Identifying Clusters Of Patients With Diabetes Using a Markov Birth-Death Process

Estimating disease trajectories has increasingly become more essential to clinical practitioners to administer effective treatment to their patients. A part of describing disease trajectories involves taking patients’ medical histories and sociodemographic factors into account and grouping them into similar groups, or clusters. Advances in computerised patient databases have paved a way for identifying such trajectories in patients by recording a patient’s medical history over a long period of time (longitudinal data): we studied data from the PREDICT-CVD dataset, a national primary-care cohort from which people with diabetes from 2002-2015 were identified through routine clinical practice. We fitted a Bayesian hierarchical linear model with latent clusters to the repeated measurements of HbA1c and eGFR, using the Markov birth-death process proposed by Stephens (2000) to handle the changes in dimensionality as clusters were added or removed.