Single-Pill Combination Product Availability of the Antihypertensive Regimens Used for Intensive Systolic Blood Pressure Treatment in the Systolic Blood Pressure Intervention Trial

Use and Cost Patterns of Antihypertensive Medications in the United States From 1996 to 2021

BACKGROUND

Antihypertensive medication use patterns have likely been influenced by changing costs and accessibility over the past 3 decades. This study examines the relationships between patent exclusivity loss, medication costs, and national health policies on antihypertensive medication use.

METHODS

Using 1996 to 2021 Medical Expenditure Panel Survey data of US adults with hypertension taking at least 1 antihypertensive medication, we conducted a cross-sectional analysis. We explored the associations between patent exclusivity loss, per-pill costs, and Medicare Part D enactment on medication use over time, focusing on the most commonly used medications (lisinopril, amlodipine, losartan, hydrochlorothiazide, and metoprolol).

RESULTS

The unweighted sample comprised 50 095 US adults (mean age, 62 years; 53% female). The survey-weighted number of adults taking antihypertensive medications increased from 22 million (95% CIs, 20-23 million) to 55 million (95% CI, 51-60 million) between 1996 and 2021. Loss of patent exclusivity led to increased medication fills, notably for lisinopril, amlodipine, and losartan, which all exhibited within-class dominance. However, per-pill cost decreases coinciding with Medicare Part D did not increase the number of individuals treated or the use of specific antihypertensive medications or classes.

CONCLUSIONS

The increase in antihypertensive medication use over the past decades highlights the significant impact of loss of patent exclusivity on the uptake in the use of specific medications. These findings underscore the complexity of factors influencing medication use, beyond cost reductions alone, and suggest that policies need to consider multiple facets to effectively improve antihypertensive medication accessibility and utilization.

Development of a machine learning-based model for predicting individual responses to antihypertensive treatments

BACKGROUND AND AIMS

Personalized antihypertensive drug selection is essential for optimizing hypertension management. The study aimed to develop a machine learning (ML) model to predict individual blood pressure (BP) responses to different antihypertensive medications.

METHODS AND RESULTS

We used data from a pragmatic, cluster-randomized trial on hypertension management in China. Each patient's multiple visit records were included, and two consecutive visits were paired as the index and subsequent visits. The least absolute shrinkage and selection operator method was used to select index visit variables for predicting subsequent BP. The dataset was randomly divided into training and test sets in a 7:3 ratio. Model performance was evaluated using mean absolute error (MAE) and R-square in the test set. A total of 19,013 hypertension management visit records (6282 patients) were included. The mean age of the study population was 63.9 years, and 2657 (42.3%) were females. A total of 12 phenotypical features (age, sex, smoking within seven days, body mass index, waist circumference, index visit systolic BP, diastolic BP, heart rate, comorbidities of diabetes, dyslipidemia, coronary heart disease, and stroke), together with currently taking any prescribed antihypertensive medication regimens and visits time interval were selected to build the model. The Extreme Gradient Boost model performed best among all candidate algorithms, with an MAE of 8.57 mmHg and an R2 = 0.28 in the test set.

CONCLUSION

The ML techniques exhibit significant potential for predicting individual responses to antihypertensive treatments, thereby aiding clinicians in achieving optimal BP control safely and efficiently.

TRIAL REGISTRATION

ClinicalTrials.gov, NCT03636334. Registered July 3, 2018, https://clinicaltrials.gov/study/NCT03636334.