Risk Assessment of COVID-19 Cases in Emergency Departments and Clinics With the Use of Real-World Data and Artificial Intelligence: Observational Study.

BACKGROUND: The recent COVID-19 pandemic has highlighted the weaknesses of health care systems around the world. In the effort to improve the monitoring of cases admitted to emergency departments, it has become increasingly necessary to adopt new innovative technological solutions in clinical practice. Currently, the continuous monitoring of vital signs is only performed in patients admitted to the intensive care unit. OBJECTIVE: The study aimed to develop a smart system that will dynamically prioritize patients through the continuous monitoring of vital signs using a wearable biosensor device and recording of meaningful clinical records and estimate the likelihood of deterioration of each case using artificial intelligence models. METHODS: The data for the study were collected from the emergency department and COVID-19 inpatient unit of the Hippokration General Hospital of Thessaloniki. The study was carried out in the framework of the COVID-X H2020 project, which was funded by the European Union. For the training of the neural network, data collection was performed from COVID-19 cases hospitalized in the respective unit. A wearable biosensor device was placed on the wrist of each patient, which recorded the primary characteristics of the visual signal related to breathing assessment. RESULTS: A total of 157 adult patients diagnosed with COVID-19 were recruited. Lasso penalty function was used for selecting 18 out of 48 predictors and 2 random forest-based models were implemented for comparison. The high overall performance was maintained, if not improved, by feature selection, with random forest achieving accuracies of 80.9% and 82.1% when trained using all predictors and a subset of them, respectively. Preliminary results, although affected by pandemic limitations and restrictions, were promising regarding breathing pattern recognition. CONCLUSIONS: This study represents a novel approach that involves the use of machine learning methods and Edge artificial intelligence to assist the prioritization and continuous monitoring procedures of patients with COVID-19 in health departments. Although initial results appear to be promising, further studies are required to examine its actual effectiveness.

Renin-Angiotensin System Inhibitors and COVID-19: a Systematic Review and Meta-Analysis. Evidence for Significant Geographical Disparities.

PURPOSE OF REVIEW: While the COVID-19 pandemic is constantly evolving, it remains unclear whether the use of angiotensin-converting enzyme (ACE) inhibitors or angiotensin receptor blockers (ARBs) affects the clinical course of SARS-CoV-2 infection. For this meta-analysis, PubMed, CENTRAL, and grey literature were searched from their inception to 19 May 2020 for randomized, controlled trials or observational studies that evaluate the association between the use of either ACE inhibitors or ARBs and the risk for major clinical endpoints (infection, hospitalization, admission to ICU, death) in adult patients during the COVID-19 pandemic. In addition, a subgroup geographical analysis of outcomes was performed. Studies including less than 100 subjects were excluded from our analysis. RECENT FINDINGS: In total, 25 observational studies were included. ACE inhibitors and ARBs were not associated with increased odds for SARS-CoV-2 infection, admission to hospital, severe or critical illness, admission to ICU, and SARS-CoV-2-related death. In Asian countries, the use of ACE inhibitors/ARBs decreased the odds for severe or critical illness and death (OR = 0.37, 95% CI 0.16-0.89, I2 = 83%, and OR = 0.62, 95% CI 0.39-0.99, I2 = 0%, respectively), whereas they increased the odds for ICU admission in North America and death in Europe (OR = 1.75, 95% CI 1.37-2.23, I2 = 0%, and OR = 1.68, 95% CI 1.05-2.70, I2 = 82%, respectively). ACE inhibitors might be marginally protective regarding SARS-CoV-2-related death compared with ARBs (OR = 0.86, 95% CI 0.74-1.00, I2 = 0%). Randomized controlled trials are needed to confirm the aforementioned associations between ACE inhibitors, ARBs, and SARS-CoV-2.