Extracorporeal membrane oxygenation (ECMO) has been adopted to support patients with acute severe cardiac or pulmonary failure that is potentially reversible and unresponsive to conventional management. In the presence of pulmonary embolism, mesenteric ischemia (MI) can present as a life-threatening disorder that leads to intestinal ischemia. Due to the nature and acuity of these conditions, determining adequate perfusion upon surgical intervention is challenging for the operating surgeon, especially in the presence of cardiogenic shock despite ECMO support. Indocyanine green fluorescent angiography (ICG-FA) has proven to be useful for real-time vascular perfusion assessment, which may potentially decrease the rate of development of perfusion-related complications. The case report here-in presented, breaks the paradigm of performing noncardiac surgical procedures on ECMO support via a pioneering visual aid technique. Learning objective Indocyanine green fluorescent angiography (ICG-FA) is a promising visual trans-operatory technique providing real-time feedback for the adequate identification and assessment of target tissue/organs. The high morbidity and mortality rates associated to MI and CS – particularly when concomitantly present – hinders salvage surgical therapy. The use of acute ECMO provides stabilization yet lacks any curative solutions. This case report highlights the importance of adequate surgical intervention under extracorporeal life support in the presence of both CS and MI. To the authors’ knowledge, said approach has never been attempted, yet trails a promising therapy for the improvement of associated mortality rates.

Introduction Extracorporeal membrane oxygenation (ECMO) is implemented as a rescue therapy in COVID-19 related acute distress respiratory syndrome (ARDS) and refractory hypoxemia. Google trends (GT) is an ongoing-developing web-kit providing feedback on specific population’s interests. This study uses GT to analyze the United States (US) general population interest in ECMO as COVD-19/ARDS salvage therapy. Methods GT was used to access data searched for the term ECMO and COVID-19. The gathered information included data from March 2020 through July 2021 within US territories. Search frequency, time intervals, sub-regions, frequent topics of interest, and related searches were analyzed. Data was reported as search frequency on means, and a value of 100 represented overall peak popularity. Results The number of Google searches related to the terms ECMO and COVID-19 has surged and sustained interest over time ever since the initial reports of COVID-19 in the US, from an initial mean of 34% in March 2020 to a 100% interest by April 2020, resulting in an up-to-date overall average of 40% interest. Over time West Virginia, Gainesville, and Houston, lead the frequency of searches in sub-region, metro and city areas, respectively. Top search terms by frequency include: ECMO machine, COVID ECMO, what is ECMO, ECMO treatment and VV ECMO. Parallel to this, the related rising terms are: COVID ECMO, ECMO machine COVID, ECMO for COVID, ECMO machine coronavirus, and ECMO vs ventilator. Seemingly, medical-relevant websites fail to adequately address these for patient therapeutic education (PTE) purposes. Conclusions GT complements the understanding of interest in ECMO for COVID-19. When properly interpreted, the use of these trends can potentially improve on PTE and therapy awareness via specific medical relevant websites.

Background & Aim Cardiogenic shock (CS) withholds a significantly high mortality rate between 40-60% despite advances in diagnosis and medical/surgical intervention. To-date, machine learning(ML) is being implemented to integrate numerous data to optimize early diagnostic predictions and suggest clinical courses. This systematic review summarizes the area under the curve (AUC) receiver operating characteristics (ROC) accuracy for the early prediction of CS. Methods A systematic review was conducted within databases of PubMed, ScienceDirect, Clinical Key/MEDLINE, Embase, GoogleScholar, and Cochrane. Cohort studies that assessed accuracy of early detection of CS using ML software were included. Data extraction was focused on AUC-ROC values directed towards early detection of CS. Results A total of 943 studies were included for systematic review. From the reviewed studies, 2.2% (N=21) evaluated patient outcomes, of which 14.3% (N=3) were assessed. The collective patient cohort (N=698) consisted of 314(45.0%) females, with an average age and body mass index (BMI) of 64.1years and 28.1kg/m2, respectively. Collectively, 159 (22.8%) mortalities were reported following early CS detection. Altogether, the AUC-ROC value was 0.82 (alfa=0.05), deeming it of superb sensitivity and specificity. Conclusions From the present comprehensively gathered data, this study accounts the use of ML software for the early detection of CS in a clinical setting as a valid tool to predict patients at risk of CS. The complexity of ML and its parallel lack of clinical evidence implies that further prospective randomized control trials are needed to draw definitive conclusions prior to standardizing use of these technologies.

High volume ECMO centers have developed mobile ECMO programs in recent years to facilitate implementation of ECMO support at hospitals with lower capabilities, and transfer these patients for further care. We report a case of mobile ECMO on patient with COVID-19 related ARDS, and discuss the potential application in current SARS-CoV-2 pandemic.