Purpose: Respiratory syncytial virus-positive bronchiolitis continues to be the main diagnosis prompting transportation in children younger than one year of age. It represents approximately 15–20% of all services performed by a specialized pediatric transport team. In October 2023, an immunization program with nirsevimab, a monoclonal antibody against RSV, was started in Spain. The purpose of the present study is to describe how nirsevimab affects the rates of bronchiolitis managed by a pediatric team specialized in critical patient transport. Secondary objectives included describing and comparing the clinical aspects of the two cohorts—pre-nirsevimab (pre-n) and post-nirsevimab (post-n)—to quantify how immunization has modified the clinical phenotype of bronchiolitis. Methods: This is a descriptive and observational study. Patients with bronchiolitis transported by a specialized pediatric transport team between September 2021 and August 2025 were included. Demographic, clinical, and microbiological data were collected. The pre-n and post-n periods were compared. Results: From a total of 2347 interfacility transports conducted by the unit between 2021 and 2025, 463 (19.7%) involved bronchiolitis patients, all of whom were recruited: 307 in the pre-n period and 156 in the post-n. The median age was 2.5 months (IQR 1.3–5.7), and 55% were male. There was a significant decrease in bronchiolitis cases that required specialized transport between the two periods: 28.2% (307/1089) pre-n vs. 12.4% (156/1258) post-n (p < 0.001). RSV detection also declined (74.3% vs. 47.4%, p < 0.001), while other viruses increased significantly in the post-n period, including rhinovirus, metapneumovirus and bocavirus. Age at admission showed statistically significant differences across the two periods (2.2 vs. 3.4 months, p < 0.001). There were no differences in severity between the two periods in terms of respiratory and inotropic support and length of stay. No mortality was reported. Conclusions: Universal nirsevimab immunization was associated with a marked reduction in pediatric transports for bronchiolitis, particularly RSV-related cases, without modifying disease severity among those requiring transfer.

Spontaneous pediatric intracerebral hemorrhage (sICH) is a rare but devastating neurological emergency associated with high mortality and long-term disability. Existing prognostic scores rely on a reduced number of variables and have shown limited validation, highlighting the need for comprehensive tools. This study describes the protocol for the development and internal validation of the CLIMPOUT (Clinical, Laboratory, and Imaging Predictive Outcome) score, a multimodal model designed to improve early prognostic assessment in pediatric sICH. This single-center retrospective cohort study will include consecutive patients (1 month–18 years) admitted to a tertiary pediatric intensive care unit (PICU) with spontaneous sICH between 2018 and 2025. Predictor variables will be restricted to baseline information available at PICU admission, including demographics, clinical status, laboratory markers, and quantitative neuroimaging features. Hematoma and intraventricular hemorrhage volumes will be measured using standardized three-dimensional segmentation. The primary outcome will be poor neurological status at 12 months, defined by the Pediatric Stroke Outcome Measure (PSOM). We will develop a multivariable logistic regression model following contemporary recommendations, including prespecified predictor selection, multiple imputation for missing data, and internal validation by 1000 bootstrap resamples. The CLIMPOUT score is expected to identify independent predictors of poor neurological outcome and provide a standardized, multimodal risk stratification model with improved prognostic performance compared with currently available pediatric scores. This protocol establishes a rigorous framework for developing a clinically applicable prediction model that may support early individualized decision-making, optimize neurocritical care management, and provide the foundation for future multicenter validation.