To determine the rate, patient profile, and outcomes of emergency department (ED) visits by older adults for major acute cardiovascular events (MACE) during the 1st wave of the COVID-19 pandemic in Spain vs the prepandemic period. We analyzed all patients aged $ 65 years who attended 52 Spanish EDs within 1 week of the 1st COVID-19 wave (April 1-7, 2020; COVID period) and within the same week of the previous year (March 30-April 5, 2019; pre-COVID period). Cardiac events (angina pectoris and myocardial infarction with and without ST-segment elevation) and cerebrovascular events (stroke) were recorded for each period, jointly and individually. We estimated changes in MACE incidence rate per 100,000 persons per year and compared patient characteristics and outcomes (30-day allcause mortality rate, intensive care unit [ICU] admission, and ED revisits, adjusted for patient characteristics) across periods. ED visits dropped by 61.8% during the COVID period (from 25,557 to 9,770). During this period, 297 patients attended the ED for MACE (incidence rate, 131.2), vs 488 during the pre-COVID period (incidence rate, 215.6; incidence reduction, 39.1%, 95% CI, 29.7-47.3). The reduction in ED visits was significantly greater (P < .001) for cardiac events (43.3 vs 99.9; reduction 56.6%, 95% CI, 45.0-65.8) than for cerebrovascular events (88.8 vs 116.7; reduction 23.9%, 95% CI, 8.5-36.6). Patients with MACE presenting during the COVID period had lower comorbidity. These patients had fewer ICU admissions (OR, 0.338; 95% CI, 0.149-0.764) and more ED revisits (OR, 1.764; 95% CI, 1.116-2.790), with no differences in 30-day mortality (HR, 1.474; 95% CI, 0.971-2.239). During the 1st wave of the COVID-19 pandemic, the rate of ED visits for MACE decreased among older adults. Patients who presented had fewer comorbidities, fewer ICU admissions, and more ED revisits, with no significant changes in mortality.

To stratify the risk of bacteremia at the time of emergency department (ED) admission in patients with hematologic malignancies. To this end, we compared the performance of unsupervised and supervised machine learning algorithms with the classical multivariable logistic regression model. We conducted a multicenter, international, retrospective cohort study including consecutive ED visits of adult patients with hematologic malignancies for whom blood cultures were obtained. The primary outcome was the prediction of bacteremia. The Bacteremia Objective Score System (BOSS)-1 used multivariable logistic regression, BOSS-2 applied K-means clustering, and BOSS-3 employed a support vector machine algorithm. Discriminative performance was assessed using sensitivity and specificity. External validation of the results was performed. The derivation cohort included 679 ED visits. Blood cultures tested positive in 88 cases (13%). BOSS-1 identified low-risk patients (3.1% bacteremia) with high sensitivity (94%; 95% CI, 87-97) but limited specificity (30%; 95% CI, 26-34). BOSS-2 better identified high-risk patients (29% bacteremia; sensitivity 93% [95% CI, 84-97], specificity 30% [95% CI, 26-34]). BOSS-3 classified all patients into low-risk (66.8%) or high-risk (33.2%) categories, without an intermediate group, unlike the other models, showing the best balance between sensitivity (61%; 95% CI, 59-64) and specificity (71%; 95% CI, 70-73) and yielding the most reproducible results in the external validation cohort. The BOSS algorithms enabled effective bacteremia risk stratification in the ED, with supervised machine learning models demonstrating the greatest potential clinical utility.

To describe the appropriateness of triage according to safety conditions, incident characteristics, and observed injury patterns in an intentional vehicle-ramming (VR) mass-casualty incident (MCI). We conducted a retrospective observational study of the intentional VR-MCI that occurred on August 17th, 2017, in Barcelona (Spain). Data were collected from emergency medical services and hospital records regarding decision-making processes, triage strategies, and hospital diagnoses. A total of 153 victims were attended, including 14 fatalities (9%) and 139 injured patients (91%), across a large affected area of 13,608 m2. Initially, due to compromised safety conditions and the high number of casualties, a triage strategy based on expert visual assessment using surgical priority criteria (VIVE-Q) with immediate transport was implemented. Once safety in the healthcare area was established, the advanced prehospital triage model (META) was applied. Both triage strategies allowed appropriate prioritization and correct destination assignment in 98.1% of patients, with no associated in-hospital mortality. Assistance time was shorter in patients triaged with VIVE-Q vs META [19 (IQR 21) vs 28 (IQR 47) minutes; P = .001]. Regarding injury patterns, 25% of the injured patients were categorized as severe (red), with 87% presenting $ 2 injuries, predominantly involving the head and thoracoabdominal regions. Security compromise during an intentional VR-MCI required adaptation of emergency response and triage plans, which proved to be effective.

Dyspnea is a frequent reason for consultation in emergency departments (EDs). Ultrasound artifacts generated within lung tissue can be interpreted for diagnostic purposes. The primary endpoint of this study was to provide evidence on the feasibility and applicability of the CLINIC-LUS algorithm, based on lung auscultation and lung ultrasound, for diagnosing causes of acute dyspnea in adult patients presenting to hospital. We conducted a prospective, multicenter feasibility study in patients evaluated for acute dyspnea in EDs. Lung auscultation and lung ultrasound patterns were assessed using a 12-point ultrasound protocol. Feasibility was evaluated by the time (in minutes) required to perform lung ultrasound and by the investigator's perception. Diagnostic accuracy (percentage of correct diagnoses) and the Cohen kappa coefficient for agreement of the algorithm were calculated. A total of 95 patients with acute dyspnea presenting to EDs from January 2024 through June 2025 were included and eventually analyzed. The time required to perform lung ultrasound [median (range)] was 12 (9 16) minutes. Investigators unanimously reported that use of the CLINIC-LUS algorithm was straightforward and did not pose difficulties in clinical practice. Diagnostic accuracy was 78.1% (95% CI, 67.5-86.4). The kappa coefficient was 0.73 (95% CI, 0.52-0.82). Integrating physical examination with lung ultrasound into a single algorithm for diagnosing causes of acute dyspnea in the ED is feasible in clinical practice.