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Objectives To evaluate the inter-rater agreement of chest X-ray (CXR) findings in coronavirus disease 2019 (COVID-19) and to determine the value of initial CXR along with demographic, clinical, and laboratory data at emergency department (ED) presentation for predicting mortality and the need for ventilatory support. Methods A total of 340 COVID-19 patients who underwent CXR in the ED setting (March 1–13, 2020) were retrospectively included. Two reviewers independently assessed CXR abnormalities, including ground-glass opacities (GGOs) and consolidation. Two scoring systems ( Brixia score and percentage of lung involvement) were applied. Inter-rater agreement was assessed by weighted Cohen’s kappa ( κ ) or intraclass correlation coefficient (ICC). Predictors of death and respiratory support were identified by logistic or Poisson regression. Results GGO admixed with consolidation ( n = 235, 69%) was the most common CXR finding. The inter-rater agreement was almost perfect for type of parenchymal opacity ( κ = 0.90), Brixia score (ICC = 0.91), and percentage of lung involvement (ICC = 0.95). The Brixia score (OR: 1.19; 95% CI: 1.06, 1.34; p = 0.003), age (OR: 1.16; 95% CI: 1.11, 1.22; p < 0.001), PaO 2 /FiO 2 ratio (OR: 0.99; 95% CI: 0.98, 1; p = 0.002), and cardiovascular diseases (OR: 3.21; 95% CI: 1.28, 8.39; p = 0.014) predicted death. Percentage of lung involvement (OR: 1.02; 95% CI: 1.01, 1.03; p = 0.001) and PaO 2 /FiO 2 ratio (OR: 0.99; 95% CI: 0.99, 1.00; p < 0.001) were significant predictors of the need for ventilatory support. Conclusions CXR is a reproducible tool for assessing COVID-19 and integrates with patient history, PaO 2 /FiO 2 ratio, and SpO 2 values to early predict mortality and the need for ventilatory support. Key Points • Chest X-ray is a reproducible tool for assessing COVID-19 pneumonia. • The Brixia score and percentage of lung involvement on chest X-ray integrate with patient history, PaO 2 /FIO 2 ratio, and SpO 2 values to early predict mortality and the need for ventilatory support in COVID-19 patients presenting to the emergency department.

Background There is no ubiquitous definition of Emergency Department (ED) crowding and several indicators have been proposed to measure it. The National ED Overcrowding Study (NEDOCS) score is among the most popular, even though it has been severely criticised. We used the waiting time for the physician’s initial assessment to evaluate the performance of the NEDOCS and proposed a new crowding indicator based on this objective measure. Methods To evaluate the NEDOCS, we used the 2022 data of all the Lombardy EDs and compared the distribution of waiting times across the five levels of the NEDOCS at ED arrival. To construct the new indicator, we estimated the centre-specific relationship between the total number of ED patients and the waiting time of those with minor or deferrable urgency. We defined seven classes of waiting times and calculated how many patients corresponded to an average waiting time in the classes. These centre-specific cutoffs were used to define the 7-level crowding indicator. The indicator was then compared to the NEDOCS score and validated on the first six months of 2023 data. Results Patients’ waiting time did not increase at the increase of the NEDOCS score, suggesting the absence of a relationship between this score and the effect of ED crowding on the ED capacity of evaluating new patients. The indicator we propose is easy to estimate in real-time and based on centre-specific cutoffs, which depend on the volume of yearly accesses. We observed minimal agreement between the proposed indicator and the NEDOCS in most EDs, both in the development and validation datasets. Conclusions We proposed to quantify ED crowding using the waiting time for physician’s initial assessment of patients with minor or deferrable urgency, which increases in crowding situations due to the prioritization of urgent patients. The centre-specific cutoffs avoid the problem of the heterogeneity of the volume of accesses and organization among EDs, while enabling a fair comparison between centres.

Background/Objectives: Emergency department (ED) crowding has become a pressing global concern exacerbated by the COVID-19 pandemic. No studies have addressed this issue in Europe during the post-pandemic period so far. This study examined ED visit volumes, patient acuity, hospital admission rates, emergency vehicle arrivals, and crowding metrics before, during, and after the pandemic. Methods: We conducted a retrospective descriptive study including data on all ED visits in the Lombardy Region of Italy from January 2019 to December 2023. Furthermore, an inferential statistical analysis was performed to compare ED trends between 2019 and 2023. Results: During the analyzed period, there were 15,515,128 visits across all Lombardy EDs. ED visits dropped from 3,514,426 in 2019 to 2,380,005 in 2020, then rebounded to 3,464,756 in 2023. In 2019, triage code distribution was 9.9% white, 68.7% green, 19.0% yellow, and 1.9% red. During the pandemic, the proportion of white and green codes decreased. By 2023, these comprised 80.7% of the total. The percentage of admitted patients was 11.9% in 2019, rose to 16.2% in 2020, and returned to 11.4% in 2023. The median ED length of stay (EDLOS) for admitted patients in 2023 was 5.2 h (IQR [2.1–17.4]), compared to 3.8 h (IQR [1.6–8.6]) in 2019 (p-value < 0.01). The median EDLOS for discharged patients in 2023 was 2.7 h (IQR [1.4–4.9]), compared to 2.4 h (IQR [1.3–4.4]) in 2019 (p-value < 0.01). The rate of patients leaving before completing treatment was 5.0% in 2019 and peaked at 6.8% in 2023 (p-value < 0.01). Conclusions: In 2023, ED visits in Lombardy increased, compared to the pandemic period, but remained below 2019 levels. The proportion of high-acuity codes and hospital admissions was slightly lower than in 2019. However, ED crowding metrics worsened. The high levels of lower-acuity visits and the deterioration in crowding metrics highlight systemic challenges within the healthcare system.

Background: he surge in the use of Pre-hospital Emergency Medical Systems (EMS) and Emergency Departments (ED) has become a pressing issue worldwide after the COVID-19 pandemic. To address this challenge, we developed an experimental and innovative care pathway supported by telemedicine. The aim of this study is to describe the activity of the Integrated Medical Center (CMI): a new telemedicine-based care model for patients referring to the Emergency Medical System. Methods: A prospective observational study was conducted from January 2022 to December 2022. The CMI was established to manage patients referring to the Emergency Medical System. Results: From January to December 2022, a total of 8680 calls were managed by CMI, with an average of 24 calls per day. 6243 patients (71.9%) were managed without ED access of whom 4884 patients (78.2%) were managed through telemedicine evaluation only, and 1359 (21.8%) with telemedicine evaluation and dispatch of the Home Rapid Response Team (HRRT). The population treated by the HRRT exhibited a higher age. The mean satisfaction score was 9.1/10. Conclusions: Telemedicine evaluation allowed for remote assessments, treatment prescriptions, and teleconsultation for HRRT and was associated with high patient satisfaction. This model could be useful in future pandemics for managing patients with non-urgent illnesses at home, preventing hospital admissions for potentially infectious patients, and thereby reducing in-hospital transmission.

* Jean-Louis Vincent, * Greg S. Martin and * Mitchell M. Levy We thank Drs Franchini and Duca for their comments. Physicians have long used fever, associated tachycardia and altered white blood cell count as signs of infection ... we have never needed the SIRS criteria to help with this and we don't need the qSOFA for this either. Furthermore, qSOFA does not replace SIRS as a screening tool for sepsis because it was conceived, derived and validated as a prognostic tool. Moreover, sepsis is more often identified from associated unexplained organ dysfunction than from infection [5]. The use of qSOFA as an alarm signal should be further validated, keeping in mind that it is not specific for sepsis. Patients with many other conditions, including severe heart failure, blood loss, pulmonary embolism and any form of acute circulatory failure (shock), can have hypotension, altered mental status and hyperventilation, thus meeting the qSOFA criteria without having sepsis. But, it is still important to identify these patients and act quickly, whatever the underlying cause. The best screening tools for sepsis remain within the minds of clinicians, suspecting infection and assessing organ function using an array of criteria that so far have eluded complete description.