The impact of fever as a testing indication on the detection rate of deep vein thrombosis in neurocritically ill patients.

Aim: Deep vein thrombosis (DVT) is considered a possible source of non-infectious, non-central fever in the intensive care unit (ICU). In the neurocritically ill, it is unknown whether lower extremity venous Doppler ultrasonography (LEVDUS) for DVT in the setting of fever leads to a higher detection rate than the baseline detection rate of DVT in this population. The aim of this study was to compare the DVT detection rate of LEVDUS performed for the indication of fever to LEVDUS performed for other indications in a neurosciences ICU.

Early tracheostomy in patients undergoing mechanical thrombectomy for acute ischemic stroke.

Purpose: Respiratory failure following mechanical thrombectomy (MT) for acute ischemic stroke (AIS) is a known complication, and requirement of tracheostomy is associated with worse outcomes. Our objective is to evaluate characteristics associated with tracheostomy timing in AIS patients treated with MT.

Methods: The National Inpatient Sample was queried for adult patients treated with MT for AIS from 2016 to 2019.

Extracorporeal membrane oxygenation in COVID-19 compared to other etiologies of acute respiratory failure: A single-center experience.

Background: The COVID-19 pandemic has led to a boom in the use of V-V ECMO for ARDS secondary to COVID. Comparisons of outcomes of ECMO for COVID to ECMO for influenza have emerged. Very few comparisons of ECMO for COVID to ECMO for ARDS of all etiologies are available.

Deep Learning Based COVID-19 Detection Using Medical Images: Is Insufficient Data Handled Well?

Deep learning is a prominent method for automatic detection of COVID-19 disease using a medical dataset. This paper aims to give a perspective on the data insufficiency issue that exists in COVID-19 detection associated with deep learning. The extensive study of the available datasets comprising CT and X-ray images is presented in this paper, which can be very much useful in the context of a deep learning framework for COVID-19 detection.