biostudies-literatureKim JSNational Institute of Arthritis and Musculoskeletal and Skin DiseasesSara and Alex Othon Research FundJerome L. Greene FoundationChresanthe Staurulakis Memorial FundJohns Hopkins inHealth initiativeManugian Family ScholarNIH/NIAMSScleroderma Research FoundationDonald B. and Dorothy L. Stabler FoundationNancy and Joachim Bechtle Precision Medicine Fund for Scleroderma197-207https://www.ebi.ac.uk/biostudies/studies/S-EPMC12919693biostudies-literatureUnknown78(2)<h4>Objective</h4>In 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.<h4>Methods</h4>We 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, usiArthritis care & researchDevelopment of a Personalized Visualization and Analysis Tool to Improve Clinical Care in Complex Multisystem Diseases With Application to Scleroderma.PMC12919693R01AR073208K24AR080217P30AR070254Orbai AMSmithwright RShah ABShah AAPitts SIHummers LKStewart WRosen AScott JKim JSKoher DYang YWoods AAslanbeik PZeger SLFisher LSmith LNTibbils BGurses APfalseDevelopment of a Personalized Visualization and Analysis Tool to Improve Clinical Care in Complex Multisystem Diseases With Application to Scleroderma.<h4>Objective</h4>In 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.<h4>Methods</h4>We 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, usi2026-01-01T00:00:00Z2026 Feb2026-07-16T10:06:14.69Z2026-07-09T10:48:55.22ZS-EPMC129196934065410910.1002/acr.25613