Coronavirus disease 2019 (COVID-19): update for anesthesiologists and intensivists March 2020

Thomas-Rüddel, Daniel; Winning, Johannes; Dickmann, Petra; Ouart, Dominique; Kortgen, Andreas; Janssens, Uwe; Bauer, Michael

doi:10.1007/s00101-020-00760-3

Cited by 124 publications

(106 citation statements)

References 26 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Bei exponentiell steigenden Zahlen von COVID-19-Patienten (Tagesspiegel, 27.03.2020) mit schweren respiratorischen Störungen [1]…”

Section: Akute Ausweitung Von Inhalativem Sauerstoff In Einer Pandemieunclassified

Akute Ausweitung von inhalativem Sauerstoff in einer Pandemie

Kober¹,

Dötsch²,

Götzelmann³

et al. 2020

Anaesthesist

View full text Add to dashboard Cite

“…Bei exponentiell steigenden Zahlen von COVID-19-Patienten (Tagesspiegel, 27.03.2020) mit schweren respiratorischen Störungen [1]…”

Section: Akute Ausweitung Von Inhalativem Sauerstoff In Einer Pandemieunclassified

Akute Ausweitung von inhalativem Sauerstoff in einer Pandemie

Kober¹,

Dötsch²,

Götzelmann³

et al. 2020

Anaesthesist

View full text Add to dashboard Cite

“…Many medical interventions increase the risk of virus transmission (5,(9)(10)(11). Examples are non-invasive ventilation (NIV), airway management, CPR, or even recording an electrocardiogram (ECG) (11)(12)(13). Similar to SARS-CoV-1 patients, there are some COVID-19 patients who are "superspreaders" with a very high transmission rate (11).…”

Section: Medical Interventionsmentioning

confidence: 99%

Pre-hospital care & interfacility transport of 385 COVID-19 emergency patients: An air ambulance perspective.

Hilbert-Carius

Braun²,

Abu‐Zidan

et al. 2020

Preprint

View full text Add to dashboard Cite

Background: COVID-19, the pandemic caused by the severe acute respiratory syndrome coronavirus-2, is challenging healthcare systems worldwide. Little is known about problems faced by emergency medical services—particularly helicopter services—caring for suspected or confirmed COVID-19 patients. We aimed to describe the issues faced by air ambulance services in Europe as they transport potential COVID-19 patients.Methods: Nine different HEMS providers across Europe were invited to share their experiences and to report their data regarding the care, transport, and safety measures in suspected or confirmed COVID-19 missions. Six air ambulance providers agreed and reported their data. 385 COVID-19-related missions were analysed, including 119 primary transport missions and 266 interfacility transport missions.Results: All providers had developed special procedures and safety instructions in preparation for COVID-19. Ground transport was the preferred mode of transport in primary missions, whereas air transport was preferred for interfacility transport. In some countries the transport of COVID-19 patients by regular air ambulance services was avoided. Patients in interfacility transport missions had a significantly higher median (range) NACA Score 3 (1-7) compared with 4 (2-5), needed significantly more medical interventions, were significantly younger (59.6±16 vs 65±21 years), and were significantly more often male (73% vs 60.5%). Conclusions: All participating air ambulance providers were prepared for COVID-19. Safe care and transport of suspected or confirmed COVID-19 patients is achievable. Most patients on primary missions were transported by ground. These patients were less sick than interfacility transport patients, for whom air transport was the preferred method.

show abstract

“…COVID-19 is an exceptionally infectious disease caused by extremely severe respiratory symptoms Coronavirus 2 [1]. The virus was initially founded in Wuhan city of China, in December 2019.…”

Section: Introductionmentioning

confidence: 99%

Chest X-ray scanning based detection of COVID-19 using deepconvolutional neural network

Yadav

Sandhu

Pathak

et al. 2020

Preprint

View full text Add to dashboard Cite

Everyone’s life on earth influenced by a global coronavirus outbreak COVID- 19. Two regular practices, pathology tests, and Computer Tomography (CT) scan used to diagnose COVID-19. Pathology tests produce a considerable amount of false-positives & are time-consuming, whereas CT scans tests are costly and require expert advice. Hence, the main aim of this work is to develop a fast, accurate, and low-cost diagnostic system for detection of COVID-19 using inexpensive chest X-rays and the modern Deep Convolutional Neural Network(CNN) approach to assist medical professionals. In this study, two pre-trained CNN models (VGG16 and InceptionV3) are evaluated by several experiments using data augmentations. The analysis is based on 2905 images of chest X-rays with 219 confirmed positive COVID-19 and 1345 positive pneumonia cases taken from the open-source database consisting of patients suffering from the COVID-19 disease. Since a database consists of multiple types of diseases, multiclass classification for diagnosis of COVID-19 is used. The InceptionV3 model provides the highest classification accuracy (99.35% and 98.29%) for two binary classifications (normal vs. COVID-19 and COVID- 19 vs. Pneumonia) compare to VGG16 model’s accuracy (97.71% and 96.27%). Whereas, VGG16 provides highest accuracy (98.84%)for multiclass-classification(normal vs COVID- 19 vs pneumonia) as compared to VGG16 model’s accuracy(96.35%).

show abstract

Coronavirus disease 2019 (COVID-19): update for anesthesiologists and intensivists March 2020

Cited by 124 publications

References 26 publications

Akute Ausweitung von inhalativem Sauerstoff in einer Pandemie

Akute Ausweitung von inhalativem Sauerstoff in einer Pandemie

Pre-hospital care & interfacility transport of 385 COVID-19 emergency patients: An air ambulance perspective.

Chest X-ray scanning based detection of COVID-19 using deepconvolutional neural network

Contact Info

Product

Resources

About

Coronavirus disease 2019 (COVID-19): update for anesthesiologists and intensivists March 2020

Cited by 124 publications

References 26 publications

Akute Ausweitung von inhalativem Sauerstoff in einer Pandemie

Akute Ausweitung von inhalativem Sauerstoff in einer Pandemie

Pre-hospital care &amp; interfacility transport of 385 COVID-19 emergency patients: An air ambulance perspective.

Chest X-ray scanning based detection of COVID-19 using deepconvolutional neural network

Contact Info

Product

Resources

About

Pre-hospital care & interfacility transport of 385 COVID-19 emergency patients: An air ambulance perspective.