Monthly video consultation for children and adolescents with type 1 diabetes mellitus during the COVID-19 pandemic.

AIMS: To explore the impact of primarily telemedical care for children and adolescents with type 1 diabetes by monthly video consultations on metabolic control and parents' treatment satisfaction and disease-specific burden during the COVID-19 pandemic. METHODS: In this 12-month multicenter observational follow-up VIDIKI 2.0 study, 100 participants (3-18 years) received monthly video consultations, which partially replaced quarterly outpatient clinic appointments during the pandemic. The children's metabolic parameters as well as the parents' treatment satisfaction and diabetes specific burden were assessed at study entry and 12 months later. RESULTS: During the study, 912 video consultations took place (mean 0.84 ±â€¯0.23 / patient/month). The children's HbA1c remained stable, while mean sensor glucose level and glucose management indicator decreased. Simultaneously, parents' treatment satisfaction significantly increased, and their diabetes-specific burden and distress decreased. CONCLUSIONS: Primarily telemedical care of children and adolescents with type 1 diabetes during the COVID-19 pandemic via monthly video consultations resulted in a significant improvement in parents' treatment satisfaction and their diabetes-specific burden and distress. It was associated with a slight improvement in mean sensor glucose and glucose management indicator, while HbA1c remained stable. Thus, video consultations offer great potential to enhance standard care for children and adolescents with diabetes.

Outcomes of monthly video consultations as an add-on to regular care for children with type 1 diabetes: A 6-month quasi-randomized clinical trial followed by an extension phase.

OBJECTIVE: To explore the outcomes of monthly video consultations for children with diabetes. METHODS: The Virtual Outpatient Diabetes Clinic for Children and Youth (VIDIKI) was a 6-month multicenter controlled clinical trial followed by an extension phase. The 240 participants (1-16 years), all using a CGM, were quasi-randomized by residence location to the intervention group (IG) or the waitlist-control group (WG). The IG started immediately after enrollment with monthly video consultations as an add-on to regular care, while the WG received regular care for 6 months before starting the intervention. The extension phase lasted between 12 months and 2 years, depending on the enrollment date. Linear regression was applied to model the primary outcome of HbA1c after 6 months and other metabolic and psychosocial outcomes. RESULTS: After covariate adjustments, the HbA1c at 6 months-corresponding to the controlled treatment phase-was 0.11% lower in the IG than that in the WG (95% CI -0.31 to 0.09, P = .277). For the total study sample, a significant HbA1c improvement was found after 12 months of video consultations, which increased further until month 15. The diabetes burden of the main caregivers was lower, and parental treatment satisfaction was significantly higher in the IG than that in the WG. CONCLUSIONS: The VIDIKI study found no significant HbA1c difference between IG and WG after 6 months in the controlled phase, but there was a decreased diabetes burden and increased treatment satisfaction for the parents. In the longitudinal perspective, a significant HbA1c improvement was found after 12 and 15 months.

Predicting the optimal basal insulin infusion pattern in children and adolescents on insulin pumps.

OBJECTIVE: We aimed at developing and cross-validating a mathematical prediction model for an optimal basal insulin infusion pattern for children with type 1 diabetes on continuous subcutaneous insulin infusion therapy (CSII). RESEARCH DESIGN AND METHODS: We used the German/Austrian DPV-Wiss database for quality control and scientific surveys in pediatric diabetology and retrieved all CSII patients <20 years of age (November 2009). A total of 1,248 individuals from our previous study were excluded (dataset 1), resulting in 6,063 CSII patients (dataset 2) (mean age 10.6 ± 4.3 years). Only the most recent basal insulin infusion rates (BRs) were considered. BR patterns were identified and corresponding patients sorted by unsupervised clustering. Logistic regression analysis was applied to calculate the probabilities for each BR pattern. Equations were based on both independent datasets separately, and probabilities for BR patterns were cross-validated using typical test patients. RESULTS: Of the 6,063 children, 5,903 clustered in one of four major circadian BR patterns, confirming our previous study. The oldest age-group (mean age 12.8 years) was represented by 2,490 patients (42.18%) with a biphasic dawn-dusk pattern (BC). A broad single insulin maximum at 9-10 p.m. (F) was unveiled by 853 patients (14.45%) (mean age 6.3 years). Logistic regression analysis revealed that age, to a lesser extent duration of diabetes, and partly sex predicted BR patterns. Cross-validation revealed almost identical probabilities for BR patterns BC and F in the two datasets but some variation in the remaining two BR patterns. CONCLUSIONS: Reconfirmation of four key BR patterns in two very large independent cohorts supports that these patterns are realistic approximations of the circadian distribution of insulin needs in children with type 1 diabetes. Prediction of an optimal pattern a priori can improve initiation and clinical follow-up of CSII in children and adolescents. In addition, these BR patterns represent valuable information for insulin-infusion algorithms in closed-loop CSII.