Daniele Donà

Respiratory tract infections (RTIs) are the most common infectious syndromes, primarily caused by viruses. The primary objective was to compare the illness courses between historical RTIs and recent SARS-CoV-2 infections. The study cohort consisted of RTI cases evaluated at the Pediatric Emergency Departments of Padua and Bologna, discharged or admitted with microbiologically confirmed viral RTI between 1 November 2018 and 30 April 2019 (historical period) and 1 March 2020 and 30 April 2021 (recent period). We evaluated the risk of oxygen or respiratory support, hospitalization, antibiotic therapy, and complications among different viral infections. The odds ratio (OR) and the 95% confidence intervals (CIs) were estimated through mixed-effect logistic regression models, including a random intercept on the individual and hospital. We identified 767 RTIs: 359 in the historical period compared with 408 SARS-CoV-2 infections. Infections of SARS-CoV-2 had a lower risk of being admitted (OR 0.04, 95% CI 0.03–0.07), receiving respiratory support (OR 0.19, 95% CI 0.06–0.58), needing antibiotic therapy (OR 0.35, 95% CI 0.22–0.56) and developing complications (OR 0.27, 95% CI 0.14–0.51) compared to all other viral RTIs. COVID-19 in children is clinically similar to other viral RTIs but is associated with a less severe infection course. Thus, most prevention strategies implemented for SARS-CoV-2 should still be considered during RSV and Influenza epidemics.

Saliva is a promising matrix with several purposes. Our aim is to verify if salivary anti-SARS-CoV-2 antibody determination is suitable for monitoring immune responses. One hundred eighty-seven subjects were enrolled at University-Hospital Padova: 105 females (56.1%) and 82 males (43.9%), 95 (50.8%) children and 92 (49.2%) adults. Subjects self-collected saliva using Salivette; nineteen subjects collected three different samples within the day. A serum sample was obtained for all individuals. The N/S anti-SARS-CoV-2 salivary IgG (sal-IgG) and serum anti-SARS-CoV-2 S-RBD IgG (ser-IgG) were used for determining anti-SARS-CoV-2 antibodies. The mean (min–max) age was 9.0 (1–18) for children and 42.5 (20–61) for adults. Of 187 samples, 63 were negative for sal-IgG (33.7%), while 7 were negative for ser-IgG (3.7%). Spearman’s correlation was 0.56 (p < 0.001). Sal-IgG and ser-IgG levels were correlated with age but not with gender, comorbidities, prolonged therapy, previous SARS-CoV-2 infection, or time from last COVID-19 infection/vaccination. The repeatability ranged from 23.8% (7.4 kAU/L) to 4.0% (3.77 kAU/L). The linearity of the assay was missed in 4/6 samples. No significant intrasubject differences were observed in sal-IgG across samples collected at different time points. Sal-IgG has good agreement with ser-IgG. Noninvasive saliva collection represents an alternative method for antibody measurement, especially in children.

Free-text information represents a valuable resource for epidemiological surveillance. Its unstructured nature, however, presents significant challenges in the extraction of meaningful information. This study presents a deep learning model for classifying otitis using pediatric medical records. We analyzed the Pedianet database, which includes data from January 2004 to August 2017. The model categorizes narratives from clinical record diagnoses into six types: no otitis, non-media otitis, non-acute otitis media (OM), acute OM (AOM), AOM with perforation, and recurrent AOM. Utilizing deep learning architectures, including an ensemble model, this study addressed the challenges associated with the manual classification of extensive narrative data. The performance of the model was evaluated according to a gold standard classification made by three expert clinicians. The ensemble model achieved values of 97.03, 93.97, 96.59, and 95.48 for balanced precision, balanced recall, accuracy, and balanced F1 measure, respectively. These results underscore the efficacy of using automated systems for medical diagnoses, especially in pediatric care. Our findings demonstrate the potential of deep learning in interpreting complex medical records, enhancing epidemiological surveillance and research. This approach offers significant improvements in handling large-scale medical data, ensuring accuracy and minimizing human error. The methodology is adaptable to other medical contexts, promising a new horizon in healthcare analytics.