Continuous Glucose Monitoring (CGM) has become an increasingly investigated tool, especially with regards to monitoring of diabetic and critical care patients. The continuous glucose data allows the calculation of several glucose variability parameters, however, without specific application the interpretation of the results is time-consuming, utilizing extreme efforts. Our aim was to create an open access software [Glycemic Variability Analyzer Program (GVAP)], readily available to calculate the most common parameters of the glucose variability and to test its usability. The GVAP was developed in MATLAB® 2010b environment. The calculated parameters were the following: average area above/below the target range (Avg. AUC-H/L); Percentage Spent Above/Below the Target Range (PATR/PBTR); Continuous Overall Net Glycemic Action (CONGA); Mean of Daily Differences (MODD); Mean Amplitude of Glycemic Excursions (MAGE). For verification purposes we selected 14 CGM curves of pediatric critical care patients. Medtronic® Guardian® Real-Time with Enlite® sensor was used. The reference values were obtained from Medtronic®’s own software for Avg. AUC-H/L and PATR/PBTR, from GlyCulator for MODD and CONGA, and using manual calculation for MAGE. The Pearson and Spearman correlation coefficients were above 0.99 for all parameters. The initial execution took 30 minutes, for further analysis with the Windows® Standalone Application approximately 1 minute was needed. The GVAP is a reliable open access program for analyzing different glycemic variability parameters, hence it could be a useful tool for the study of glycemic control among critically ill patients.

Background: Despite the growing uptake of smart technologies in pediatric type 1 diabetes mellitus (T1DM) care, little is known about caregiving parents’ skills to deal with electronic health information sources. Objective: We aimed to assess the electronic health literacy of parents caring for children with T1DM and investigate its associations with disease management and children’s outcomes. Methods: A cross-sectional survey was performed involving 150 parent-child (8-14 years old with T1DM) dyads in a university pediatric diabetology center. Parents’ electronic health literacy (eHealth Literacy Scale [eHEALS]), general health literacy (Chew questionnaire and Newest Vital Sign [NVS]), and attitudes toward T1DM care (Parental Self-Efficacy Scale for Diabetes Management [PSESDM] and Hypoglycemia Fear Survey [HFS]) were investigated. Children’s treatment, HbA1c level, and quality of life (Pediatric Quality of Life Inventory Diabetes Module [PedsQL Diab] and EQ-5D-Y-3L) were assessed. Multiple linear regression analysis was performed to investigate the determining factors of 6-month average HbA1c. Results: Of the 150 children, 38 (25.3%) used a pen, 55 (36.7%) used a pen plus a sensor, 6 (4.0%) used an insulin pump, and 51 (34.0%) used an insulin pump plus a sensor. Parents’ average eHEALS score (mean 31.2, SD 4.9) differed significantly by educational level (P=.04) and the children’s treatment (P=.005), being the highest in the pump + sensor subgroup. The eHEALS score showed significant Pearson correlations with the Chew score (r=−0.45; P<.001), NVS score (r=0.25; P=.002), and PSESDM score (r=0.35; P<.001) but not with the children’s HbA1c (r=−0.143; P=.08), PedsQL Diab (r=−0.0002; P>.99), and EQ-5D-Y-3L outcomes (r=−0.13; P=.12). Regression analysis revealed significant associations of the child’s HbA1c level with sex (β=0.58; P=.008), treatment modality (pen + sensor: β=−0.66; P=.03; pump + sensor: β=−0.93; P=.007), and parents’ self-efficacy (PSESDM; β=−0.08; P=.001). Conclusions: Significantly higher parental electronic health literacy was found in T1DM children using a glucose sensor. The electronic health literacy level was associated with parents’ diabetes management attitude but not with the child’s glycemic control. Studies further investigating the role of parental electronic health literacy in T1DM children managed at different levels of care and the local context are encouraged.

Continuous glucose monitoring (CGM) originally was developed for diabetic patients and it may be a useful tool for monitoring glucose changes in pediatric intensive care unit (PICU). Its use is, however, limited by the lack of sufficient data on its reliability at insufficient peripheral perfusion. We aimed to correlate the accuracy of CGM with laboratory markers relevant to disturbed tissue perfusion. In 38 pediatric patients (age range, 0–18 years) requiring intensive care we tested the effect of pH, lactate, hematocrit and serum potassium on the difference between CGM and meter glucose measurements. Guardian® (Medtronic®) CGM results were compared to GEM 3000 (Instrumentation laboratory®) and point-of-care measurements. The clinical accuracy of CGM was evaluated by Clarke Error Grid -, Bland-Altman analysis and Pearson’s correlation. We used Friedman test for statistical analysis (statistical significance was established as a p < 0.05). CGM values exhibited a considerable variability without any correlation with the examined laboratory parameters. Clarke, Bland-Altman analysis and Pearson’s correlation coefficient demonstrated a good clinical accuracy of CGM (zone A and B = 96%; the mean difference between reference and CGM glucose was 1,3 mg/dL, 48 from the 780 calibration pairs overrunning the 2 standard deviation; Pearson’s correlation coefficient: 0.83). The accuracy of CGM measurements is independent of laboratory parameters relevant to tissue hypoperfusion. CGM may prove a reliable tool for continuous monitoring of glucose changes in PICUs, not much influenced by tissue perfusion, but still not appropriate for being the base for clinical decisions.

Background: The purpose of this study was to investigate cognitive emotion regulation in adolescents with chronic illness and their parents. Methods: Eighty-five young people (mean = 15.86 years, standard deviation = ± 1.42, girls 65.88%) with chronic illnesses (inflammatory bowel disease n = 40 or type 1 diabetes n = 45), and their parents (mean = 46.06 years, 87.06% mother) completed the Cognitive Emotion Regulation Questionnaire (CERQ) for themselves and the Inventory of Quality of Life in Children and Adolescents (ILC) questionnaire adolescent and parent version. We conducted two hierarchical linear regression analyses with “enter” method. The CERQ scales and the diagnosis of chronic disease were chosen as independent variables, and the total ILC score in the first analysis and the ILC proxy score in the second analysis were chosen as dependent variables. Results: Among adolescents, cognitive emotion regulation strategies such as self-blame, positive reappraisal, and catastrophizing have been proven to be predictors of their own quality of life; however, parental self-blame was also found to be a predictor of adolescents’ quality of life. Parental rumination and positive refocusing have been shown to be predictors of how parents rate their child’s quality of life. Conclusions: The present study sheds light on cognitive emotion regulation strategies in adolescents with chronic illness and their parents that have a significant impact on the development of young people’s quality of life.

Introduction: Studies assessing longitudinal changes in the prevalence of autoimmune thyroiditis (AIT) among the pediatric population are limited. During the COVID-19 era, several papers proposed a rise in AIT cases. Our study aimed to analyze the prevalence of thyroid autoimmunity (TA) over a 10-year period spanning pre-pandemic and pandemic years in a population who are regularly screened for thyroid disturbances.Materials and methods: This single-center retrospective cohort study analyzed data from 1,361 children and young adults with type 1 diabetes (T1D) treated between 2013 and 2022 in Hungary’s largest pediatric endocrinology center. Results of anti-thyroid autoantibodies (anti-thyroid peroxidase/ATPO/and antithyroglobulin/ATG/), thyroid function tests (TFTs) and thyroid ultrasound examinations were obtained. Annual prevalence rates of TA and ultrasound-proven thyroiditis were calculated. Mean (± SD) follow-up period was 4.7 (± 2.8) years.Results: The overall prevalence of TA among our T1D children was 22.8% ([20.3;25.5], 310 cases) with significantly more girls affected (p<0.001). From 2013 to 2022, TA prevalence rose from 15.9% to 20.6% (p=0.041). The increase was detected during the pre-pandemic years but not in the COVID-19 era. Ultrasound-confirmed thyroiditis was present in 80.0% of examined TA cases. Ultrasound positivity rate was stable during the study period. Among our children with TA, 28.5% exhibited clinically relevant thyroid-stimulating hormone (TSH) abnormalities (most commonly subclinical hypothyroidism) and/or were prescribed thyroid medication. Children with AIT had a significantly elevated risk of thyroid dysfunction compared to those with only thyroid autoantibody positivity (p<0.001).Conclusion: Our results show a rise in the prevalence of thyroid autoimmunity among T1D children over the past decade, but our data do not support the assumed role of SARS-CoV-2 in the development of the disease.

Maturity-onset diabetes of the young (MODY) has about a dozen known causal genes to date, the most common ones being HNF1A, HNF4A, HNF1B and GCK. The phenotype of this clinically and genetically heterogeneous form of diabetes depends on the gene in which the patient has the mutation. We have tested 450 Hungarian index patients with suspected MODY diagnosis with Sanger sequencing and next-generation sequencing and found a roughly 30% positivity rate. More than 70% of disease-causing mutations were found in the GCK gene, about 20% in the HNF1A gene and less than 10% in other MODY-causing genes. We found 8 pathogenic and 9 likely pathogenic mutations in the HNF1A gene in a total of 48 patients and family members. In the case of HNF1A-MODY, the recommended first-line treatment is low dose sulfonylurea but according to our data, the majority of our patients had been on unnecessary insulin therapy at the time of requesting their genetic testing. Our data highlights the importance of genetic testing in the diagnosis of MODY and the establishment of the MODY subtype in order to choose the most appropriate treatment.