Emergencias : revista de la Sociedad Espanola de Medicina de Emergencias最新文献

Objective: To evaluate the impact of respiratory syncytial virus (RSV) prophylaxis with nirsevimab during its 2nd year of administration and analyze the differences between the different strategies implemented across Spanish autonomous communities (ACs) during the current epidemic season.

Methods: We conducted a retrospective analytical study with 15 pediatric emergency departments across 9 ACs. We included all infants younger than 6 months who were treated in November, December, and January during 5 epidemic seasons (2018-2019, 2019-2020, 2022-2023, 2023-2024, and 2024-2025).

Results: Compared with the average of epidemic seasons prior to the introduction of nirsevimab, in 2024-2025 there was a 65.5% decrease in episodes of acute bronchiolitis (95% CI, 64.3-63.7; P .001). Hospital admissions for bronchiolitis decreased by 74.7% (95% CI, 72.7-76.6; P .001), and PICU admissions fell by 72.9% (95% CI, 68.1-77.2; P .001). The reductions achieved in the 2024-2025 season were greater than those observed in the 2023-2024 season. The hospital from the AC that decided not to immunize infants born before the start of the epidemic had the worst outcomes. Among the ACs that applied the same strategy, worse outcomes were observed in the one that immunized out-of-season infants through selected hospitals rather than primary care centers.

Conclusions: Nirsevimab prophylaxis in Spain during the 2024-2025 season provided even greater protection vs RSV infection in infants younger than 6 months. However, differences across ACs were observed, likely related to the specific immunization strategies implemented.

Objective: To estimate the probability of adverse events based on the length of emergency department (ED) stay prior to hospitalization in patients aged > 65 treated in Spanish EDs.

Methods: All patients aged $ 65 years treated in 52 Spanish EDs during a 1-week period and included in the EDEN (Emergency Department and Elder Needs) Registry who were hospitalized and had available data on the length of ED stay were included in the study. Adverse events considered were in-hospital mortality and prolonged hospitalization (> 7 days) in patients discharged alive. Non-linear models using restricted cubic splines were applied to estimate the probability of adverse events as a function of the length of ED stay, and crude and adjusted odds ratios (OR) with 95% confidence intervals (CI) were calculated, adjusting for 13 demographic, baseline health, and vital sign variables. First-order interactions with covariates were also examined.

Results: A total of 6,333 patients were included. The length of ED stay ranged from 0 to 240 hours prior to hospital admission (median, 6 hours; and 12.6%, 3.5%, 1.5%, and 0.2% of patients remained in the ED for more than 1, 2, 3, and 7 days, respectively). A total of 683 patients (10.8%) died during hospitalization, and among survivors, 2,734 (46.9%) experienced prolonged hospitalization. Compared with patients with a 12-hour ED stay (reference), those with stays of 6 and 3 hours had adjusted ORs for in-hospital mortality of 1.11 (1.01 1.22) and 1.22 (1.03-1.44), and defor prolonged hospitalization of 0.89 (0.84-0.95) and 0.83 (0.75-0.93), respectively. For ED stays of 1, 2, 3, and 7 days, the adjusted ORs for in-hospital mortality were 1.00 (0.94-1.06), 1.18 (0.97-1.43), 1.39 (0.98-1.97), and 2.70 (1.02-7.13), and for prolonged hospitalization, 1.14 (1.09-1.19), 1.39 (1.17-1.62), 1.66 (1.25-2.21), and 3.53 (1.60-7.76), respectively. Patients with functional limitation and hypotension showed a stronger association between the length of ED stay and in-hospital mortality, while those aged 80 years and without prior falls showed a stronger association between the length of ED stay and prolonged hospitalization.

Conclusions: Both short and long ED stays in patients who are subsequently hospitalized were associated with increased in-hospital mortality. However, shorter stays were associated with a lower likelihood of prolonged hospitalization, whereas longer stays were associated with a higher likelihood of prolonged hospitalization. These associations may be significantly stronger in certain patient subgroups.

Objective: To analyze the length of emergency department (ED) stay prior to hospital admission, its causes, and repercussions on patient health.

Methods: We conducted a retrospective observational study of length of ED stay for patients requiring hospitalization in adult medical specialties at a tertiary referral center in Spain from 2022 through 2023. We analyzed the different time intervals between triage and arrival at the hospital ward.

Results: A total of 257,814 adult patients were seen in the ED during this period. Of these, 21,984 (8.5%) were admitted to various medical services. Men accounted for 55.1%, and women were older [75 (SD, 20) vs 72 (SD, 15) years; P .001]. The mean ED stay was 22.8 hours (SD, 14.3), with 61.4% of this time dedicated to administrative processes and ward placement. Notably, 48% of patients stayed more than 12 hours, while 6% stayed less than 4 hours. Log-rank analysis showed higher in-hospital mortality among patients who stayed longer in the ED (P = .002). However, this association was not confirmed in multivariate analysis, which did reveal a significant relationship between hospital mortality, age, and admission to an "ectopic" bed (eg, outside the specific unit of the destination service) (P < .001).

Conclusions: There is a need to optimize administrative and logistical processes in the ED to reduce waiting times and enhance patient safety.

Objective: To analyze the association between the intensity of care and the various discharge destinations of patients treated in the emergency department (ED) of a tertiary referral center.

Methods: Observational cohort study. Adult patients treated from June 2021 through June 2022. The main variables were intensity of care, measured using the ATIC (Acute to Intensive Care) patient classification system, and discharge destination from the ED. Data were retrospectively collected from electronic health records.

Results: A total of 49,344 patient episodes were included. Patients were distributed across the following care intensity groups: 11.6% in Acute (nurse-to-patient ratio 1:8); 26.4% in Step-Up (ratio 1:6); 46.5% in Intermediate (ratio 1:4); 14.8% in Pre-Intensive (ratio 1:3); and 0.7% in Intensive care (ratio 1:2). Patients in higher-intensity care groups (Pre-Intensive/Intensive) showed a higher rate of adverse discharge outcomes [hospital admission, transfer to intensive care units (ICUs), and death]. The Intermediate group was associated with hospitalization [OR, 1.78 (95%CI, 1.64-1.94)], ICU transfer [OR, 2.07 (95%CI, 1.66-2.58)], and death [OR, 1.9 (95%CI, 1.14-3.19)]. The Pre-Intensive/Intensive group was a risk factor for hospitalization [OR, 3.67 (95%CI, 3.34-4.04)], ICU transfer [OR, 3.6 (95%CI, 2.82-4.61)], and death [OR, 6.19 (95%CI, 3.69-10.39)].

Conclusions: Care intensity groups showed strong associations with discharge destinations from the ED, with patients requiring higher intensity of care (Pre-Intensive/Intensive) being more likely to experience adverse outcomes (hospitalization, ICU transfer, and death).

Text: Sepsis remains one of the leading causes of mortality in emergency departments (EDs). Despite advances in definitions and management protocols, early identification continues to be a critical challenge due to the nonspecific presentation of the disease. Early management is based on 3 fundamental pillars: source control, antibiotics, and hemodynamic resuscitation, all of which require early intervention. Tools such as the SOFA score, biomarkers (C-reactive protein, procalcitonin, lactate), and protocols like the Sepsis Code have improved detection and management. However, the clinical heterogeneity of sepsis and limitations of current models hinder their universal implementation. Artificial intelligence (AI) is emerging as a key tool to improve early detection of sepsis through the analysis of large volumes of clinical data. Open data, following FAIR principles (Findable, Accessible, Interoperable, Reusable), facilitate the development of robust and personalized algorithms, minimizing bias and enhancing scientific collaboration. Spain generates vast amounts of clinical data in its EDs but lacks a unified database. The creation of an open system with data use agreements would enable the development of predictive models specific to its population. The use of A.I. in combination with specific databases promises to improve treatment personalization, reduce mortality, and optimize resources in sepsis care, changing the current paradigm of clinical management.