Non-Invasive Assessment of Micro- and Macrovascular Function after Initiation of JAK Inhibitors in Patients with Rheumatoid Arthritis.

BACKGROUND: Janus kinase (JAK) inhibitors constitute a novel class of oral biologic disease-modifying antirheumatic drugs for patients with rheumatoid arthritis (RA). However, their use has been associated with increased risk of major cardiovascular events. We investigated whether treatment with JAK inhibitors exerts significant alterations in the micro- and microvasculature in RA patients. METHODS: Thirteen patients with RA initiating treatment with JAK inhibitors were prospectively studied. Eventually, data from 11 patients who completed the study were analyzed. Procedures were performed at baseline and 3 months after treatment. Nailfold videocapillaroscopy was applied to detect alterations of the dermal capillary network. Participants underwent 24 h ambulatory blood pressure monitoring (Mobil-O-Graph device) for the assessment of blood pressure (both brachial and aortic) and markers of large artery stiffening [pulse wave velocity (PWV), augmentation index] throughout the whole 24 h and the respective day- and nighttime periods. Carotid intima-media thickness was assessed with ultrasound. RESULTS: Three-month treatment with JAK inhibitors was not associated with any differences in brachial and aortic blood pressure, arterial stiffness, and carotid atherosclerosis, with the only exception of nighttime PWV, which was significantly elevated at follow-up. However, three-month treatment with JAK inhibitors induced significant microvascular alterations and increased the total number of capillaroscopic abnormalities. CONCLUSIONS: Three-month treatment with JAK inhibitors may exert significant effects on microcirculation as assessed with nailfold videocapillaroscopy, whereas macrovascular structure and function appears largely unaffected. Further research toward this direction may add substantial information to the available literature regarding cardiovascular aspects of JAK inhibitors in RA.

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.

Early Vascular Aging Risk Assessment From Ambulatory Blood Pressure Monitoring: The Early Vascular Aging Ambulatory Score.

BACKGROUND: This study compared the diagnostic accuracy of blood pressure (BP) measurement methods, office BP, ambulatory BP monitoring (ABPM), and home BP, in the identification of early vascular aging (EVA) and developed a score to predict the risk of EVA in hypertensive patients. METHODS: Two-hundred eighty-two consecutive subjects (39.7% male) aged 56.8  ±â€Š 15.8 years were included. Office and out-of-office BP measurements including ABPM on a usual working day and 7 days home BP monitoring were performed. Carotid-femoral pulse wave velocity (c-f PWV) was measured in all patients. EVA was defined as c-f PWV values higher than the expected for age average values according to European population data. RESULTS: In univariate analysis, EVA was significantly correlated with office systolic BP, average 24-hour systolic and diastolic BP, and average 24-hour and office heart rates. The area under the curve for predicting EVA was 0.624 (95% CI 0.551 to 0.697), 0.559 (95% CI 0.484 to 0.635) and 0.565 (95% CI 0.49 to 0.641), for daytime, home, and office systolic BP, respectively. Ambulatory BP variables, age, sex, body mass index, diabetes mellitus (yes/no), and estimated glomerular filtration rate were used to develop a new score for EVA providing a total accuracy of 0.82, 0.84 sensitivity, and 0.78 specificity. CONCLUSIONS: In conclusion, the new risk score, Early Vascular Aging Ambulatory score, may accurately identify hypertensive patients with EVA using ABPM values and classic cardiovascular risk factors.