Cardiovascular Risk Prediction Parameters for Better Management in Rheumatic Diseases

Abstract

The early detection of cardiovascular disease (CVD) serves as a key element in preventive cardiology. The risk of developing CVD in patients with rheumatic disease is higher than that of the general population. Thus, the objective of this narrative review was to assess and describe updated risk-prediction parameters for CVD in patients suffering from rheumatic diseases, and, additionally, to evaluate therapeutic and risk management possibilities. The processes of recognizing CVD risk factors in rheumatic diseases, establishing diagnoses, and discovering CV risk assessments are currently displeasing in clinical practice; they have a limited clinical impact. A large number of references were found while screening PUBMED, Scopus, and Google scholar databases; the 47 most relevant references were utilized to build up this study. The selection was limited to English language full text articles, RCTs, and reviews published between 2011 and 2021. Multiple imaging techniques, such as ECG, ultrasound, and cIMT, as well as biomarkers like osteoprotegerin cytokine receptor and angiopoietin-2, can be beneficial in both CV risk prediction and in early subclinical diagnosis. Physical exercise is an essential non-pharmacological intervention that can maintain the health of the cardiovascular system and, additionally, influence the underlying disease. Lipid-lowering drugs (methotrexate from the non-biologic DMARDs family as well as biologic DMARDs such as anti-TNF) were all associated with a lower CV risk; however, anti-TNF medication can decrease cardiac compliance and promote heart failure in patients with previously diagnosed chronic HF. Although they achieved success rates in reducing inflammation, glucocorticoids, NSAIDs, and COX-2 inhibitors were correlated with an increased risk of CVD. When taking all of the aforementioned points into consideration, there appears to be a dire need to establish and implement CVD risk stratification models in rheumatic patients.