Background: Propionic acidemia is an inherited disorder caused by deficiency of propionyl-CoA carboxylase. Although it is one of the most frequent organic acidurias, information on the outcome of affected individuals is still limited. Study design/methods: Clinical and outcome data of 55 patients with propionic acidemia from 16 European metabolic centers were evaluated retrospectively. 35 patients were diagnosed by selective metabolic screening while 20 patients were identified by newborn screening. Endocrine parameters and bone age were evaluated. In addition, IQ testing was performed and the patients’ and their families’ quality of life was assessed. Results: The vast majority of patients (>85%) presented with metabolic decompensation in the neonatal period. Asymptomatic individuals were the exception. About three quarters of the study population was mentally retarded, median IQ was 55. Apart from neurologic symptoms, complications comprised hematologic abnormalities, cardiac diseases, feeding problems and impaired growth. Most patients considered their quality of life high. However, according to the parents’ point of view psychic problems were four times more common in propionic acidemia patients than in healthy controls. Conclusion: Our data show that the outcome of propionic acidemia is still unfavourable, in spite of improved clinical management. Many patients develop long-term complications affecting different organ systems. Impairment of neurocognitive development is of special concern. Nevertheless, self-assessment of quality of life of the patients and their parents yielded rather positive results.

Up to now, only limited data on long-term medical treatment in congenital hyperinsulinism (CHI) is available. Moreover, most of the drugs used in CHI are therefore not approved. We aimed to assemble more objective information on medical treatment in CHI with regard to type and duration, dosage as well as side effects. We searched MEDLINE (from 1947) and EMBASE (from 1988) using the OVID interface for relevant data to evaluate medical treatment in a large cohort of patients with CHI from different clinical centers. Randomized, controlled trials were not available. We evaluated case reports and case series. No language restrictions were made. A total number of 619 patients were medically treated and information regarding conservative treatment was available. Drugs used were diazoxide (in 84 % of patients), somatostatin analogues (16 %), calcium channel antagonists (4 %) and glucagon (1 %). Mean dose of diazoxide was 12.5 (±4.3) mg/kg ⋅ d (range 2–60 mg/kg ⋅ d), mean duration of diazoxide treatment until remission was 57 months. Side effects of diazoxide were usually not severe. The causal relation between diazoxide and severe side effects, e.g. heart failure (3.7 %) remains doubtful. Mean dose of octreotide was 14.9 (±7.5) μg/kg ⋅ d (range 2.3–50 μg/kg ⋅ d), of lanreotide 67.3 (±39.8) mg ⋅ month (range 10–120 mg ⋅ month). Mean duration of treatment with somatostatin analogues until remission was 49 months. Frequent side effects included tachyphylaxis and mild gastrointestinal symptoms. The risk of persistent growth deceleration was low (<5 %). Severe side effects are rare and a causal relation remains disputable. We conclude that long-term conservative treatment of CHI is feasible.

Background: To analyze key indicators of metabolic control in adults with type 1 diabetes (T1D) using real-time or intermittent scanning continuous glucose monitoring (rtCGM/iscCGM) during real-life care, based on the German/Austrian/Swiss Prospective Diabetes Follow-up (DPV) registry. Methods: Cross-sectional analysis including 233 adults with T1D using CGM. We assessed CGM metrics by gender, age group (18 to <30 years vs. ≥30 years), insulin delivery method (multiple daily injections vs. continuous subcutaneous insulin infusion [CSII]) and sensor type (iscCGM vs. rtCGM), working days versus weekends, and daytime versus night-time using multivariable linear regression models (adjusted for demographic variables) or Wilcoxon signed-rank test. Results: Overall, 79/21% of T1D patients used iscCGM/rtCGM. Those aged ≥30 years spent more time in range (TIR [70–180 mg/dL] 54% vs. 49%) and hypoglycemic range 180 mg/dL (38% vs. 46%) and had a lower glucose variability (coefficient of variation [CV] 36% vs. 37%) compared with adults aged 250 mg/dL, CV and daytime–night-time differences in those treated with CSII and rtCGM. Glucose profiles were slightly more favorable on working days. Conclusions: In our real-world data, rtCGM versus iscCGM was associated with a higher percentage of TIR and improved metabolic stability. Differences in ambulatory glucose profiles on working and weekend days may indicate lifestyle habits affecting glycemic stability. Real-life CGM results should be included in benchmarking reports in addition to hemoglobin A1c (HbA1c) and history of hypoglycemia.

Neonatal hypoglycemia affects up to 15% of all newborns. Despite the high prevalence there is no uniform definition of neonatal hypoglycemia, and existing guidelines differ significantly in terms of when and whom to screen for hypoglycemia, and where to set interventional thresholds and treatment goals. In this review, we discuss the difficulties to define hypoglycemia in neonates. Existing knowledge on different strategies to approach this problem will be reviewed with a focus on long-term neurodevelopmental outcome studies and results of interventional trials. Furthermore, we compare existing guidelines on the screening and management of neonatal hypoglycemia. We summarize that evidence-based knowledge about whom to screen, how to screen, and how to manage neonatal hypoglycemia is limited – particularly regarding operational thresholds (single values at which to intervene) and treatment goals (what blood glucose to aim for) to reliably prevent neurodevelopmental sequelae. These research gaps need to be addressed in future studies, systematically comparing different management strategies to progressively optimize the balance between prevention of neurodevelopmental sequelae and the burden of diagnostic or therapeutic procedures. Unfortunately, such studies are exceptionally challenging because they require large numbers of participants to be followed for years, as mild but relevant neurological consequences may not become apparent until mid-childhood or even later. Until there is clear, reproducible evidence on what blood glucose levels may be tolerated without negative impact, the operational threshold needs to include some safety margin to prevent potential long-term neurocognitive impairment from outweighing the short-term burden of hypoglycemia prevention during neonatal period.

Objective: Aim was to identify hypotheses why adverse neurodevelopment still occurs in children with transient or persistent hyperinsulinism despite improvements in long-term treatment options during the last decades. Material and Methods: A retrospective review of 87 children with transient (n=37) or persistent congenital hyperinsulinism (CHI) (n=50) was conducted at the University Children’s Hospital Duesseldorf, Germany. Possible risk factors for neurodevelopmental sequelae due to hypoglycemia were analyzed with a focus on the first days after onset of disease. Results: Median age at follow-up was 7 years (IQR 8). Adverse neurodevelopmental outcome was seen in 34.5% (n=30) of all CHI patients. Fifteen had mildly abnormal neurodevelopment and 15 had a severe hypoglycemic brain injury. In univariate analysis, mildly abnormal neurodevelopment was associated with the diagnosis of persistent CHI (odds ratio (OR) 8.3; p=0.004) and higher birth weight (mean difference 1049 g; p<0.001). Severe hypoglycemic brain injury was associated with the diagnosis of persistent CHI (OR 5.1; p=0.013), being born abroad (OR 18.3; p<0.001) or in a lower-level maternity hospital (OR 4.8; p=0.039), and of note history of hypoglycemic seizures (OR 13.0; p=<0.001), and a delay between first symptoms of hypoglycemia and first blood glucose measurement/initiation of treatment (OR 10.7; p<0.001). Children with severe hypoglycemic brain injury had lower recorded blood glucose (mean difference -8.34 mg/dl; p=0.022) and higher birth weight than children with normal development (mean difference 829 g; p=0.012). In multivariate binary logistic regression models, lowest blood glucose <20 mg/dl (OR 134.3; p=0.004), a delay between initial symptoms and first blood glucose measurement/initiation of treatment (OR 71.7; p=0.017) and hypoglycemic seizures (OR 12.9; p=0.008) were positively correlated with severe brain injury. Analysis showed that the odds for brain injury decreased by 15% (OR 0.85; p=0.035) if the blood glucose increased by one unit. Conclusion: While some risk factors for adverse outcome in CHI are not influenceable, others like lowest recorded blood glucose values <20 mg/dl, hypoglycemic seizures, and insufficiently—or even untreated hypoglycemia can be avoided. Future guidelines for management of neonatal hypoglycemia should address this by ensuring early identification and immediate treatment with appropriate escalation steps.

The integration of artificial intelligence (AI) and machine learning (ML) has shown potential for various applications in the medical field, particularly for diagnosing and managing chronic diseases among children and adolescents. This systematic review aims to comprehensively analyze and synthesize research on the use of AI for monitoring, guiding, and assisting pediatric patients with chronic diseases. Five major electronic databases were searched (Medline, Scopus, PsycINFO, ACM, Web of Science), along with manual searches of gray literature, personal archives, and reference lists of relevant papers. All original studies as well as conference abstracts and proceedings, focusing on AI applications for pediatric chronic disease care were included. Thirty-one studies met the inclusion criteria. We extracted AI method used, study design, population, intervention, and main results. Two researchers independently extracted data and resolved discrepancies through discussion. AI applications are diverse, encompassing, e.g., disease classification, outcome prediction, or decision support. AI generally performed well, though most models were tested on retrospective data. AI-based tools have shown promise in mental health analysis, e.g., by using speech sampling or social media data to predict therapy outcomes for various chronic conditions. Conclusions: While AI holds potential in pediatric chronic disease care, most reviewed studies are small-scale research projects. Prospective clinical implementations are needed to validate its effectiveness in real-world scenarios. Ethical considerations, cultural influences, and stakeholder attitudes should be integrated into future research. What is known: •  Artificial Intelligence (AI) will play a more dominant role in medicine and healthcare in the future and many applications are already being developed. What is new: •  Our review provides an overview on how AI-driven systems might be able to support children and adolescents with chronic illnesses. • While many applications are being researched, few have been tested on real-world, prospective, clinical data.