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Accuracy of Emergency Department Clinical Findings for Diagnosis of Coronavirus Disease 2019.

ABSTRACT: STUDY OBJECTIVE:We seek to describe the medical history and clinical findings of patients attending the emergency department (ED) with suspected coronavirus disease 2019 (COVID-19) and estimate the diagnostic accuracy of patients' characteristics for predicting COVID-19. METHODS:We prospectively enrolled all patients tested for severe acute respiratory syndrome coronavirus 2 by reverse-transcriptase polymerase chain reaction in our ED from March 9, 2020, to April 4, 2020. We abstracted medical history, physical examination findings, and the clinical probability of COVID-19 (low, moderate, and high) rated by emergency physicians, depending on their clinical judgment. We assessed diagnostic accuracy of these characteristics for COVID-19 by calculating positive and negative likelihood ratios. RESULTS:We included 391 patients, of whom 225 had positive test results for severe acute respiratory syndrome coronavirus 2. Reverse-transcriptase polymerase chain reaction result was more likely to be negative when the emergency physician thought that clinical probability was low, and more likely to be positive when he or she thought that it was high. Patient-reported anosmia and the presence of bilateral B lines on lung ultrasonography had the highest positive likelihood ratio for the diagnosis of COVID-19 (7.58, 95% confidence interval [CI] 2.36 to 24.36; and 7.09, 95% CI 2.77 to 18.12, respectively). The absence of a high clinical probability determined by the emergency physician and the absence of bilateral B lines on lung ultrasonography had the lowest negative likelihood ratio for the diagnosis of COVID-19 (0.33, 95% CI 0.25 to 0.43; and 0.26, 95% CI 0.15 to 0.45, respectively). CONCLUSION:Anosmia, emergency physician estimate of high clinical probability, and bilateral B lines on lung ultrasonography increased the likelihood of identifying COVID-19 in patients presenting to the ED.

SUBMITTER: Peyrony O

PROVIDER: S-EPMC7241345 | biostudies-literature | 2020 Oct

REPOSITORIES: biostudies-literature

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Accuracy of Emergency Department Clinical Findings for Diagnosis of Coronavirus Disease 2019.

Peyrony Olivier O Marbeuf-Gueye Carole C Truong Vy V Giroud Marion M Rivière Clémentine C Khenissi Khalil K Legay Léa L Simonetta Marie M Elezi Arben A Principe Alessandra A Taboulet Pierre P Ogereau Carl C Tourdjman Mathieu M Ellouze Sami S Fontaine Jean-Paul JP

Annals of emergency medicine 20200521 4

<h4>Study objective</h4>We seek to describe the medical history and clinical findings of patients attending the emergency department (ED) with suspected coronavirus disease 2019 (COVID-19) and estimate the diagnostic accuracy of patients' characteristics for predicting COVID-19.<h4>Methods</h4>We prospectively enrolled all patients tested for severe acute respiratory syndrome coronavirus 2 by reverse-transcriptase polymerase chain reaction in our ED from March 9, 2020, to April 4, 2020. We abstr ...[more]

PMID: 32563600

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Project description:ObjectiveThere have been few descriptions in the literature to date specifically examining initial coronavirus disease 2019 (COVID-19) patient presentation to the emergency department (ED) and the trajectory of patients who develop critical illness. Here we describe the ED presentation and outcomes of patients with COVID-19 presenting during our initial local surge.MethodsThis is a multicenter, retrospective cohort study using data extracted from the electronic health records at 3 hospitals within a single health system from March 1, 2020 to June 1, 2020. Patients were included in the study if they presented to an ED and had laboratory-confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection during the study period. Data elements were extracted from the electronic health record electronically and by trained data abstractors and entered into a secure database. We used multivariable regression analysis to examine ED factors associated with the development of critical illness and mortality, with a primary outcome of ICU admission.ResultsA total of 330 patients with laboratory-confirmed SARS-CoV-2 infection were admitted during the study period. Of these, 112 (34%) were admitted to the ICU. Among these patients, 20% were female, 50% were White, the median age was 61 (interquartile range [IQR], 52-72), and the median body mass index (BMI) was 28.1 (IQR, 24.3-35.1). On univariable analysis, a doubling of lactate dehydrogenase (LDH) (odds ratio [OR], 3.87; 95% confidence interval [CI], 2.40-6.27) or high-sensitivity C-reactive protein (hsCRP; OR, 1.32; 95% CI, 1.11-1.57) above the reference range or elevated troponin (OR, 12.1; 95% CI, 1.20-121.8) were associated with ICU admission. After adjusting for age, sex, and BMI, LDH was the best predictor of ICU admission (OR, 3.54; 95% CI, 2.12-5.90). Of the patients, 15% required invasive mechanical ventilation during their hospital course, and in-hospital mortality was 19%.ConclusionsNearly one-third of ED patients who required hospitalization for COVID-19 were admitted to the ICU, 15% received invasive mechanical ventilation, and 19% died. Most patients who were admitted from the ED were tachypneic with elevated inflammatory markers, and the following factors were associated with ICU admission: elevated hsCRP, LDH, and troponin as well as lower oxygen saturation and increased respiratory rate.

Project description:Since December 2019, the world has been facing the life-threatening disease, named Coronavirus disease-19 (COVID-19), recognized as a pandemic by the World Health Organization. The response of the Emergency Medicine network, integrating "out-of-hospital" and "hospital" activation, is crucial whenever the health system has to face a medical emergency, being caused by natural or human-derived disasters as well as by a rapidly spreading epidemic outbreak. We here report the Pavia Emergency Medicine network response to the COVID-19 outbreak. The "out-of-hospital" response was analysed in terms of calls, rescues and missions, whereas the "hospital" response was detailed as number of admitted patients and subsequent hospitalisation or discharge. The data in the first 5 weeks of the Covid-19 outbreak (February 21-March 26, 2020) were compared with a reference time window referring to the previous 5 weeks (January 17-February 20, 2020) and with the corresponding historical average data from the previous 5 years (February 21-March 26). Since February 21, 2020, a sudden and sustained increase in the calls to the AREU 112 system was noted (+?440%). After 5 weeks, the number of calls and missions was still higher as compared to both the reference pre-Covid-19 period (+?48% and?+?10%, respectively) and the historical control (+?53% and?+?22%, respectively). Owing to the overflow from the neighbouring hospitals, which rapidly became overwhelmed and had to temporarily close patient access, the population served by the Pavia system more than doubled (from 547.251 to 1.135.977 inhabitants,?+?108%). To minimize the possibility of intra-hospital spreading of the infection, a separate "Emergency Department-Infective Disease" was created, which evaluated 1241 patients with suspected infection (38% of total ED admissions). Out of these 1241 patients, 58.0% (n?=?720) were admitted in general wards (n?=?629) or intensive care unit (n?=?91). To allow this massive number of admissions, the hospital reshaped many general ward Units, which became Covid-19 Units (up to 270 beds) and increased the intensive care unit beds from 32 to 60. In the setting of a long-standing continuing emergency like the present Covid-19 outbreak, the integration, interaction and team work of the "out-of-hospital" and "in-hospital" systems have a pivotal role. The present study reports how the rapid and coordinated reorganization of both might help in facing such a disaster. AREU-112 and the Emergency Department should be ready to finely tune their usual cooperation to respond to a sudden and overwhelming increase in the healthcare needs brought about by a pandemia like the current one. This lesson should shape and reinforce the future.

Dataset Information

Accuracy of Emergency Department Clinical Findings for Diagnosis of Coronavirus Disease 2019.

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Accuracy of Emergency Department Clinical Findings for Diagnosis of Coronavirus Disease 2019.

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