Objectives A significant proportion of COVID‐19 patients may have cardiac involvement including arrhythmias. Although arrhythmia characterization and possible predictors were previously reported, there are conflicting data regarding the exact prevalence of arrhythmias. Clinically applicable algorithms to classify COVID patients' arrhythmic risk are still lacking, and are the aim of our study. Methods We describe a single center cohort of hospitalized patients with a positive nasopharyngeal swab for COVID‐19 during the initial Israeli outbreak between 1/2/2020 –30/5/2020. The study's outcome was any documented arrhythmia during hospitalization, based on daily physical examination, routine ECG's, periodic 24‐hour Holter, and continuous monitoring. Multivariate analysis was used to find predictors for new arrhythmias and create classification trees for discriminating patients with high and low arrhythmic risk. Results Out of 390 COVID‐19 patients included, 28 (7.2%) had documented arrhythmias during hospitalization, including: 23 atrial tachyarrhythmias, combined atrial fibrillation (AF) and ventricular fibrillation, ventricular tachycardia storm, and 3 bradyarrhythmias. Only 7/28 patients had previous arrhythmias. Our study showed significant correlation between disease severity and arrhythmia prevalence (p<0.001) with a low arrhythmic prevalence among mild disease patients (2%). Multivariate analysis revealed background heart failure (CHF) and disease severity are independently associated with overall arrhythmia while age, CHF, disease severity, and arrhythmic symptoms are associated with tachyarrhythmias. A novel decision tree using age, disease severity, CHF, and troponin levels was created to stratify patients into high and low risk for developing arrhythmia. Conclusions Dominant arrhythmia among COVID‐19 patients is AF. Arrhythmia prevalence is associated with age, disease severity, CHF, and troponin levels. A novel simple Classification tree, based on these parameters, can discriminate between high and low arrhythmic risk patients.

Background: Blood culture contamination leads to unnecessary interventions and costs. It may be caused by bacteria in deep skin structures unsusceptible to surface decontamination. This study was designed to test whether diversion of blood obtained at venipuncture into a lithium heparin tube prior to aspiration of blood culture reduces contamination.Methods: The order of blood draws for biochemistry and blood cultures was randomized. Following standard disinfection and venipuncture, blood was either aspirated into a sterile lithium heparin tube before blood culture bottles (diversion group) or blood cultures first and then lithium heparin tube (control group). All study personnel were blinded with the exception of the phlebotomist.Results: After exclusions, 970 blood culture/biochemistry sets were analyzed. Contamination occurred in 24 of 480 (5.0%) control vs 10 of 490 (2.0%) diversion group cultures (P = .01). True pathogens were identified in 26 of 480 (5.4%) control vs 18 of 490 (3.7%) diversion cultures (P = .22). Despite randomization, demographic differences were apparent between the 2 groups. A post hoc analysis of 637 cultures from 610 medical patients admitted from home neutralized demographic differences. Culture contamination remained more frequent in the control vs diversion group (17/312 [5%] vs 7/325 [2%]; P = .03). Fewer diversion group patients were admitted to hospital (control: 200/299 [66.9%] vs diversion: 182/311 [58.5%]; P = .03), and length of stay was shorter (control: 30 hours [interquartile range {IQR}, 6–122] vs diversion: 22 [IQR, 5–97]; P = .02).Conclusions: Use of lithium heparin tubes for diversion prior to obtaining blood cultures led to a 60% decrease in contamination. This technique is easy and inexpensive and might decrease overall hospital length of stay.Clinical Trials Registration: NCT03966534.

BACKGROUND: Tracheostomy in patients who are critically ill is generally performed due to prolonged mechanical ventilation and expected extubation failure. However, tracheostomy criteria and ideal timing are poorly defined, including equivocal data from randomized controlled trials and median intubation to tracheostomy times that range from 7–21 d. However, a consistent finding is that only ∼50% of late tracheostomy groups actually undergo tracheostomy, with non-performance due to recovery or clinical deterioration. Unlike in many jurisdictions, elective surgical procedures in our institution require a court-appointed guardian, which necessitates an approximately 1-week delay between the decision to perform tracheostomy and surgery. This offers a unique opportunity to observe patients with potential tracheostomy during a delay between the decision and the performance. METHODS: ICU patients who were ventilated were identified for inclusion retrospectively by an application for guardianship relating to tracheostomy, the intention-to-treat point. The main outcomes of tracheostomy, extubation, or death/palliative care after inclusion were noted. Demographics, outcomes, and event timing were compared for the 3 outcome groups. RESULTS: Tracheostomy-related guardianship requests were made for 388 subjects. Of these, 195 (50%) underwent tracheostomy, whereas 127 (33%) were extubated and 66 (17%) either died before tracheostomy (46 [12%]) or were transitioned to palliative care (20 [5%]). The median time (interquartile range) from guardianship request until a defining event was the following: 6.2 (4.0–11) d for tracheostomy, 5.0 (2.9–8.2) d for extubation ( P < .001 as compared to tracheostomy group), and 6.5 (2.5–11) d for death/palliative care ( P = .55 as compared to tracheostomy). Neurological admissions were more common in the tracheostomy group and less common in the palliative group. Other admission demographics and hospitalization characteristics were similar. Hospital mortality was higher for subjects undergoing tracheostomy (58/195 [30%]) versus extubation (24/127 [19%]) ( P = .03). CONCLUSIONS: Delay in performing tracheostomy due to legal requirements was associated with a 50% decrease in the need for tracheostomy. This suggests that decision-making with regard to ideal tracheostomy timing could be improved, saving unnecessary procedures.

Objectives: A significant proportion of COVID-19 patients may have cardiac involvement including arrhythmias. Although arrhythmia characterization and possible predictors were previously reported, there are conflicting data regarding the exact prevalence of arrhythmias. Clinically applicable algorithms to classify COVID patients’ arrhythmic risk are still lacking, and are the aim of our study. Methods: We describe a single center cohort of hospitalized patients with a positive nasopharyngeal swab for COVID-19 during the initial Israeli outbreak between 1/2/2020 –30/5/2020. The study’s outcome was any documented arrhythmia during hospitalization, based on daily physical examination, routine ECG’s, periodic 24-hour Holter, and continuous monitoring. Multivariate analysis was used to find predictors for new arrhythmias and create classification trees for discriminating patients with high and low arrhythmic risk. Results: Out of 390 COVID-19 patients included, 28 (7.2%) had documented arrhythmias during hospitalization, including: 23 atrial tachyarrhythmias, combined atrial fibrillation (AF) and ventricular fibrillation, ventricular tachycardia storm, and 3 bradyarrhythmias. Only 7/28 patients had previous arrhythmias. Our study showed significant correlation between disease severity and arrhythmia prevalence (p<0.001) with a low arrhythmic prevalence among mild disease patients (2%). Multivariate analysis revealed background heart failure (CHF) and disease severity are independently associated with overall arrhythmia while age, CHF, disease severity, and arrhythmic symptoms are associated with tachyarrhythmias. A novel decision tree using age, disease severity, CHF, and troponin levels was created to stratify patients into high and low risk for developing arrhythmia. Conclusions: Dominant arrhythmia among COVID-19 patients is AF. Arrhythmia prevalence is dependent on age, disease severity, CHF, and troponin levels. A novel simple Classification tree, based on these parameters, can discriminate between high and low arrhythmic risk patients.