Patients with Chronic Inflammatory Rheumatic diseases (CIRD) are at increased risk of cardiovascular disease (CVD), ascribed not only to classical risk factors, but also to the presence of chronic systemic inflammatory response. Αtherosclerosis, the cornerstone of CVD, is known to be accelerated in CIRD; rheumatoid arthritis promotes atheromatosis and associates with preclinical atherosclerosis equivalent to Diabetes Mellitus, which also seems to apply for systemic lupus erythematosus. Data on ankylosing spondylitis and psoriatic arthritis, albeit more limited, also support an increased CV risk in these patients. The association between inflammation and atherosclerosis, has been thoroughly investigated in the last three decades and the role of inflammation in the pathogenesis and progression of atherogenesis has been well established. Endothelial dysfunction, oxidative stress in vascular endothelial cells and macrophage accumulation, toll-like receptor signaling, NLPR-3 formation and subsequent pro-inflammatory cytokine production, such as TNFa, IL-1β, IL-6, and TNF-like cytokine 1A, are few of the mechanisms implicated in the atherogenic process. Moreover, there is evidence that anti-inflammatory biologic drugs, such as anti-TNF and anti-IL1β agents, can decelerate the atherogenic process, thus setting new therapeutic targets for early and effective disease control and suppression of inflammation, in addition to aggressive management of classical CV risk factors.

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Background: Statistically derived cardiovascular risk calculators (CVRC) that use conventional risk factors, generally underestimate or overestimate the risk of cardiovascular disease (CVD) or stroke events primarily due to lack of integration of plaque burden. This study investigates the role of machine learning (ML)-based CVD/stroke risk calculators (CVRC ML ) and compares against statistically derived CVRC (CVRC Stat ) based on (I) conventional factors or (II) combined conventional with plaque burden (integrated factors).Methods: The proposed study is divided into 3 parts: (I) statistical calculator: initially, the 10-year CVD/stroke risk was computed using 13 types of CVRC Stat (without and with plaque burden) and binary risk stratification of the patients was performed using the predefined thresholds and risk classes; (II) ML calculator: using the same risk factors (without and with plaque burden), as adopted in 13 different CVRC Stat , the patients were again risk-stratified using CVRC ML based on support vector machine (SVM) and finally;(III) both types of calculators were evaluated using AUC based on ROC analysis, which was computed using combination of predicted class and endpoint equivalent to CVD/stroke events.Results: An Institutional Review Board approved 202 patients (156 males and 46 females) of Japanese ethnicity were recruited for this study with a mean age of 69±11 years. The AUC for 13 different types of CVRC Stat calculators were: AECRS2.0 (AUC 0.83, P<0.001), QRISK3 (AUC 0.72, P<0.001), WHO (AUC 0.70, P<0.001), ASCVD (AUC 0.67, P<0.001), FRS cardio (AUC 0.67, P<0.01), FRS stroke (AUC 0.64, P<0.001), MSRC (AUC 0.63, P=0.03), UKPDS56 (AUC 0.63, P<0.001), NIPPON (AUC 0.63, P<0.001), PROCAM (AUC 0.59, P<0.001), RRS (AUC 0.57, P<0.001), UKPDS60 (AUC 0.53, P<0.001), and SCORE (AUC 0.45, P<0.001), while the AUC for the CVRC ML with integrated risk factors (AUC 0.88, P<0.001), a 42% increase in performance. The overall risk-stratification accuracy for the CVRC ML with integrated risk factors was 92.52% which was higher compared all the other CVRC Stat.

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Cardiovascular disease (CVD) is one of the leading causes of morbidity and mortality in the United States of America and globally. Carotid arterial plaque, a cause and also a marker of such CVD, can be detected by various non-invasive imaging modalities such as magnetic resonance imaging (MRI), computer tomography (CT), and ultrasound (US). Characterization and classification of carotid plaque-type