Background and Objectives: The COVID-19 pandemic has led to significant changes globally, which has also affected patients with type 1 diabetes mellitus (T1DM). This study aimed to determine the incidence of T1DM and the characteristics of diabetic ketoacidosis (DKA) during the pandemic comparing it to pre-pandemic period. Materials and Methods: Data from patients <19 years with newly diagnosed T1DM between 1st January 2017 and 31st December 2021 from four regional centers in Vojvodina were retrospectively collected and analyzed. Results: In 2021, the highest incidence of T1DM in the last five years was recorded, 17.3/100,000. During the pandemic period (2020–2021), there were 99 new-onset T1DM, of which 42.4% presented in DKA, which is significantly higher than in the pre-pandemic period (34.1%). During the pandemic, symptom duration of T1DM lasted significantly longer than before the COVID-19 period. At the age of 10–14 years, the highest incidence of T1DM and COVID-19, the highest frequency rate of DKA, and severe DKA were observed. Conclusions: The pandemic is associated with a high incidence rate of T1DM, longer duration of symptoms of T1DM, a high frequency of DKA, and a severe DKA at diagnosis. Patients aged 10–14 years are a risk group for the occurrence of T1DM with severe clinical presentation. Additional studies are needed with a longer study period and in a wider geographical area, with data on exposure to COVID-19 infection, the permanence of new-onset T1DM, and the psychosocial impact of the pandemic.

In order to limit the spread of the novel betacoronavirus (SARS-CoV-2), it is necessary to detect positive cases as soon as possible and isolate them. For this purpose, machine-learning algorithms, as a field of artificial intelligence, have been recognized as a promising tool. The aim of this study was to assess the utility of the most common machine-learning algorithms in the rapid triage of children with suspected COVID-19 using easily accessible and inexpensive laboratory parameters. A cross-sectional study was conducted on 566 children treated for respiratory diseases: 280 children with PCR-confirmed SARS-CoV-2 infection and 286 children with respiratory symptoms who were SARS-CoV-2 PCR-negative (control group). Six machine-learning algorithms, based on the blood laboratory data, were tested: random forest, support vector machine, linear discriminant analysis, artificial neural network, k-nearest neighbors, and decision tree. The training set was validated through stratified cross-validation, while the performance of each algorithm was confirmed by an independent test set. Random forest and support vector machine models demonstrated the highest accuracy of 85% and 82.1%, respectively. The models demonstrated better sensitivity than specificity and better negative predictive value than positive predictive value. The F1 score was higher for the random forest than for the support vector machine model, 85.2% and 82.3%, respectively. This study might have significant clinical applications, helping healthcare providers identify children with COVID-19 in the early stage, prior to PCR and/or antigen testing. Additionally, machine-learning algorithms could improve overall testing efficiency with no extra costs for the healthcare facility.

Background/Objectives: Respiratory morbidity in preterm infants has been widely studied, with evidence showing that individuals born prematurely experience more frequent respiratory symptoms, airflow obstruction, and radiological lung abnormalities throughout life. Methods: This study included 150 children aged 6 to 11 years, divided into two groups. The preterm group (n = 90) consisted of children born before 32 weeks of gestation, while the control group (n = 60) included term-born children. All participants underwent spirometry and completed a respiratory health questionnaire. Results: A significantly higher proportion of preterm children exhibited respiratory morbidity compared to term-born peers (χ² = 7.035; p = 0.030). However, no significant differences were found between preterm children with and without bronchopulmonary dysplasia (BPD) defined at day 28 (BPD28) (χ² = 0.002; p = 0.968) or BPD defined at 36 weeks postmenstrual age (BPD36) (χ² = 0.029; p = 0.864). Lung function parameters—forced vital capacity (FVC), forced expiratory volume in 1 s (FEV₁), FEV₁/IVC, maximal expiratory flow at 25%, 50% and 75% of FVC (MEF₂₅, MEF₅₀, and MEF₇₅) were significantly lower in the preterm group (p = 0.004 for FVC and p < 0.001 for all other parameters). No significant differences were observed between BPD and non-BPD subgroups in any lung function parameter. BPD36 was found to be a stronger predictor of respiratory morbidity (OR = 1.214) than BPD28 (OR = 1.093), although neither BPD28 nor BPD36 were statistically significant predictors. Conclusions: This study underscores the long-term respiratory consequences of prematurity and challenges the traditional view of BPD as the primary determinant of poor respiratory outcomes. Our findings suggest that prematurity itself, rather than BPD, may play a more central role.

Background/Objectives: To investigate if the severity and presentation of multisystem inflammatory syndrome in children (MIS-C) vary between different severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) variants. Methods: This retrospective study included 59 patients aged 0–18 years, diagnosed with COVID-19 and MIS-C, treated and monitored over a one-year period after discharge from hospital. The patients were grouped according to the predominant SARS-CoV-2 variant. The predominant variant of SARS-CoV-2 was assumed by the date of hospitalization. The following patient data were collected: demographic data (age, sex), information on comorbidities, body mass index, clinical data (fever and duration of febrile periods, symptoms of Kawasaki-like phenotypes, and presence of respiratory, cardiovascular, gastrointestinal, neurological and other symptoms), and laboratory and imaging findings. Results: In total, 24 (41%), 19 (32%), and 15 (25%) patients were diagnosed with MIS-C during the Alpha, Delta, and Omicron periods, respectively (63.8% were males; 36.2% were females). Comorbidities were present in 49% of patients. Respiratory symptoms were the most common during the Delta period (73%, p = 0.028). There was no statistically significant difference in the occurrence of other symptoms, laboratory findings, treatment, complications, and long-term outcomes between groups. Conclusions: No significant correlation was found between hospitalization date (used to estimate COVID-19 variant) and presentation/severity of MIS-C.

Introduction/Objective. Asthma is the most common chronic disease in children. Status asthmaticus is a severe exacerbation of asthma that can lead to hypoxemia and respiratory failure. Diagnosing asthma in preschool-aged children is often challenging as wheezing episodes at this age are most often caused by viral infections. The objective was to assess this issue in our population and improve clinical practice for diagnosing and treating status asthmaticus in preschool children. Methods. A retrospective study included 200 children aged up to five years who were hospitalized with status asthmaticus during January 2019 ? December 2023. Data on patients, diagnostic procedures, therapy, and clinical course were analyzed. Results. The largest proportion of patients was aged one year (31.5%), and males predominated (60.5%). The patients were most commonly hospitalized during the fall months, with an average length of hospital stay of 6.4 days. Allergy was confirmed in 50% of the children. Family history of atopy was present in 56% of the children. Previously diagnosed asthma was present in 13.5% of the children. At discharge, asthma preventive therapy was introduced in 91.5% of the children. All the children had a favorable outcome and were discharged from the hospital. Conclusion. Status asthmaticus most commonly occurs in children in the first year of life, often presenting as the initial manifestation of asthma. More than half of the children had a family history of atopy and confirmed allergies. Timely treatment according to current protocols is crucial for a good outcome.

Introduction: Foreign body aspiration is an emergency and potentially life threatening condition, most commonly observed in children under the age of three. In older children and adolescents, it occurs less frequently and is often overlooked due to nonspecific clinical manifestations and the absence of reliable anamnestic information. Individuals with autism spectrum disorder and other neurodevelopmental conditions are at particularly increased risk, as behavioral characteristics and impaired communication constitute significant factors both for aspiration and for delayed recognition and diagnosis. Case Report: We present the case of a fifteen year old adolescent with autism spectrum disorder who was hospitalized for persistent cough and fever lasting one month, unresponsive to prior antibiotic therapy. Difficulties in obtaining a reliable medical history and the absence of witnesses to the aspiration event contributed to a delayed diagnosis. Upon admission chest radiography demonstrated atelectasis and consolidation of the basal segments of the right lung. Flexible bronchoscopy revealed complete obstruction of the right intermediate bronchus by a foreign body (a figurine from a board game), which was successfully extracted using rigid bronchoscopy. Following removal of the foreign body, the patient experienced rapid clinical improvement and complete radiological resolution of pathological findings. Conclusion: Foreign body aspiration should be considered in older children and adolescents with autism spectrum disorder who present with prolonged, nonspecific respiratory symptoms. In the presence of a high index of clinical suspicion, bronchoscopy serves as an indispensable diagnostic and therapeutic modality and should be performed in order to prevent serious respiratory complications.