Citations

Introduction: Incident cardiac arrhythmias are common in hospitalized COVID-19 patients, but the impact of a second incident cardiac arrhythmia during hospitalization on clinical outcomes remain unclear. Hypothesis: The aim of this study was to assess the frequency and prognostic implications of a second incident arrhythmia in hospitalized COVID-19 patients over the pandemic course till vaccine availability. Methods: The multicenter COVID-RHYTHM Registry enrolled hospitalized COVID-19 patients from 3/8/2020 to 1/24/2021. Cardiac arrhythmia included documented atrial fibrillation (AF), atrial flutter (AFL), ventricular tachycardia (VT), severe sinus bradycardia (<40 bpm) or AV block. Outcomes were compared between patients with single vs. more than one incident arrhythmias adjusting for baseline variables. Results: Of 6299 hospitalized COVID-19 patients, 4398 had an had an incident cardiac arrhythmia during hospitalization, of which 822 (19%) had a second incident arrhythmia. Those with >1 incident arrhythmia had a higher baseline prevalence of heart failure, chronic kidney disease, coronary artery disease and sleep apnea (Table). Those with >1 incident arrhythmia had higher in-hospital mortality, need for ICU admission and need for rehab upon discharge. Length of stay was similar between the groups. Conclusions: In hospitalized COVID-19 patients with incident cardiac arrhythmias, a second incident arrhythmia during hospitalization was seen in 19%. Having >1 incident arrhythmia was associated with increased need for ICU admission and reduced survival.

The enteric nervous system (ENS), a collection of neural cells contained in the wall of the gut, is of fundamental importance to gastrointestinal and systemic health. According to the prevailing paradigm, the ENS arises from progenitor cells migrating from the neural crest and remains largely unchanged thereafter. Here, we show that the lineage composition of maturing ENS changes with time, with a decline in the canonical lineage of neural-crest derived neurons and their replacement by a newly identified lineage of mesoderm-derived neurons. Single cell transcriptomics and immunochemical approaches establish a distinct expression profile of mesoderm-derived neurons. The dynamic balance between the proportions of neurons from these two different lineages in the post-natal gut is dependent on the availability of their respective trophic signals, GDNF-RET and HGF-MET. With increasing age, the mesoderm-derived neurons become the dominant form of neurons in the ENS, a change associated with significant functional effects on intestinal motility which can be reversed by GDNF supplementation. Transcriptomic analyses of human gut tissues show reduced GDNF-RET signaling in patients with intestinal dysmotility which is associated with reduction in neural crest-derived neuronal markers and concomitant increase in transcriptional patterns specific to mesoderm-derived neurons. Normal intestinal function in the adult gastrointestinal tract therefore appears to require an optimal balance between these two distinct lineages within the ENS.

Therapeutic plasma exchange (TPE) is a treatment paradigm used to remove harmful molecules from the body. In short, it is a technique that employs a process that functions partially outside the body and involves the replacement of the patient’s plasma. It has been used in the ICU for a number of different disease states, for some as a first-line treatment modality and for others as a type of salvage therapy. This paper provides a brief review of the principles, current applications, and potential future directions of TPE in critical care settings.

Abstract Aims ECG abnormalities are often the first signs of arrhythmogenic right ventricular cardiomyopathy (ARVC) and we hypothesized that an artificial intelligence (AI)-enhanced ECG could help identify patients with ARVC and serve as a valuable disease-detection tool. Methods and results We created a convolutional neural network to detect ARVC using a 12-lead ECG. All patients with ARVC who met the 2010 task force criteria and had disease-causative genetic variants were included. All case ECGs were randomly assigned in an 8:1:1 ratio into training, validation, and testing groups. The case ECGs were age- and sex-matched with control ECGs at our institution in a 1:100 ratio. Seventy-seven patients (51% male; mean age 47.2 ± 19.9), including 56 patients with PKP2, 7 with DSG2, 6 with DSC2, 6 with DSP, and 2 with JUP were included. The model was trained using 61 case ECGs and 5009 control ECGs; validated with 7 case ECGs and 678 control ECGs and tested in 22 case ECGs and 1256 control ECGs. The sensitivity, specificity, positive and negative predictive values of the model were 77.3, 62.9, 3.32, and 99.4%, respectively. The area under the curve for rhythm ECG and median beat ECG was 0.75 and 0.76, respectively. Conclusion Our study found that the model performed well in excluding ARVC and supports the concept that the AI ECG can serve as a biomarker for ARVC if a larger cohort were available for network training. A multicentre study including patients with ARVC from other centres would be the next step in refining, testing, and validating this algorithm.