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Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma.

Ji Soo Kim1, Ami A Shah2, Laura K Hummers2

  • 1Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA. jkim478@jhu.edu.

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|November 14, 2021
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Summary
This summary is machine-generated.

This study introduces a new Bayesian model to predict life-threatening events like interstitial lung disease in scleroderma patients. The method offers improved accuracy and real-time risk assessment within electronic health records.

Keywords:
Bayesian hierarchical modelsLongitudinal profilesMultivariate mixed modelsSclerodermaSequentially-updated prediction

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Area of Science:

  • Rheumatology and Immunology
  • Biostatistics and Bioinformatics
  • Computational Medicine

Background:

  • Scleroderma is a chronic autoimmune disease affecting multiple organ systems.
  • Predicting critical events such as interstitial lung disease (ILD), cardiomyopathy, and pulmonary hypertension (PH) is crucial for patient management.
  • Accurate risk prediction requires utilizing comprehensive clinical history from individual patients and a reference population.

Purpose of the Study:

  • To develop and validate a statistical approach for precise prediction of key clinical events in scleroderma patients.
  • To create a real-time, personalized risk estimation tool for multiple scleroderma complications.

Main Methods:

  • A Bayesian mixed model was employed to simultaneously characterize individual biomarker trajectories.
  • The model was estimated using data from the Johns Hopkins Scleroderma Center Research Registry.
  • A cross-validated, sequential prediction (CVSP) algorithm was developed for clinical risk prediction.

Main Results:

  • The Bayesian approach demonstrated superior performance compared to standard methods.
  • The CVSP algorithm provides sequentially updated predictions as new data become available.
  • Updated predictions for longitudinal trajectories and specific complications (ILD, cardiomyopathy, PH) are generated efficiently.

Conclusions:

  • The developed method enables real-time, personalized risk estimates for multiple scleroderma complications.
  • This approach has been implemented in an electronic health record system for clinical testing.
  • This represents a novel method for computing personalized risk estimates for scleroderma complications.