Blood glucose forecasting from temporal and static information in children with T1D.

Background: The overarching goal of blood glucose forecasting is to assist individuals with type 1 diabetes (T1D) in avoiding hyper- or hypoglycemic conditions. While deep learning approaches have shown promising results for blood glucose forecasting in adults with T1D, it is not known if these results generalize to children. Possible reasons are physical activity (PA), which is often unplanned in children, as well as age and development of a child, which both have an effect on the blood glucose level. Materials and Methods: In this study, we collected time series measurements of glucose levels, carbohydrate intake, insulin-dosing and physical activity from children with T1D for one week in an ethics approved prospective observational study, which included daily physical activities. We investigate the performance of state-of-the-art deep learning methods for adult data-(dilated) recurrent neural networks and a transformer-on our dataset for short-term (30 min) and long-term (2 h) prediction. We propose to integrate static patient characteristics, such as age, gender, BMI, and percentage of basal insulin, to account for the heterogeneity of our study group. Results: Integrating static patient characteristics (SPC) proves beneficial, especially for short-term prediction. LSTMs and GRUs with SPC perform best for a prediction horizon of 30 min (RMSE of 1.66 mmol/l), a vanilla RNN with SPC performs best across different prediction horizons, while the performance significantly decays for long-term prediction. For prediction during the night, the best method improves to an RMSE of 1.50 mmol/l. Overall, the results for our baselines and RNN models indicate that blood glucose forecasting for children conducting regular physical activity is more challenging than for previously studied adult data. Conclusion: We find that integrating static data improves the performance of deep-learning architectures for blood glucose forecasting of children with T1D and achieves promising results for short-term prediction. Despite these improvements, additional clinical studies are warranted to extend forecasting to longer-term prediction horizons.

Characterizing Associations of QTc Interval with Nocturnal Glycemic Control in Children with Type 1 Diabetes.

An association between QT prolongation (Bazett's corrected QT interval, QTcB) of 7 milliseconds and nocturnal hypoglycemia, compared with euglycemia, has been observed in children with type 1 diabetes (T1D). The objective of this pharmacometric analysis was to understand this association and other sources of QTc variability quantitatively. Data originate from a prospective observational study (25 cardiac healthy children with T1D, aged 8.1-17.6 years) with continuous subcutaneous glucose and electrocardiogram measurements for 5 consecutive nights. Mixed-effect modeling was used to compare QTcB with individual heart-rate correction (QTcI). Covariate models accounting for circadian variation, age, and sex were evaluated, followed by an investigation of glucose-QTc relationships (with univariable and combined adjusted analysis). Factors potentially modifying sensitivity to QTc lengthening were explored. Random inter-individual variability was reduced in the QTcI versus QTcB model (±12.6 vs 14.1 milliseconds), and was further reduced in the adjusted covariate model (±9.7 milliseconds), accounting for the significantly (P < .01) shortened QTc in adolescent boys (-14.6 milliseconds), circadian variation (amplitude, 19.2 milliseconds; shift, 2.9 hours), and linear glucose-QTc relationship (delay rate, 0.56-h ; slope, 0.76 milliseconds [95%CI 0.67- 0.85 milliseconds] per 1 mmol/L decrease in glucose). Differing sensitivity was suggested to depend upon hemoglobin A1c (HbA1c), T1D duration, and time spent in nocturnal hypoglycemia. In conclusion, a clinically mild association of QTc prolongation with nocturnal hypoglycemia was confirmed and quantified in this pharmacometric analysis, and the longest QTc interval was around 03:00 a.m. The characterized delayed association with glucose highlights the relevance of both the extent and the duration of hypoglycemia. Further clinical studies are warranted to investigate whether these factors contribute to increased risk of hypoglycemia-associated cardiac arrhythmia in children with T1D.

New model of glucose-insulin regulation characterizes effects of physical activity and facilitates personalized treatment evaluation in children and adults with type 1 diabetes.

Accurate treatment adjustment to physical activity (PA) remains a challenging problem in type 1 diabetes (T1D) management. Exercise-driven effects on glucose metabolism depend strongly on duration and intensity of the activity, and are highly variable between patients. In-silico evaluation can support the development of improved treatment strategies, and can facilitate personalized treatment optimization. This requires models of the glucose-insulin system that capture relevant exercise-related processes. We developed a model of glucose-insulin regulation that describes changes in glucose metabolism for aerobic moderate- to high-intensity PA of short and prolonged duration. In particular, we incorporated the insulin-independent increase in glucose uptake and production, including glycogen depletion, and the prolonged rise in insulin sensitivity. The model further includes meal absorption and insulin kinetics, allowing simulation of everyday scenarios. The model accurately predicts glucose dynamics for varying PA scenarios in a range of independent validation data sets, and full-day simulations with PA of different timing, duration and intensity agree with clinical observations. We personalized the model on data from a multi-day free-living study of children with T1D by adjusting a small number of model parameters to each child. To assess the use of the personalized models for individual treatment evaluation, we compared subject-specific treatment options for PA management in replay simulations of the recorded data with altered meal, insulin and PA inputs.

Fear of hypoglycemia and quality of life in young people with type 1 diabetes and their parents in the era of sensor glucose monitoring.

Introduction: It is crucial to understand psychosocial outcomes in children and adolescents with type 1 diabetes (T1D) and their families to provide optimal family-centered care. Hence, the aim of this study was to explore psychosocial outcomes in young people with T1D and their parents using currently available glucose monitoring devices in a real-life clinic setting. Methods: Children and adolescents aged 2-18 years with T1D for more than 6 months and their parents were recruited into a cross-sectional study to complete the Hypoglycemia Fear Survey (HFS) and the Pediatric Quality of Life Inventory (PedsQL) Generic Score Scales, Diabetes Module and Family Impact Module. Demographics and diabetes-specific parameters were obtained from medicals records. Results: Fifty-nine children and adolescents (mean age 15.1 ± 3.0 years) and 49 parents of children (mean age of children 12.5± 3.3 years) of which 44 were child-parent dyads completed the questionnaires. Parents had a higher mean (SD) FOH total and worry subscore than children, total score was 37.9 (14.6) vs. 32.2 (11.9), p = 0.047 and worry subscore was 17.8 (10.4) vs. 12.8 (9.0), p = 0.01. Furthermore, lower parental diabetes-specific QoL score was observed in parents, 78.8 (12.2) vs. 82.7 (10.3), p=0.02. No difference in FOH and QoL between real-time continuous glucose monitoring (rtCGM) and intermittently scanned glucose monitoring (isCGM) users and users of devices with and without alerts was observed. In isCGM users (n=36 completing the child questionnaires, n=33 completing parent questionnaires), higher parental FOH and lower parental diabetes-specific QoL correlated with higher scanning frequency, r = 0.399, p = 0.021, and r = -0.464, p = 0.007 respectively. No significant correlation was documented between scanning frequency and child questionnaire scores. Conclusions: Parents are more likely to perceive higher levels of psychosocial burden related to their child's diabetes than children and adolescents with T1D, especially parents of younger children. This highlights the need for family-based education and treatment resources to support parents in diabetes management in addition to rapidly advancing diabetes technology. In isCGM users, higher parental FOH and lower parent-perceived QoL correlated with a higher scanning frequency, indicating the potential impact of glucose monitoring modality on psychosocial outcomes or vice versa.

SwissPedData: Standardising hospital records for the benefit of paediatric research.

BACKGROUND: Improvement of paediatric healthcare is hampered by inefficient processes for generating new evidence. Clinical research often requires extra encounters with patients, is costly, takes place in an artificial situation with a biased selection of patients, and entails long delays until new evidence is implemented into health care. Electronic health records (EHR) contain detailed information on real patients and cover the entirety of patients. However, the use of EHR for research is limited because they are not standardised between hospitals. This leads to disproportionate amounts of work for extracting data of interest and frequently data are incomplete and of poor quality. AIMS: SwissPedData aims to lay the foundation for a paediatric learning health system in Switzerland by facilitating EHR-based research. In this project, we aimed to assess the way routine clinical data are currently recorded in large paediatric clinics in Switzerland and to develop a national EHR-based set of common data elements (CDEs) that covers all processes of routine paediatric care in hospitals. METHODS: A taskforce of paediatricians from large Swiss children's hospitals reviewed the current status of routine data documentation in paediatric clinical care and the extent of digitalisation. We then used a modified Delphi method to reach a broad consensus on a national EHR-based set of CDEs. RESULTS: All Swiss children's hospitals use EHR to document some or all aspects of care. One hundred and nineteen paediatricians, representing eight hospitals and all paediatric subspecialties, participated in an extended Delphi process to create SwissPedData. The group agreed on a national set of CDEs that comprises a main module with general paediatric data and sub-modules relevant to paediatric subspecialties. The data dictionary includes 336 CDEs: 76 in the main module on general paediatrics and between 11 and 59 CDEs per subspecialty module. Among these, 266 were classified as mandatory, 52 as recommended and 18 as optional. CONCLUSION: SwissPedData is a set of CDEs for information to be collected in EHR of Swiss children's hospitals. It covers all care processes including clinical and paraclinical assessment, diagnosis, treatment, disposition and care site. All participating hospitals agreed to implement SwissPedData in their clinical routine and clinic information systems. This will pave the way for a national paediatric learning health system in Switzerland that enables fast and efficient answers to urgent clinical questions by facilitating high-quality nationwide retrospective and prospective observational studies and recruitment of patients for nested prospective studies and clinical trials.

Autonomic cardiac regulation during spontaneous nocturnal hypoglycemia in children with type 1 diabetes.

BACKGROUND: Hypoglycemia is the most common complication in insulin treated diabetes. Though mostly mild, it can be fatal in rare cases: It is hypothesized that hypoglycemia related QTc prolongation contributes to cardiac arrhythmia. OBJECTIVE: To evaluate influence of nocturnal hypoglycemia on QTc and heart rate variability (HRV) in children with T1D. METHODS: Children and adolescents with T1D for at least 6 months participated in an observational study using continuous glucose monitoring (CGM) and Holter electrocardiogram for five consecutive nights. Mean QTc was calculated for episodes of nocturnal hypoglycemia (<3.7 mmol/L) and compared to periods of the same duration preceding hypoglycemia. HRV (RMSSD, low and high frequency power LF and HF) was analyzed for different 15 min intervals: before hypoglycemia, onset of hypoglycemia, before/after nadir, end of hypoglycemia and after hypoglycemia. RESULTS: Mean QTc during hypoglycemia was significantly longer compared to euglycemia (412 ± 15 vs. 405 ± 18 ms, p = 0.005). HRV changed significantly: RMSSD (from 88 ± 57 to 73 ± 43 ms) and HF (from 54 ± 17 to 47 ± 17nu) decreased from before hypoglycemia to after nadir, while heart rate (from 69 ± 9 to 72 ± 12 bpm) and LF (from 44 ± 17 to 52 ± 21 nu) increased (p = 0.04). CONCLUSION: A QTc lengthening effect of nocturnal hypoglycemia in children with T1D was documented. HRV changes occurred even before detection of nocturnal hypoglycemia by CGM, which may be useful for hypoglycemia prediction.

Clinical data for paediatric research: the Swiss approach : Proceedings of the National Symposium in Bern, Switzerland, Dec 5-6, 2019.

BACKGROUND AND PURPOSE: Continuous improvement of health and healthcare system is hampered by inefficient processes of generating new evidence, particularly in the case of rare diseases and paediatrics. Currently, most evidence is generated through specific research projects, which typically require extra encounters with patients, are costly and entail long delays between the recognition of specific needs in healthcare and the generation of necessary evidence to address those needs. The Swiss Personalised Health Network (SPHN) aims to improve the use of data obtained during routine healthcare encounters by harmonizing data across Switzerland and facilitating accessibility for research. The project "Harmonising the collection of health-related data and biospecimens in paediatric hospitals throughout Switzerland (SwissPedData)" was an infrastructure development project funded by the SPHN, which aimed to identify and describe available data on child health in Switzerland and to agree on a standardised core dataset for electronic health records across all paediatric teaching hospitals. Here, we describe the results of a two-day symposium that aimed to summarise what had been achieved in the SwissPedData project, to put it in an international context, and to discuss the next steps for a sustainable future. The target audience included clinicians and researchers who produce and use health-related data on children in Switzerland. KEY HIGHLIGHTS: The symposium consisted of state-of-the-art lectures from national and international keynote speakers, workshops and plenary discussions. This manuscript summarises the talks and discussions in four sections: (I) a description of the Swiss Personalized Health Network and the results of the SwissPedData project; (II) examples of similar initiatives from other countries; (III) an overview of existing health-related datasets and projects in Switzerland; and (IV) a summary of the lessons learned and future prospective from workshops and plenary discussions. IMPLICATIONS: Streamlined processes linking initial collection of information during routine healthcare encounters, standardised recording of this information in electronic health records and fast accessibility for research are essential to accelerate research in child health and make it affordable. Ongoing projects prove that this is feasible in Switzerland and elsewhere. International collaboration is vital to success. The next steps include the implementation of the SwissPedData core dataset in the clinical information systems of Swiss hospitals, the use of this data to address priority research questions, and the acquisition of sustainable funding to support a slim central infrastructure and local support in each hospital. This will lay the foundation for a national paediatric learning health system in Switzerland.

Simulation-Based Evaluation of Treatment Adjustment to Exercise in Type 1 Diabetes.

Regular exercise is beneficial and recommended for people with type 1 diabetes, but increased glucose demand and changes in insulin sensitivity require treatment adjustments to prevent exercise-induced hypoglycemia. Several different adjustment strategies based on insulin bolus reductions and additional carbohydrate intake have been proposed, but large inter- and intraindividual variability and studies using different exercise duration, intensity, and timing impede a direct comparison of their effects. In this study, we use a mathematical model of the glucoregulatory system and implement published guidelines and strategies in-silico to provide a direct comparison on a single 'typical' person on a standard day with three meals. We augment this day by a broad range of exercise scenarios combining different intensity and duration of the exercise session, and different timing with respect to adjacent meals. We compare the resulting blood glucose trajectories and use summary measures to evaluate the time-in-range and risk scores for hypo- and hyperglycemic events for each simulation scenario, and to determine factors that impede prevention of hypoglycemia events. Our simulations suggest that the considered strategies and guidelines successfully minimize the risk for acute hypoglycemia. At the same time, all adjustments substantially increase the risk of late-onset hypoglycemia compared to no adjustment in many cases. We also find that timing between exercise and meals and additional carbohydrate intake during exercise can lead to non-intuitive behavior due to superposition of meal- and exercise-related glucose dynamics. Increased insulin sensitivity appears as a major driver of non-acute hypoglycemic events. Overall, our results indicate that further treatment adjustment might be required both immediately following exercise and up to several hours later, but that the intricate interplay between different dynamics makes it difficult to provide generic recommendations. However, our simulation scenarios extend substantially beyond the original scope of each model component and proper model validation is warranted before applying our in-silico results in a clinical setting.

Copeptin Kinetics and Its Relationship to Osmolality During Rehydration for Diabetic Ketoacidosis in Children.

CONTEXT: Copeptin is a surrogate marker for arginine vasopressin (AVP) release in response to hyperosmolal stimuli such as diabetic ketoacidosis (DKA). OBJECTIVE: The objective of this work is to characterize kinetics of copeptin and osmolality, and their dynamic relationship during rehydration and insulin therapy in children with type 1 diabetes (T1D) and DKA. DESIGN AND SETTING: A prospective, observational, multicenter study was conducted. PATIENTS AND INTERVENTION: Children with T1D admitted for DKA underwent serial serum copeptin and osmolality measurements from start of rehydration at 14 time points during 72 hours. MAIN OUTCOME MEASURES: Measurements included temporal course of copeptin and osmolality (kinetics), relationship between both (dynamics), and association between-subject variability (BSV) (coefficient of variation, CV%). RESULTS: Twenty-eight children (20 newly diagnosed T1D) aged 1 to 16 years were included. Copeptin decreased from 95 pmol/L (95% CI, 55-136 pmol/L) (CV%, 158%) to 9.7 pmol/L (95% CI, 8.1-11.4 pmol/L) (CV%, 31%) with a 50% recovery time (t1/2) of 7.1 hours (range, 5.1-11.5 hours) (114%). Serum osmolality decreased from 321 mOsm/kg (range, 315-327 mOsm/kg) (4%) to 294 mOsm/kg (range, 292-296 mOsm/kg) (1%) with a t1/2 of 4.3 hours (range, 3.0-5.6 hours) (64%). Copeptin levels doubled with each osmolality increase by 15 mOsm/kg (range, 10-21 mOsm/kg) (59%), from 9.8 pmol/L (range, 7.3-12.3 pmol/L) (48%) to 280 mOsm/kg. Copeptin kinetics differed between newly diagnosed and known T1D patients (P = .001), and less between mild vs moderate-severe DKA (P = .04). CONCLUSIONS: First, this study characterized for the first time copeptin kinetics and dynamics in the high hyperosmolar range in children with DKA. Second, it revealed significant differences in copeptin kinetics between newly diagnosed and known T1D patients that may be explained by changes at the osmoreceptor and renal AVP receptor level due to longstanding osmotic diuresis and DKA.

Early changes in body composition after cessation of growth hormone therapy in childhood-onset growth hormone deficiency.

At final height, somatic maturity has not been reached yet. We investigated bone and body composition in patients, who completed pediatric growth hormone (GH) treatment at final height. After a mean period of 0.55 ± 0.17 yr off GH treatment 90 (66 m/24 f) childhood-onset growth hormone deficiency (GHD) patients were reinvestigated for GHD by insulin tolerance testing at a mean age of 17.52 ± 1.50 yr. Thirty-seven (25 m/12 f) patients remained GH deficient (persistent GHD). Bone and body composition were measured using peripheral quantitative computed tomography of the nondominant forearm. Bone mineral density (BMD) was within normal limits. Total cross-sectional bone area Z-score (0.64 ± 1.3) was significantly higher as a result of an enlarged medullary cavity Z-score (1.12 ± 1.2) leading to reduction of cortical thickness Z-score (-1.21 ± 1.0). Patients with persistent GHD had a significantly higher fat mass (13.3 ± 8.7 and 6.8 ± 4.6 cm(2), p<0.05), which was more pronounced in multiple pituitary hormone deficiency patients. Shortly after cessation of GH treatment in patients treated for childhood-onset GHD age adequate normal BMD and enlarged diaphysis was detectable. Patients with persistent GHD status had a significant higher fat mass.

Insulin sensitivity decreases in short children born small for gestational age treated with growth hormone.

OBJECTIVES: To evaluate insulin sensitivity in short children born small for gestational age (SGA) treated with growth hormone (GH), and to study the relationship between growth response and insulin levels. STUDY DESIGN: In 29 children (16 female, 13 male) who were short and SGA, an oral glucose tolerance test was performed before (mean age, 8.8 years; range, 4.5-14.3 years) and after 1 year of GH treatment (33 microg/kg/day). Insulin sensitivity was calculated with the homeostasis model assessment (HOMA) and the insulin sensitivity index (ISI) of Matsuda. RESULTS: The mean height increased from -3.1 to -2.4 SD. Insulin resistance (ISI<5) was seen in 17.2% of children before and in 48.3% (mainly pubertal) children after GH treatment. Insulin sensitivity decreased significantly: ISI fell from 12.2 to 6.1 (P= .02) and HOMA increased from 1.2 to 2.2 (P= .001). Glucose and HbA1c levels did not change significantly. ISI after 1 year did not correlate with height gain, but it did correlate with age (r= -0.469; P= .01) and body mass index (r= -0.52; P= .004). CONCLUSIONS: Insulin sensitivity is impaired in some children who are SGA already at baseline and decreases with GH treatment in most of them. Children close to puberty and children who are less underweight have the highest risk to become insulin resistant.

Reassessment of the optimal growth hormone cut-off level in insulin tolerance testing for growth hormone secretion in patients with childhood-onset growth hormone deficiency during transition to adulthood.

CONTEXT: Retesting of patients with childhood-onset growth hormone deficiency (CO-GHD) is recommended after completion of growth hormone (GH) treatment. AIM: To identify patients who are at risk of persistent GHD, and to evaluate the most reliable cut-off level for GH secretion using the insulin tolerance test (ITT) in the transition from childhood to adulthood. RESULTS: Ninety patients (22 female) with CO-GHD were retested using the ITT 1.1 +/- 1.1 years after discontinuation of therapy. Fifty-eight of 77 patients (75%) initially diagnosed with idiopathic GHD showed normalization of GH secretion. Thirteen patients were diagnosed with multiple pituitary hormone deficiency (MPHD) and diagnosis was reconfirmed in all of them, except for one patient. IGF-I levels of patients with persistent GHD were significantly lower (112 +/- 74 ng/ml [range 22-283] vs 245 +/- 107 ng/ml [range 31-505], p<0.01). IGF-I levels correlated positively with GH peak during ITT (r = 0.54, p <0.01), but the wide range of IGF-I levels shows that IGF-I alone cannot replace retesting in many cases. The best sensitivity and specificity scores were obtained when a GH cutoff level below 5.0 ng/ml for patients in the transition phase was used. ROC analysis demonstrates that ITT is a reliable test for evaluation of GH secretion in the transition phase (ROC-AUC 0.97). CONCLUSIONS: Patients with CO-GHD should be retested after discontinuation of therapy to identify those who may profit from further replacement therapy. Patients with organic or genetic GHD or MPHD are likely to have persistent GHD, whereas 75% of our patients with idiopathic GHD showed normalization of growth hormone secretion. IGF-I levels alone are not reliable in the diagnosis of adult GHD in many cases. The best sensitivity and specificity scores were found when a GH cut-off level below 5.0 ng/ml was used in patients with GHD in the transition from childhood to adulthood.