  Electronic health records (EHRs) are increasingly used for clinical and
comparative effectiveness research, but suffer from missing data. Motivated by
health services research on diabetes care, we seek to increase the quality of
EHRs by focusing on missing values of longitudinal glycosylated hemoglobin
(A1c), a key risk factor for diabetes complications and adverse events. Under
the framework of multiple imputation (MI), we propose an individualized
Bayesian latent profiling approach to capture A1c measurement trajectories
subject to missingness. The proposed method is applied to EHRs of adult
patients with diabetes in a large academic Midwestern health system between
2003 and 2013 and had Medicare A and B coverage. We combine MI inferences to
evaluate the association of A1c levels with the incidence of acute adverse
health events and examine patient heterogeneity across identified patient
profiles. We investigate different missingness mechanisms and perform
imputation diagnostics. Our approach is computationally efficient and fits
flexible models that provide useful clinical insights.
