Cardiovascular risk assessment in patients with rheumatoid arthritis using carotid ultrasound B-mode imaging.
Ankush D JamthikarDeep GuptaAnudeep PuvvulaAmer M JohriNarendra N KhannaLuca SabaSophie I MavrogeniJohn R LairdGyan PareekMartin MinerPetros P SfikakisAthanasios D ProtogerouGeorge D KitasRaghu KolluriAditya M SharmaVijay ViswanthanVijay S RathoreJasjit S SuriPublished in: Rheumatology international (2020)
Rheumatoid arthritis (RA) is a systemic chronic inflammatory disease that affects synovial joints and has various extra-articular manifestations, including atherosclerotic cardiovascular disease (CVD). Patients with RA experience a higher risk of CVD, leading to increased morbidity and mortality. Inflammation is a common phenomenon in RA and CVD. The pathophysiological association between these diseases is still not clear, and, thus, the risk assessment and detection of CVD in such patients is of clinical importance. Recently, artificial intelligence (AI) has gained prominence in advancing healthcare and, therefore, may further help to investigate the RA-CVD association. There are three aims of this review: (1) to summarize the three pathophysiological pathways that link RA to CVD; (2) to identify several traditional and carotid ultrasound image-based CVD risk calculators useful for RA patients, and (3) to understand the role of artificial intelligence in CVD risk assessment in RA patients. Our search strategy involves extensively searches in PubMed and Web of Science databases using search terms associated with CVD risk assessment in RA patients. A total of 120 peer-reviewed articles were screened for this review. We conclude that (a) two of the three pathways directly affect the atherosclerotic process, leading to heart injury, (b) carotid ultrasound image-based calculators have shown superior performance compared with conventional calculators, and (c) AI-based technologies in CVD risk assessment in RA patients are aggressively being adapted for routine practice of RA patients.
Keyphrases
- rheumatoid arthritis
- end stage renal disease
- risk assessment
- artificial intelligence
- healthcare
- cardiovascular disease
- ejection fraction
- newly diagnosed
- chronic kidney disease
- peritoneal dialysis
- magnetic resonance imaging
- primary care
- deep learning
- type diabetes
- disease activity
- ankylosing spondylitis
- heavy metals
- heart failure
- interstitial lung disease
- computed tomography
- oxidative stress
- climate change
- coronary artery disease
- ultrasound guided
- systemic sclerosis
- high resolution
- cardiovascular events
- social media
- quantum dots
- fluorescence imaging