Back

Retention and loss to follow-up among patients with hypertension in primary care: a multi-practice cohort study

Ye, J.; Song, A.

2026-05-13 cardiovascular medicine
10.64898/2026.05.10.26352856 medRxiv
Show abstract

Effective hypertension management depends on sustained engagement with primary care, and there is a need to understand the magnitude and determinants of follow-up loss in real-world primary care. We analyzed electronic health record (EHR) data from 26,541 patients with hypertension across primary care practices participating in the EvidenceNOW quality-improvement initiative. We characterized retention in care, longitudinal blood pressure (BP) control, and predictors of loss to follow-up using descriptive statistics, cumulative retention curves, and multivariable Cox proportional-hazards regression. At baseline, mean systolic and diastolic BP were 140.0 {+/-} 20.6 and 84.7 {+/-} 13.0 mmHg, respectively; only 10.7% (95% CI 10.4-11.1) of patients had controlled BP and 18.1% never returned for any follow-up visit. Among the 21,729 patients who had [≥]1 follow-up encounter, retention declined steeply over time--from 59.9% at 6 months to 16.3% at 36 months. Patients identifying as Black/African American (adjusted hazard ratio [aHR] 1.44; 95% CI 1.33-1.56), Hispanic/Latino (aHR 1.43; 1.35-1.52), or Other race/ethnicity (aHR 1.50; 1.41-1.59) had significantly higher hazards of being lost to follow-up than White patients, whereas older age, female sex, comorbid diabetes, heart failure, chronic kidney disease, stroke, and baseline BP control were each independently protective. Among patients retained for at least 12 months, BP control rose to 63.7% and remained near 64-66% through 36 months. These findings reveal a substantial and inequitable longitudinal care-engagement gap that is likely a principal driver of suboptimal hypertension control in the United States and identify actionable demographic and clinical targets for primary-care retention interventions.

Matching journals

The top 4 journals account for 50% of the predicted probability mass.

50% of probability mass above

"Similar papers" are the closest papers from that journal in the model's embedding space. They show what the match is built on, but the ranking comes mostly from a classifier over the whole training set, not from these examples alone.