Predicting the Absence and Gaps in Recorded Blood Pressure Follow-up Among Patients With Hypertension

Background: Electronic health record (EHR) data can help identify patients who would benefit from blood pressure reassessment after an elevated reading. Objective: To develop and internally validate a model to predict the absence of a recorded blood pressure measurement after an elevated reading among adults with hypertension; evaluate the potential yield of targeted outreach and model performance across subgroups; and determine whether recorded contact patterns provide additional information for predicting subsequent cardiovascular hospitalization. Methods: This retrospective cohort study used MIMIC-IV data. Adults with hypertension, a qualifying systolic blood pressure reading >140 mmHg, defined as the first such reading on or after index, and 90 days of subsequent database coverage and survival were included. The primary outcome was the absence of a recorded blood pressure measurement within 90 days of the index reading. Gradient-boosted trees incorporating 62 demographic, clinical, and contact-history features were compared with logistic regression models using a random 6:2:2 training, validation, and test split, with isotonic recalibration. Nested cross-validation was used to assess the tree model. Model discrimination, calibration, simulated outreach yield, and subgroup performance were evaluated. Results: Among 30,942 patients, 10,899 (35.2%) had no recorded blood pressure measurement within 90 days. In the test set (n = 6,189), the gradient-boosted tree model achieved an area under the receiver operating characteristic curve (AUROC) of 0.740 (95% CI, 0.727-0.753), compared with 0.698 for penalized logistic regression and 0.687 for two-variable logistic regression. The calibration slope was 0.91, and the area under the precision-recall curve was 0.568 (95% CI, 0.546- 0.593). Selecting the 20% of patients with the highest predicted risk identified 34.2% (95% CI, 32.7-35.7) of patients without a recorded measurement, with a positive predictive value of 60.4% (95% CI, 57.4-63.2). In out-of-fold subgroup analyses using a common selection threshold corresponding to an overall selection rate of 20%, sensitivity was 21.5% among Black patients, 23.3% among Hispanic patients, and 37.2% among White patients. Among 15,200 participants in the landmark analysis, 664 cardiovascular hospitalizations occurred. Increasing and sustained contact patterns were associated with modestly higher hazards of cardiovascular hospitalization, with the association primarily attributable to heart failure hospitalization. For sustained versus minimal contact, the hazard ratio for the composite outcome was 1.40 (95% CI, 1.06-1.85). However, adding contact gaps and trajectories improved the C-statistic by only 0.0023 (95% CI, -0.0006 to 0.0056). Conclusions: The model showed potential for prioritizing blood pressure reassessment outreach, although sensitivity varied across racial and ethnic groups. Recorded contact patterns provided little incremental discrimination for cardiovascular hospitalization.