Machine Learning to Reduce Hypertension Treatment Clinical Inertia

NCT ID: NCT05406336

Last Updated: 2025-04-10

Basic Information

• Recruitment Status: NOT_YET_RECRUITING
• Clinical Phase: NA
• Total Enrollment: 50 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2025-04-25
• Study Completion Date: 2025-07-31

Brief 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.

Detailed Description

Among adults with uncontrolled blood pressure (BP) at a clinic visit, clinical inertia is common. Clinical inertia is defined as a failure of providers to initiate or intensify treatment (i.e., adding medication or increasing dosage) when guidelines indicate doing so. Prior studies report that clinicians intensify antihypertensive medication treatment in less than 20% of visits where intensification would have been clinically recommended. Thus, patients who have uncontrolled BP may not receive timely therapy to control their BP. To address this issue, the investigators will use a randomized design to test the hypothesis that clinicians will be more likely to intensify the hypertensive regimen and/or assess nonadherence for patients with uncontrolled BP at the current visit when presented with information that a patient is predicted to have uncontrolled BP at the next visit by a machine learning algorithm.

Conditions

Hypertension

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: TREATMENT
• Blinding Strategy: DOUBLE

Study Groups

No Information from Machine Learning Algorithm

The investigators will create case vignettes to assess clinician hypertension management behavior, specifically antihypertensive medication intensification among individuals with uncontrolled blood pressure (BP). This arm will not include information from a machine learning algorithm designed to predict uncontrolled BP at a follow up visit.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Information from Machine Learning Algorithm

The investigators will create case vignettes to assess clinician hypertension management behavior, specifically antihypertensive medication intensification among individuals with uncontrolled blood pressure (BP). This arm will include information from a machine learning algorithm designed to predict uncontrolled BP at a follow up visit about whether the algorithm predicts that the patient will have uncontrolled BP at the next visit.

Group Type: EXPERIMENTAL

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

Intervention Type: OTHER

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.

Interventions

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.

Intervention Type: OTHER

Eligibility Criteria

Exclusion Criteria

-

• Minimum Eligible Age: 20 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: Yes

Sponsors

Temple University

OTHER

Sponsor Role: lead

Responsible Party

Responsibility Role: SPONSOR

Principal Investigators

Gabriel Tajeu, DrPH

Role: PRINCIPAL_INVESTIGATOR

University of Alabama at Birmingham

Central Contacts

Gabriel Tajeu, DrPH

Role: CONTACT

gtajeu@uab.edu

2055312258

Other Identifiers

K01HL151974

Identifier Type: NIH

Identifier Source: org_study_id

View Link