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Machine Learning to Reduce Hypertension Treatment Clinical Inertia

NCT05406336 · Status: NOT_YET_RECRUITING · Phase: NA · Type: INTERVENTIONAL · Enrollment: 50

Last updated 2025-04-10

No results posted yet for this study

Summary

Among individuals with an uncontrolled BP at the current visit, the objective of this study is to compare clinical management of hypertension with and without information from a machine learning algorithm on whether a patient will have uncontrolled blood pressure at their next follow up visit through a case-vignette study.

Conditions

Interventions

OTHER

Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithm

The investigators have created a machine learning algorithm to predict uncontrolled blood pressure (BP) status (yes/no) at a follow up visit among adults with uncontrolled BP at their current visit. The investigators will determine whether adding this information to a vignette describing a patient will increase the likelihood that a clinician will intensify antihypertensive medication treatment.

Sponsors & Collaborators

  • Temple University

    lead OTHER

Principal Investigators

  • Gabriel Tajeu, DrPH · University of Alabama at Birmingham

Study Design

Allocation
RANDOMIZED
Purpose
TREATMENT
Masking
DOUBLE
Model
PARALLEL

Eligibility

Min Age
20 Years
Sex
ALL
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2025-04-25
Primary Completion
2025-05-31
Completion
2025-07-31

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT05406336 on ClinicalTrials.gov