Introduction: We performed this study aiming to evaluate changes in epidemiology, clinical presentation and outcomes of children hospitalized for viral lower respiratory tract infections (LRTI).Methods: We performed a retrospective study of children younger than 18 years of age hospitalized for LRTIs with a positive respiratory viral testing from 2018 to 2022. We compared need of pediatric intensive care unit (PICU), invasive ventilation, and other respiratory support, viral etiologies, clinical presentations, imaging, and laboratory results in the precovid (2018–2019) and covid (2020–2022) period.Results: A total of 523 were included in the analysis. In the pandemic period, the detection of influenza was 95% less likely to occur (odds ratio [OR]: 0.05; 95% confidence interval [95% CI]: 0.02–0.12; p < .001), likewise the detection of adenovirus was 77% less likely to occur (OR: 0.23; 95% CI: 0.10–0.51; p < .001). In the pandemic period, the number of codetections increased from 15.52% in 2018 to 57.25% in 2022, resulting in a significantly increasing trend (p < .001). The odds of transfer to PICU was more than five times greater during the pandemic period (OR: 5.31; 95% CI: 1.78–15.86; p = .003).Conclusions: We found that the pattern of LRTI in children during COVID‐19 pandemic significantly changed in terms of etiologies and increased severity.

Background: Respiratory viral infections (RVIs) exhibit seasonal patterns influenced by biological, ecological, and climatic factors. Weather variables such as temperature, humidity, and wind impact the transmission of droplet-borne viruses, potentially affecting disease severity. However, the role of climate in predicting complications in pediatric RVIs remains unclear, particularly in the context of climate-change-driven extreme weather events. Methods: This retrospective cohort study analyzed 1610 hospitalization records of children (0–18 years) with lower respiratory tract infections in Rome, Italy, between 2018 and 2023. Viral pathogens were identified using nasopharyngeal molecular testing, and weather data from the week preceding hospitalization were collected. Several machine learning models were tested, including logistic regression and random forest, comparing the baseline (demographic and clinical) models with those including climate variables. Results: Logistic regression showed a slight improvement in predicting severe RVIs with the inclusion of weather variables, with accuracy increasing from 0.785 to 0.793. Average temperature, dew point, and humidity emerged as significant contributors. Other algorithms did not demonstrate similar improvements. Conclusions: Climate variables can enhance logistic regression models’ ability to predict RVI severity, but their inconsistent impact across algorithms highlights challenges in integrating environmental data into clinical predictions. Further research is needed to refine these models for use in reliable healthcare applications.

Cannabinoid hyperemesis syndrome (CHS) is characterized by a pattern of cyclic vomiting and abdominal pain despite an absence of an organic cause, occurring in regular cannabis users. This syndrome was first described in 2004. Initially considered rare, with the increased use and legalization of cannabis, a growing incidence of diagnoses has been observed. Data on the pediatric population are still scant despite the high rate of cannabis consumption in young people. In this narrative review, we aim to synthesize the growing knowledge about CHS and its epidemiology, pathophysiology, diagnosis, and management in the pediatric population. Findings in this review highlight the diagnostic challenges in pediatric patients, the limited efficacy of standard anti-emetic therapies, and the central role of cannabis cessation in treatment. This review underscores the need for increased awareness of CHS in pediatric practice to ensure timely diagnosis and avoid unnecessary investigations and interventions.