ArticleArthritis care & research2026
Development of a Personalized Visualization and Analysis Tool to Improve Clinical Care in Complex Multisystem Diseases With Application to Scleroderma.
Article in Arthritis care & research, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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Who cites it
2 citing papers in PubMed.
- Unlocking multi-institutional insights into disease progression with PEAL as a lossless, one-shot federated learning solution.NPJ digital medicine · 2026Article
- Liver stiffness measurement-based risk score for predicting liver decompensation risk: a single-center retrospective Chinese study.Clinical and experimental medicine · 2025Article
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Authors and funding
19 authors.
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Abstract
objectiveIn complex diseases, it is challenging to assess a patient's disease state, trajectory, treatment exposures, and risk of multiple outcomes simultaneously, efficiently, and at the point of care.
methodsWe developed an interactive patient-level data visualization and analysis tool (VAT) that automates illustration of the trajectory of a patient with scleroderma across multiple organs and illustrates this relative to a reference population, including patient subgroups who share risk factors with the index patient, to improve estimation of disease state. We conducted VAT usability testing with patients and clinicians. We then embedded results from internally cross-validated, Bayesian multivariate mixed models that calculate an individual's risk of critical events, using baseline risk factors, patient-level information in past trajectories in multiple dimensions, and known outcomes from the entire population and relevant subgroups.
resultsThe web-based application aggregates complex, longitudinal data to illustrate patient-, subgroup-, and population-level health trajectories across multiple organ systems. Patients (n = 7) exposed to the VAT reported increased knowledge about their disease and confidence in medical decision-making. Rheumatologists (n = 4) were able to access 8.6 times more data in 81.5% of the time using two-thirds fewer clicks using the VAT compared with an electronic medical record system. Statistical modeling was successfully embedded in the VAT, enabling real-time estimation of a patient's risks of multiple complications.
conclusionSystematic analysis and visualization of individual- and population-level data in a complex disease has potential to improve medical decision-making and warrants further study. Individualized risk estimation disseminated at the point of care may enable targeted screening and early intervention in high-risk patients.
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