Latent Class Trajectory Phenotypes of Longitudinal Visit and Follow-up Patterns Among Patients with Hypertension

Objective: To identify latent phenotypes of observed electronic health record (EHR) contact among patients with hypertension, evaluate their reproducibility across temporal resolutions and sensitivity to administrative censoring, and examine associations with treatment documentation and blood pressure (BP) control after adjustment for comorbidity burden and measurement opportunity. Methods: We conducted a retrospective cohort study of patients with hypertension and at least two recorded visits (N = 26,710). Latent class trajectory modelling of repeated binary visit indicators within a Bernoulli finite mixture framework was performed using 1-month and 3-month intervals over 36 months. Models were selected using the Bayesian Information Criterion and GRoLTS-recommended metrics. Multivariable linear regression examined associations between trajectory phenotypes and treatment documentation and BP control after adjustment for age, sex, race, baseline BP, and comorbidity burden. BP outcomes were also compared at 12, 24, and 36 months. Results: Four observed EHR contact trajectory groups were identified at both temporal resolutions (1-month: relative entropy 0.821, minimum APP 0.770, minimum OCC 6.49; 3-month: relative entropy 0.771, minimum APP 0.778, minimum OCC 5.39), with excellent bootstrap reproducibility (mean ARI 0.966 - 0.969). The four-group structure was replicated in the [&ge;] 24-month subgroup, although this represented only 20.8% of the cohort. Overall, 79.2% of patients had <24 months of follow-up, with censoring concentrated in the lowest-contact groups, indicating that these trajectories reflect observed EHR contact under variable administrative observation rather than patient disengagement. After adjustment, higher-contact groups had consistently higher treatment documentation rates than the low observed follow-up group, whereas differences in BP control were small and inconsistent. The highest-contact group did not achieve the lowest BP at any fixed time point despite the largest first-to-last BP reduction, demonstrating bias from differential observation window length. Conclusion: Four internally reproducible phenotypes of observed EHR contact were identified, but their trajectories were substantially influenced by administrative censoring and lack external validation. Contact phenotype was associated with treatment documentation but only weakly with BP control after adjustment.