Dynamic Individualized Prediction of Binary Clinical Outcomes Using Bayesian Generalized Linear Mixed Models With Application in Scleroderma.

backgroundAccurate and timely prediction of critical events from longitudinal electronic health record (EHR) data is essential for precision medicine, particularly in complex chronic diseases with heterogeneous and evolving patient trajectories. Despite rapid growth in the number of statistical and machine learning methods, prediction remains challenging, especially for binary outcomes. Many valid approaches either lack transparency, ignore temporal dependence, or are difficult to dynamically update with new data, limiting clinical applicability. Existing dynamic prediction methods are designed primarily for continuous outcomes and do not directly extend to binary settings, which are common in clinical practice.

methodsWe use a Bayesian generalized linear mixed model approach to develop a cross-validated sequential prediction (CVSP) algorithm for estimating the conditional probability of a binary outcome given all available historical data. The prediction model integrates population-level fixed effects, lagged outcomes to capture shorter-term dependence, and patient-specific random intercepts to account for longer-term dependence due to unobserved heterogeneity. Predictions are cross-validated and generated sequentially at each visit with Monte Carlo integration, without refitting the model as new data accrue. Variable selection via bootstrap LASSO is incorporated to improve model parsimony and prediction validity.

resultsThe framework is motivated by and applied to systemic sclerosis (SSc) patients with longitudinal assessments of proximal muscle weakness. The proposed method demonstrates superior discriminative performance (cross-validated AUC of 0.86) compared to standard regression and machine learning approaches, while also maintaining good calibration. The CVSP approach allows efficient individualized predictions that incorporate a patient's entire medical history. Variable selection reduces the number of predictors while preserving accuracy.

conclusionThis Bayesian CVSP framework provides a generalizable approach for individualized, visit-by-visit prediction of binary outcomes from longitudinal EHR data. By dynamically updating risk estimates without repeated refitting, it supports real-time clinical decision-making and advances precision medicine for chronic disease management.