Discovering the underlying typology of emergency departments.

BACKGROUND: We hypothesized that monitoring the volume of activity and overall performance indicators is not sufficient to understand the underlying differences between emergency departments. We aimed to understand the underlying common characteristics of emergency departments and map their typology in order to propose adaptive solutions, that would take into account territorial specificities and manage existing resources. METHODS: We applied a multifactorial analysis based on input data at three levels; 1) the health care available in the area surrounding the emergency departments, 2) the level of medical technicality of the hospitals and 3) the profile of emergency department visits. RESULTS: We included 73 emergency departments in this study, representing 93.6% of the emergency departments in our region and seven groups were retained. The smallest group (n = 5) included both public and private structures with low volumes of activity. These medical structures were associated with the shortest length of stay and one of the lowest hospitalisation rates. The largest group (n = 21) included only public structures in peri-urban areas, which were associated with the highest rate of hospitalization in the region. The surrounding population was representative of the regional population, but the patients were older. CONCLUSIONS: This approach represents a systemic response to target the organisational needs and constraints, propose appropriate solutions and adjust the financial resources allocated to hospitals. Future policies to improve care delivery may benefit from stratifying solutions and performance objectives depending on these groups.

Impact of Air Transport on SpO2/FiO2 among Critical COVID-19 Patients during the First Pandemic Wave in France.

During the first wave of the COVID-19 pandemic, some French regions were more affected than others. To relieve those areas most affected, the French government organized transfers of critical patients, notably by plane or helicopter. Our objective was to investigate the impact of such transfers on the pulse oximetric saturation (SpO2)-to-inspired fraction of oxygen (FiO2) ratio among transferred critical patients with COVID-19. We conducted a retrospective study on medical and paramedical records. The primary endpoint was the change in SpO2/FiO2 during transfers. Thirty-eight patients were transferred between 28 March and 5 April 2020, with a mean age of 62.4 years and a mean body mass index of 29.8 kg/m2. The population was 69.7% male, and the leading medical history was hypertension (42.1%), diabetes (34.2%), and dyslipidemia (18.4%). Of 28 patients with full data, we found a decrease of 28.9 points in SpO2/FiO2 (95% confidence interval, 5.8 to 52.1, p = 0.01) between the starting and the arrival intensive care units (SpO2/FiO2, 187.3 ± 61.3 and 158.4 ± 62.8 mmHg, respectively). Air medical transfers organized to relieve intensive care unit teams under surging conditions during the first COVID wave were associated with significant decreases in arterial oxygenation.

Diagnostic accuracy of copeptin sensitivity and specificity in patients with suspected non-ST-elevation myocardial infarction with troponin I below the 99th centile at presentation.

OBJECTIVE: To determine whether copeptin-us can rule out diagnosis of non-ST-segment elevation myocardial infarction (NSTEMI) without prolonged monitoring and serial blood sampling in patients with high-sensitive cardiac troponin I (hs-cTnI) below the 99th centile at presentation to the emergency department (ED) [corrected]. DESIGN: Prospective, non-randomised, individual blinded diagnostic accuracy study. SETTING: Two EDs of a rural region of France. PARTICIPANTS: Patients with chest pain suspected of NSTEMI with onset within the last 12 h were considered for enrollment. INTERVENTIONS: Serial clinical, electrographical and biochemical investigations were performed at admission and after 2, 4, 6 and 12 h. Hs-cTnI was measured using an assay with Dimension VISTA, Siemens [corrected]. Copeptin was measured by the BRAHMS copeptin-us assay on the KRYPTOR Compact Plus system. The follow-up period was 90 days. PRIMARY AND SECONDARY OUTCOME MEASURES: Copeptin, troponin, myoglobin and creatine kinase values. Clinical and paraclinical events. The final diagnosis was adjudicated blinded to copeptin result. RESULTS: During 12 months, 102 patients were analysed. Final diagnosis was NSTEMI for 7.8% (n=8), unstable angina for 3.9% (n=4), cardiac but non-coronary artery disease for 8.8% (n=9), non-cardiac chest pain for 52% (n=53) and unknown for 27.5% (n=28). There was no statistical difference for copeptin values between patients with NSTEMI and others (respectively 5.5 pmol/L IQR (3.1-7.9) and 6.5 pmol/L IQR (3.9-12.1), p=0.49). Only one patient with NSTEMI had a copeptin value above the cut-off of 95th centile at admission. CONCLUSIONS: In this study, copeptin does not add a diagnostic value at admission to ED for patients with suspected acute coronary syndrome without ST-segment elevation and with hs-cTnI below the 99th centile [corrected]. TRIAL REGISTRATION NUMBER: Clinicaltrials.gov identifier: NCT01334645.