Machine Learning to Reduce Hypertension Treatment Clinical Inertia
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: Predicted uncontrolled BP status (yes/no) at follow up visit, derived using a machine learning algorithm.
- Who it may be relevant to
- Registry conditions: Hypertension. Basic parameters: from 20 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Center list to be confirmed — check the primary protocol.
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
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.
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.
Primary outcome measures
- Vignette #1 - antihypertensive medication treatment intensification [Time frame: Immediately after clinical vignette]
- Vignette #2 - antihypertensive medication treatment intensification [Time frame: Immediately after clinical vignette]
- Vignette #3 - antihypertensive medication treatment intensification [Time frame: Immediately after clinical vignette]
Eligibility criteria
Inclusion Criteria: practicing primary care clinicians who see patients (i.e., internal medicine, family medicine, attending physicians, nurse practitioners) will be eligible to participate -
Exclusion criteria
\-
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Double blind
- Primary purpose
- Treatment
Study locations
Center list to be confirmed — check the primary protocol.
Identifiers
NCT: NCT05406336 · K01HL151974