Abstract
While severe vasculopathic manifestations of systemic sclerosis (SSc) are well-recognized, characterization of subclinical progressive vasculopathy contributing to cardiac involvement remains an unmet clinical need. This review highlights the evolving understanding of SSc heart involvement (SHI), including current standard clinical cardiac evaluation methods, prevalence of various cardiac manifestations of SHI, and advances at the forefront of precision medicine. Informed by this growing body of literature, we describe the development of a novel interdisciplinary cardio-rheumatology clinic at the Vanderbilt University Medical Center. Utilizing advances in imaging techniques and systemic retrieval and analysis of complex data sets, our dedicated cardio-rheumatology clinic offers opportunities for therapeutic advances and personalized medicine through mechanistic disease phenotyping in SSc. Nailfold capillaroscopy, thermography, and hand ultrasound with Doppler are acquired to characterize small vessel vasculopathy, while echocardiogram, ambulatory cardiac rhythm monitoring, cardiac magnetic resonance imaging, and cardiac positron emission tomography/computed tomography are utilized to characterize cardiac disease. By correlating vasculopathy imaging with cardiac manifestations, our cardio-rheumatology clinic aims to identify patients with SSc who would benefit from additional cardiac investigation even in the absence of cardiac symptomatology. This interdisciplinary collaboration may allow earlier detection of primary SHI, which is a common cause of death in SSc patients, resulting from both morpho-functional and electrical cardiac abnormalities. Our shared model of care and robust data acquisition facilitate clinical investigation by utilizing technological advances in data management. Using deep learning and pattern recognition, artificial intelligence (AI) offers opportunities to integrate data from imaging and monitoring techniques outlined in this report to provide quantifiable markers of disease progression and treatment efficacy. Given the potential for extensive AI data processing but the low prevalence of SSc, developing a multicenter cloud-based image sharing platform would accelerate clinical investigation in the field. Ultimately, we aim to tailor therapeutic decisions and risk mitigation strategies to improve SSc patient outcomes.