Summary Background Since December, 2019, Wuhan, China, has experienced an outbreak of coronavirus disease 2019 (COVID-19), caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Epidemiological and clinical characteristics of patients with COVID-19 have been reported but risk factors for mortality and a detailed clinical course of illness, including viral shedding, have not been well described. Methods In this retrospective, multicentre cohort study, we included all adult inpatients (≥18 years old) with laboratory-confirmed COVID-19 from Jinyintan Hospital and Wuhan Pulmonary Hospital (Wuhan, China) who had been discharged or had died by Jan 31, 2020. Demographic, clinical, treatment, and laboratory data, including serial samples for viral RNA detection, were extracted from electronic medical records and compared between survivors and non-survivors. We used univariable and multivariable logistic regression methods to explore the risk factors associated with in-hospital death. Findings 191 patients (135 from Jinyintan Hospital and 56 from Wuhan Pulmonary Hospital) were included in this study, of whom 137 were discharged and 54 died in hospital. 91 (48%) patients had a comorbidity, with hypertension being the most common (58 [30%] patients), followed by diabetes (36 [19%] patients) and coronary heart disease (15 [8%] patients). Multivariable regression showed increasing odds of in-hospital death associated with older age (odds ratio 1·10, 95% CI 1·03–1·17, per year increase; p=0·0043), higher Sequential Organ Failure Assessment (SOFA) score (5·65, 2·61–12·23; p Interpretation The potential risk factors of older age, high SOFA score, and d-dimer greater than 1 μg/mL could help clinicians to identify patients with poor prognosis at an early stage. Prolonged viral shedding provides the rationale for a strategy of isolation of infected patients and optimal antiviral interventions in the future. Funding Chinese Academy of Medical Sciences Innovation Fund for Medical Sciences; National Science Grant for Distinguished Young Scholars; National Key Research and Development Program of China; The Beijing Science and Technology Project; and Major Projects of National Science and Technology on New Drug Creation and Development.

Clinical course and risk factors for mortality of adult inpatients with COVID-19 in Wuhan, China: a retrospective cohort study

3-month, 6-month, 9-month, and 12-month respiratory outcomes in patients following COVID-19-related hospitalisation: a prospective study.

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745–0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

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An artificial intelligence system for predicting the deterioration of COVID-19 patients in the emergency department

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.

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Emergency Department and Out-of-Hospital Emergency System (112-AREU 118) integrated response to Coronavirus Disease 2019 in a Northern Italy centre.

During the coronavirus disease 2019 (COVID-19) pandemic, rapid and accurate triage of patients at the emergency department is critical to inform decision-making. We propose a data-driven approach for automatic prediction of deterioration risk using a deep neural network that learns from chest X-ray images and a gradient boosting model that learns from routine clinical variables. Our AI prognosis system, trained using data from 3,661 patients, achieves an area under the receiver operating characteristic curve (AUC) of 0.786 (95% CI: 0.745-0.830) when predicting deterioration within 96 hours. The deep neural network extracts informative areas of chest X-ray images to assist clinicians in interpreting the predictions and performs comparably to two radiologists in a reader study. In order to verify performance in a real clinical setting, we silently deployed a preliminary version of the deep neural network at New York University Langone Health during the first wave of the pandemic, which produced accurate predictions in real-time. In summary, our findings demonstrate the potential of the proposed system for assisting front-line physicians in the triage of COVID-19 patients.

Introduction Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2 or COVID-19) is a highly infectious viral syndrome currently threatening millions of people worldwide. It is widely recognized as a disease of the pulmonary system, presenting with fever, cough, and shortness of breath. However, a number of extrapulmonary manifestations have been described in the literature. Objective In this review, we seek to provide a comprehensive summary of the hematologic, gastroenterological, renal, dermatologic, neurologic, and psychiatric manifestations of COVID-19. Discussion Hematological presentations of COVID-19 include laboratory abnormalities such as decreased total lymphocyte count, prolonged prothrombin time (PT), elevated d-dimer, and increased lactate dehydrogenase (LDH). Several of these findings are associated with increased mortality among infected patients. The most common gastrointestinal symptoms include nausea, vomiting, diarrhea, and abdominal pain. Furthermore, presence of viral RNA in patient stool suggests the possibility of additional testing modalities for COVID-19. Nephrological findings such as proteinuria, hematuria, and elevated BUN and creatinine levels have been observed. Additionally, several studies demonstrated that patients with COVID-19 who developed acute kidney injury (AKI) had a greater risk of mortality. The virus can also present with cutaneous symptoms such as erythematous rashes, urticaria, and chicken pox-like lesions. Neuropsychiatric symptoms have been described in the literature, and patients can exhibit findings consistent with viral encephalitis, cerebral vascular disease, peripheral nerve disorders, and psychosis. Conclusion Although COVID-19 does usually present primarily with respiratory symptoms, the extra-pulmonary manifestations of the virus are unpredictable and varied. Better understanding and awareness of these symptoms can lead to more efficient diagnosis, rapid treatment, isolation, and decreased spread of the disease.

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Non-respiratory presentations of COVID-19, a clinical review.

As shown by the current COVID-19 pandemic, emergency departments (ED) are the front line for hospital-and-community-based care during viral respiratory disease outbreaks. As such, EDs must be able to reorganize and reformat operations to meet the changing needs and staggering patient volume. This paper addresses ways to adapt departmental operations to better manage in times of elevated disease burden, specifically identifying areas of intervention to help limit crowding and spread. Using experience from past outbreaks and the current COVID-19 pandemic, we advise strategies to increase surge capacity and limit patient inflow. Triage should identify and geographically cohort symptomatic patients within a designated unit to limit exposure early in an outbreak. Screening and PPE guidelines for both patient and staff should be followed closely, as determined by hospital administration and the CDC. Equipment needs are also greatly affected in an outbreak; we emphasis portable radiographic equipment to limit transport, and an upstocking of certain medications, respiratory supplies, and PPE.

Redesigning emergency department operations amidst a viral pandemic.

The COVID-19 pandemic poses significant challenges to patients with end-stage kidney disease who receive treatment in outpatient dialysis centers. These patients represent a fragile population that is at higher risk for both infection and transmission. At the start of the pandemic, many suspected COVID-19 dialysis patients were diverted to the emergency department (ED) for testing/treatment, placing a tremendous burden on the ED and inpatient dialysis units. Several recommendations and guidelines have been established to optimize patient care while also decreasing the burden on the ED and inpatient dialysis units and maximizing the ability to perform outpatient hemodialysis. As the pandemic continues, dialysis facilities will have an increasing burden to provide safe and accessible dialysis, while also being able to direct patients to the ED for either emergent dialysis or COVID-19 treatment/testing. We reviewed opinions, recommendations and guidelines developed by professional organizations and dialysis facilities for the management of "patients under investigation" (PUIs) and COVID-19 positive patients that depend on whether the suspicion occurs while the patient is at home vs. at the dialysis center.

The emergency department care for hemodialysis patient during the COVID-19 pandemic.

Gastric outlet obstruction (GOO) is a rare diagnosis that can be challenging to make as its symptoms, which include abdominal distention, nausea, and persistent vomiting, often overlap with many other acute abdominal pathologies. Point-of-care ultrasound (POCUS) can help the clinician identify gastric outlet obstruction in patients who present to the emergency department (ED). Sonographic identifiers include a markedly dilated stomach that is filled with both hyper- and hypoechoic contents and may extend into the lower abdomen in the pelvic views.

Marya Alsamman

Papers

Non-respiratory presentations of COVID-19, a clinical review.

Redesigning emergency department operations amidst a viral pandemic.

The emergency department care for hemodialysis patient during the COVID-19 pandemic.

Adult Female With Abdominal Distention.