HypertensionNCT05406336
Machine Learning to Reduce Hypertension Treatment Clinical Inertia
A study of Hypertension, run by Temple University.
Trial 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… Read more about this study
- Who is it for?
- Ages 20 years and olderHealthy volunteers can take part
- Conditions the study is for
- Hypertension
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 50 participants
- Where
- No sites listed yet
- Study dates
- 25 Apr 2025to 31 Jul 2025
- Phase
- Interventional
- Are there results?
- Not yet: the study is under way
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 20 years and older
- All sexes
- Healthy volunteers accepted
- Adult, Older adult
Who may be able to join
None listed.
Who cannot join
- -
Trial locations and contacts
No sites are listed yet. Contact the study team or a site near you to ask about taking part.
About this study
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.
Read the 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
- Being tested
- Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithm
- Keywords
- Uncontrolled blood pressure, Antihypertensive medication, Hypertension treatment, Clinical inertia, Machine learning
- Medical subject headings
- Hypertension
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithmOther
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.
| Study group | Role | What they receive |
|---|---|---|
| No Information from Machine Learning Algorithm | No intervention | 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. |
| Information from Machine Learning Algorithm | Experimental | 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. Receives: Other: Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithm |
What is being measured
The primary outcome is the main question the study is designed to answer.
Vignette #1 - antihypertensive medication treatment intensification
A clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.
Measured: Immediately after clinical vignette
Vignette #2 - antihypertensive medication treatment intensification
A second clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.
Measured: Immediately after clinical vignette
Vignette #3 - antihypertensive medication treatment intensification
A third clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.
Measured: Immediately after clinical vignette
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted6 Jun 2022
- Study start25 Apr 2025 (estimated)
- Primary completion31 May 2025 (estimated)
- Study completion31 Jul 2025 (estimated)
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Other trials for HypertensionStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Investigator
- Enrolment
- 50 participants (estimated)
Sponsor and investigators
- Lead sponsor
- Temple University
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Gabriel Tajeu, DrPH, Principal investigator, University of Alabama at Birmingham
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT05406336
- Sponsor's protocol ID
- K01HL151974
- Status last verified
- Apr 2025
- First submitted
- 25 May 2022
- Last updated
- 10 Apr 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- No
Study at a glance
From the registry record, last updated by the sponsor on 10 Apr 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Machine Learning to Reduce Hypertension Treatment Clinical Inertia
- ClinicalTrials.gov ID
- NCT05406336
- Acronym / sponsor ID
- K01HL151974
- Conditions
- Hypertension
- Keywords
- Uncontrolled blood pressure, Antihypertensive medication, Hypertension treatment, Clinical inertia, Machine learning
- Study type
- Interventional
- Recruitment status
- Not yet recruiting
- Enrolment
- 50 participants (estimated)
- Lead sponsor
- Temple UniversityUniversity, hospital or other
- Study start
- 25 Apr 2025 (estimated)
- Primary completion
- 31 May 2025 (estimated)
- Study completion
- 31 Jul 2025 (estimated)
- First posted
- 6 Jun 2022 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
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.
Study design
- Primary purpose
- Treatment
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, investigator)
- Number of arms
- 2
| Arm | Type | Receives |
|---|---|---|
| No Information from Machine Learning Algorithm | No intervention | –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. |
| Information from Machine Learning Algorithm | Experimental | Other: Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithmThe 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. |
Who may qualify
- Ages
- 20 years and older
- Sex
- All
- Healthy volunteers
- Accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (0 listed)
Key exclusion criteria (1 listed)
- -
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithmThe 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. | Other | Information from Machine Learning Algorithm |
Primary outcome
Vignette #1 - antihypertensive medication treatment intensification
Time frame: Immediately after clinical vignette
A clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.
Vignette #2 - antihypertensive medication treatment intensification
Time frame: Immediately after clinical vignette
A second clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.
Vignette #3 - antihypertensive medication treatment intensification
Time frame: Immediately after clinical vignette
A third clinical vignette of a patient with uncontrolled blood pressure will be presented to clinicians. They will then be asked to assess wether they would intensify antihypertensive medication treatment. This will be assessed using a 5-point Likert scale with scores ranging from 1 ("Strongly disagree") to 5 ("Strongly agree"). Likert scale scores will be compared between clinicians in the control and intervention group.
Results and safety
This study is still under way. Results are not available in the source record yet.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 10 Apr 2025.
- Individual participant dataSharing planned: No
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part.
Gabriel Tajeu, DrPH principal investigator
University of Alabama at Birmingham
Questions about this trial
What is this clinical trial studying?
A study of Hypertension, run by Temple University.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 0 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: opening soon, as of the sponsor's last update on 10 Apr 2025. Status can change between updates, so confirm with the study contact before making plans.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
This study is still under way. Results are not available in the source record yet.
How can a doctor refer a patient or contact the research team?
Contact Gabriel Tajeu, DrPH (2055312258, gtajeu@uab.edu), or a site near the patient. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Vignette #1 - antihypertensive medication treatment intensification (time frame: Immediately after clinical vignette). The study lists 3 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 10 Apr 2025.