Ami Shah, MD, MHS
Scott Zeger, PhD
Johns Hopkins University School of Medicine
Scleroderma presents in many different forms and can affect multiple organ systems. There is a critical need to identify patients who are likely to progress from their disease at an early stage, as these patients may benefit from earlier, targeted interventions. Within the broad spectrum of scleroderma, there are subgroups of patients whose disease behaves similarly, likely because their disease shares an underlying mechanism or mechanisms.
By integrating longitudinal clinical data on the many organ systems affected by scleroderma, paying particular attention to key clinical events that have a major impact on the patient’s quality of life, the Shah/Zeger group is working to improve their estimation of how a patient’s disease will likely progress.
The group has developed and is currently testing a computer visualization tool that can be used in the clinic to help patients better understand their disease. The tool illustrates a patient’s baseline characteristics, cumulative disease manifestations, autoantibody status, and longitudinal trajectory across multiple organ systems.
The tool also allows an individual patient’s data to be shown relative to other patients who share specific subgroup characteristics. The researchers are testing whether this tool aids the patient’s understanding of their disease and whether it changes clinical decision-making.
The group is also developing computed personalized risk estimates for SSc complications across multiple organ systems. To date, their work has focused on ILD, cardiomyopathy, and pulmonary hypertension (as defined by trajectories from pulmonary function tests and echocardiogram measures) and they are working to refine their ability to estimate trajectory for these complications by incorporating additional data, including additional clinical measures and patient-reported outcomes. They are also expanding the range of SSc complications under study.
Finally, the group will examine the use of subgroups to predict patients’ likely benefits from specific drugs and treatment regimens, with the goal of improving clinical decision-making.