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

Research output: Contribution to journalArticlepeer-review

Abstract

Background: Scleroderma is a serious chronic autoimmune disease in which a patient’s disease state manifests in several irregularly spaced longitudinal measures of lung, heart, skin, and other organ systems. Threshold crossings of pulmonary and cardiac measures indicate potentially life-threatening key clinical events including interstitial lung disease (ILD), cardiomyopathy, and pulmonary hypertension (PH). The statistical challenge is to accurately and precisely predict these events by using all of the clinical history for the patient at hand and for a reference population of patients. Methods: We use a Bayesian mixed model approach to simultaneously characterize each individual’s future trajectories for several biomarkers. We estimate this model using a large population of patients from the Johns Hopkins Scleroderma Center Research Registry. The joint probabilities of critical lung and heart events are then calculated as a byproduct of the mixed model. Results: The performance of this approach is substantially better than standard, more common alternatives. In order to predict an individual’s risks in a clinical setting, we also develop a cross-validated, sequential prediction (CVSP) algorithm. As additional data are observed during a patient’s visit, the algorithm sequentially produces updated predictions for the future longitudinal trajectories and for ILD, cardiomyopathy, and PH. The updated prediction distributions with little additional computing, for example within an electronic health record (EHR). Conclusions: This method that generates real-time personalized risk estimates has been implemented within the electronic health record system for clinical testing. To our knowledge, this work represents the first approach to compute personalized risk estimates for multiple scleroderma complications.

Original languageEnglish (US)
Article number249
JournalBMC medical research methodology
Volume21
Issue number1
DOIs
StatePublished - Dec 2021

Keywords

  • Bayesian hierarchical models
  • Longitudinal profiles
  • Multivariate mixed models
  • Scleroderma
  • Sequentially-updated prediction

ASJC Scopus subject areas

  • Epidemiology
  • Health Informatics

Fingerprint

Dive into the research topics of 'Predicting clinical events using Bayesian multivariate linear mixed models with application to scleroderma'. Together they form a unique fingerprint.

Cite this