The Use of Rapid COVID-19 Antigen Test in the Emergency Department as a Decision-Support Tool.

The emergency department (ED) is the initial point of contact between hospital staff and patients potentially infected with SARS-CoV-2, thus, prevention of inadvertent exposure to other patients is a top priority. We aimed to assess whether the introduction of antigen-detecting rapid diagnostic tests (Ag-RDTs) to the ED affected the likelihood of unwanted SARS-CoV-2 exposures. In this retrospective single-center study, we compared the rate of unwarranted exposure of uninfected adult ED patients to SARS-CoV-2 during two separate research periods; one before Ag-RDTs were introduced, and one with Ag-RDT used as a decision-support tool. The introduction of Ag-RDTs to the ED significantly decreased the relative risk of SARS-CoV-2-negative patients being incorrectly assigned to the COVID-19 designated site ("red ED"), by 97%. There was no increase in the risk of SARS-CoV-2-positive patients incorrectly assigned to the COVID-19-free site ("green ED"). In addition, duration of ED admission was reduced in both the red and the green ED. Therefore, implementing the Ag-RDT-based triage protocol proved beneficial in preventing potential COVID-19 nosocomial transmission.

Utilization of machine-learning models to accurately predict the risk for critical COVID-19.

Among patients with Coronavirus disease (COVID-19), the ability to identify patients at risk for deterioration during their hospital stay is essential for effective patient allocation and management. To predict patient risk for critical COVID-19 based on status at admission using machine-learning models. Retrospective study based on a database of tertiary medical center with designated departments for patients with COVID-19. Patients with severe COVID-19 at admission, based on low oxygen saturation, low partial arterial oxygen pressure, were excluded. The primary outcome was risk for critical disease, defined as mechanical ventilation, multi-organ failure, admission to the ICU, and/or death. Three different machine-learning models were used to predict patient deterioration and compared to currently suggested predictors and to the APACHEII risk-prediction score. Among 6995 patients evaluated, 162 were hospitalized with non-severe COVID-19, of them, 25 (15.4%) patients deteriorated to critical COVID-19. Machine-learning models outperformed the all other parameters, including the APACHE II score (ROC AUC of 0.92 vs. 0.79, respectively), reaching 88.0% sensitivity, 92.7% specificity and 92.0% accuracy in predicting critical COVID-19. The most contributory variables to the models were APACHE II score, white blood cell count, time from symptoms to admission, oxygen saturation and blood lymphocytes count. Machine-learning models demonstrated high efficacy in predicting critical COVID-19 compared to the most efficacious tools available. Hence, artificial intelligence may be applied for accurate risk prediction of patients with COVID-19, to optimize patients triage and in-hospital allocation, better prioritization of medical resources and improved overall management of the COVID-19 pandemic.

Off-patient assessment of pre-cordial impact mechanics among medical professionals in North-East Italy involved in emergency cardiac resuscitation.

Pre-cordial thump (PT) relies on cardiac mechano-electric transduction to transform mechanically-delivered energy into an electrophysiologically relevant stimulus. Its use for emergency resuscitation has declined recent years, amidst concerns about effectiveness and side-effects. In addition, there is insufficient knowledge about bio-mechanical properties and mechanisms of PT. Using a PT-mechanics recorder, we measured PT off-patient among healthcare professionals (n = 58) in North-East Italy, and related this to retrospective information on self-reported PT outcomes. Impact-speed and peak-force were 4.7 ± 1.3 m s⁻¹ (2.2-7.8 m s⁻¹) and 394 ± 110 N (202-648 N), respectively. Average self-reported cardioversion rate by PT was 35%. No adverse events were stated. All but 3 of PT providers with self-reported cardioversion rates ≥50% had pre-impact fist-speeds of ≥3.7 m s⁻¹. In comparison with previously-reported data from UK and US (n = 22 each), self-reported success-rates and pre-impact fist-speeds were more similar to US (PT-induced cardioversion rate 27.7%; fist-speed 4.17 ± 1.68 m s⁻¹) than to UK participants (PT-induced cardioversion rate 13.3%; fist-speed 1.55 ± 0.68 m s⁻¹). Small cohort-size, retrospective nature of data-gathering, and 'self-selection bias' (participants who have used PT on patients) limits the extent to which firm conclusions can be drawn. Observations are compatible, though, with the possibility that pre-impact fist-speed may affect success-rate of PT. Thus, where PT is used for acute resuscitation, it is delivered because it is immediately 'at hand'. Negative side effects are rare or absent in witnessed cardiac arrest cases. Pre-impact fist-speed may be a determinant of outcome, and this could be trained using devices suitable for self-assessment.

Soft tissue impact characterisation kit (STICK) for ex situ investigation of heart rhythm responses to acute mechanical stimulation.

Both mechanical induction and mechanical termination of arrhythmias have been reported in man. Examples include pre-cordial impacts by sports implements (baseballs, pucks) that can trigger arrhythmias, including ventricular fibrillation, or via the so-called pre-cordial thump, used as an emergency resuscitation measure to convert arrhythmias to normal sinus node rhythm. These interventions have been partially reproduced in experimental studies on whole animals. Relating observations at the system's level to underlying mechanisms has been difficult, however, largely because of: (i) a deficit in efficient and affordable pharmacological agents to selectively target (sub-)cellular responses in whole animal studies, and (ii) the lack of suitable experimental models to study the above responses at intermediate levels of functional and structural integration, such as the isolated heart or cardiac tissue. This paper presents a soft tissue impact characterisation kit (STICK), suitable for quantitative investigations into the effects of acute mechanical stimulation on cardiac electro-mechanical function in rodent isolated heart or tissue preparations. The STICK offers independent control over a range of relevant biophysical parameters, such as impact location and energy, pre-impact projectile speed and contact area, as well as over the timing of a mechanical stimulus relative to the cardiac cycle (monitored via electrocardiogram, ECG, here recorded directly from the cardiac surface). Projectile deceleration upon interaction with the tissue is monitored, contact-free, with a resolution of 175 microm, providing information on tissue deformation dynamics, force, pressure and work of the mechanical intervention. In order to study functional effects of cardiac mechanical stimulation in the absence of tissue damage, impacts must be limited (for juvenile Guinea pig heart) to 2-2.5 mJ in the slack left ventricle (diastolic impact) and 5-10 mJ in contracture (systolic impact), as confirmed by enzyme assay and histological investigation. Impacts, timed to coincide with the early T-wave of the ECG, are capable of triggering short runs of ventricular fibrillation. Thus, the STICK is a suitable tool for the study of acute cardiac mechano-electric feedback effects, caused by short impulse-like mechanical stimulation, at the level of the isolated organ or tissue.