Pulmonary arterial hypertension (PAH) is a highly morbid disease that commonly complicates patients with the
autoimmune disease systemic sclerosis (SSc) and is a leading cause of death in this population. Right
ventricular (RV) adaptation to progressive increases in afterload is the main determinant of outcome in SSc
and despite guideline-recommended early detection algorithms designed to identify PAH and therapeutic
advances in the treatment of PAH, SSc patients are often diagnosed late, and mortality remains exceedingly
high. Although several strategies are available, existing screening algorithms have low predictive accuracy.
Thus, an unmet need to better delineate high-risk phenotypes in SSc and improve identification of subgroups
at greatest risk of PAH earlier in the disease course when therapeutic interventions may affect prognosis. This
research aims to fill this gap by providing noninvasive and objective prognostic quantitative imaging markers
and trajectory-based subtype analysis using sophisticated biostatistical and machine-learning (ML) techniques,
enabling early identification of subpopulations at risk for adverse, long-term outcomes. Utilizing novel
echocardiographic techniques, we recently identified early changes in RV contractility and contractile reserve
in SSc prior to the development of overt PAH. Drawing from these findings, in Aim 1, we will specifically
determine whether the addition of echo-derived parameters of RV contractility to standard screening can
identify distinct clinical phenotypes and high-risk trajectories in the development of PAH, while characterizing
the nature and interaction of these trajectories across each of the variables. Our methodology will combine ML
with Bayesian multivariate linear mixed modeling to improve characterization and phenotyping of similar
subgroups and how trajectories present unique risk for adverse events. Aim 2 focuses on the biologic
validation of echo-derived techniques with simultaneous direct chamber-level measures of RV contractile
reserve and RV-arterial coupling to determine whether RVLSS is a noninvasive surrogate for RV contractility
and RV contractile reserve. Our synergistic and complementary aims will be used to derive and validate a
robust early detection strategy in Aim 3, improving upon existing screening methods, enabling the early
prediction of PAH in SSc. We are uniquely positioned to study these critical questions given our access to one
of the largest and finely phenotyped SSc cohorts in the world, our strong track record of excellence in the
noninvasive and invasive assessment of RV function in SSc, and our expertise in the complex multifaceted
methodology necessary to complete this project. Early identification and characterization of RV maladaptation
to emerging pulmonary vascular disease would be transformative in the clinical management of SSc, a
population with exceedingly high morbidity and mortality from cardiopulmonary disease. If successful, our
findings may also be applicable to other cohorts who are at-risk for the development of PAH to allow for earlier
detection and earlier intervention.
