type 1 diabetes

BACKGROUND AND OBJECTIVES

Despite advancements in diabetes management, a subgroup of patients does not achieve an acceptable level of metabolic control. To achieve treatment goals for high-risk patients, a higher frequency of contact with healthcare professionals is essential. However, socioeconomic challenges often lead to nonattendance at the outpatient clinic. Therefore, home visits were introduced as a supplement/an alternative to consultations at the paediatric diabetes clinic. The aim of this study was to uncover the perception of patients, mothers and healthcare professionals regarding home visits versus consultations at the outpatient clinic in order to being able to improve the services offered.

METHODS

Semistructured interviews with patients, mothers and healthcare professionals were conducted to explore the experiences and perspectives of the concept at two paediatric diabetes clinics in Denmark. Paired t tests were used to compare the glycated hemoglobin A1c (HbA1c) levels at the start of the intervention with HbA1c 1 year before and HbA1c at the end of the intervention.

RESULTS

Both patients, mothers and healthcare professionals found consultations in familiar surroundings to result in stronger relationships between healthcare professionals and families, creating greater opportunities to discuss personal issues aimed at improving the outcome of diabetes. Furthermore, home visits facilitated finding alternative methods of helping patients and the families integrating diabetes into everyday life.

CONCLUSION

Overall, home visits were found to contribute toward creating a more constructive relationship. However, it was still a challenge to keep the scheduled appointments, and during this short study, no improvement in metabolic control could be observed.

Pediatric Smart Insulin Pen Use: The Next Best Thing

In the pediatric population, insulin pump therapy, or CSII, is often considered the gold standard for intensive diabetes management. Insulin pump technology offers families and caregivers many beneficial features including a calculator for insulin dosing and the ability to review diabetes management data to provide data-driven diabetes management. However, for those who find CSII challenging or choose to use multiple daily injections (MDI) there is an option that offers similar features called the Smart Insulin Pen (SIP). Even though SIP technology provides a safe and data-driven diabetes self-management tool for the pediatric population using MDI, there is limited pediatric specific literature. This article will describe current options, data-driven diabetes management, benefits, challenges and clinical use of SIP technology in the pediatric population.

Home Visits for Children and Adolescents with Uncontrolled Type 1 Diabetes

Background: Home-based video visits were provided over one year as a supplement to in-person care for pediatric type 1 diabetes (T1D) patients with suboptimal glycemic control. We hypothesized that the intervention would be feasible and satisfactory for the target population and would significantly improve hemoglobin A1c (HbA1c) levels and completion of recommended quarterly diabetes clinic visits. Methods: This was a nonrandomized clinical trial. Fifty-seven patients aged 3-17 years with known T1D and HbA1c ≥8% (64 mmol/mol) were recruited to receive the intervention. The study population was 49% adolescent (13-17 years old) and 58% publicly insured patients. Video visits were scheduled every 4, 6, or 8 weeks depending on the HbA1c level. HbA1c levels as well as frequencies of clinic visits and of diabetes-related emergency department (ED) and hospital encounters were compared before and after the study. Results: Thirty participants completed 12 months of video visits. The study cohort demonstrated significant improvement in mean HbA1c in both intention-to-treat (N = 57) analysis (10.8% [95 mmol/mol] to 10.0% [86 mmol/mol], P = 0.01) and per-protocol (N = 30) analysis (10.8% [95 mmol/mol] to 9.6% [81 mmol/mol], P = 0.004). Completion of ≥4 annual diabetes clinic visits improved significantly from 21% at baseline to 83% during the study period for the entire cohort, P < 0.0001. The frequency of diabetes-related ED and hospital encounters did not change significantly. Conclusions: Home-based video visits are a feasible supplement to in-person care for children and adolescents with T1D and suboptimal glycemic control and can successfully improve HbA1c levels and adherence to recommended frequency of care in this high-risk clinical population.

Predicting Quality of Overnight Glycaemic Control in Type 1 Diabetes Using Binary Classifiers

In type 1 diabetes management, maintaining nocturnal blood glucose within target range can be challenging. Although semi-automatic systems to modulate insulin pump delivery, such as low-glucose insulin suspension and the artificial pancreas, are starting to become a reality, their elevated cost and performance below user expectations is hindering their adoption. Hence, a decision support system that helps people with type 1 diabetes, on multiple daily injections or insulin pump therapy, to avoid undesirable overnight blood glucose fluctuations (hyper- or hypoglycaemic) is an attractive alternative. In this paper, we introduce a novel data-driven approach to predict the quality of overnight glycaemic control in people with type 1 diabetes by analyzing commonly gathered data during the day-time period (continuous glucose monitoring data, meal intake and insulin boluses). The proposed approach is able to predict whether overnight blood glucose concentrations are going to remain within or outside the target range, and therefore allows the user to take the appropriate preventive action (snack or change in basal insulin). For this purpose, a number of popular established machine learning algorithms for binary classification were evaluated and compared on a publicly available clinical dataset (i.e., OhioT1DM). Although there is no clearly superior classification algorithm, this study indicates that, by using commonly gathered data in type 1 diabetes management, it is possible to predict the quality of overnight glycaemic control with reasonable accuracy (AUC-ROC = 0.7).

E-drug delivery: a futuristic approach

Drug delivery systems are undergoing technology changes to enhance patient comfort and compliance. Electronic drug delivery (E-drug delivery) systems are being developed to regulate drug dose delivery by easy monitoring of doses, especially in chronic and age-related diseases. E-drug delivery can monitor the correct dose of anesthesia, could be used in GI tracking by E-capsules, in epilepsy, insulin drug delivery, cardiac ailments and cancer therapy. Wearable E-drug delivery systems and Smartphone apps are the new additions. In this review, the authors attempt to highlight how technology is changing for improved patient comfort and treatment. Personalized drug delivery systems will be the future treatment process in healthcare.