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.

Assessment of skin microcirculation in primary aldosteronism: impaired microvascular responses compared to essential hypertensives and normotensives.

Primary aldosteronism (PA) is associated with considerably higher cardiovascular risk and increased prevalence of organ damage compared to essential hypertension (EH). Laser speckle contrast imaging (LSCI) has emerged as a novel non-invasive tool to assess of skin microcirculation. Our aim was to evaluate skin microvascular function (SMF) using LSCI coupled with post-occlusive reactive hyperemia (PORH) in a group of PA patients (PAs) compared to patients with EH (EHs) and normotensive controls (NTs). We enrolled PAs, age- and gender-matched with EHs and NTs. All participants underwent SMF assessment by LSCI with PORH. We enrolled 109 participants including 29 PAs, 47 EHs, and 33 NTs. SMF was significantly impaired in PAs, including peak time (p < 0.001) and base to peak flux (p < 0.001) compared to NTs and EHs. Among PAs, plasma aldosterone showed a positive correlation with occlusion flux (p = 0.005). Our study shows for the first time that PAs present impaired SMF as assessed with LSCI coupled with PORH, not only compared to NTs but also compared to EHs with similar blood pressure profile. Further studies are needed to investigate the clinical impact of such alterations in terms of pathophysiology and cardiovascular risk prediction.