Probabilistic Clustering Using Multivariate Growth Mixture Model in Clinical Settings-A Scleroderma Example.

backgroundScleroderma (systemic sclerosis; SSc) is a chronic autoimmune disease known for wide heterogeneity in patients' disease progression in multiple organ systems. Our goal is to guide clinical care by real-time classification of patients into clinically interpretable subpopulations based on their baseline characteristics and the temporal patterns of their disease progression.

methodsA Bayesian multivariate growth mixture model was fit to identify subgroups of patients from the Johns Hopkins Scleroderma Center Research Registry who share similar lung function trajectories. We jointly modeled forced vital capacity (FVC) and diffusing capacity for carbon monoxide (DLCO) as pulmonary outcomes for 289 patients with SSc and anti-topoisomerase 1 antibodies and developed a framework to sequentially update class membership probabilities for any given patient based on her accumulating data.

resultsWe identified a "stable" group of 150 patients for whom both biomarkers changed little from the date of disease onset over the next 10 years, and a "progressor" group of 139 patients that, on average, experienced a clinically significant decline in both measures starting soon after disease onset. For any given patient at any given time, our algorithm calculates the probability of belonging to the progressor group using both baseline characteristics and the patient's longitudinal FVC and DLCO observations.

conclusionsOur method calculates the probability of being a fast progressor at baseline when no FVC and DLCO are observed, then sequentially updates it as more information becomes available. This sequential integration of patient data and classification of her disease trajectory has the potential to improve clinical decisions and ultimately patient outcomes.