Heart FailureNCT03804606
Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure
A study testing Referral to MTM Pharmacist in people with Heart Failure, run by Geisinger Clinic at 1 site in United States.
Trial summary
Out-of-hospital care of complex diseases, such as heart failure, is transitioning from an individual patient-doctor relationship to population health management strategies. As an example, at our institution, medication therapy management (MTM) pharmacists are being deployed to patients with heart failure with the… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Heart Failure
- What is being tested?
- Referral to MTM Pharmacist
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 100 participants
- Where
- 1 siteUnited States
- Study dates
- 28 Feb 2019to 1 Sep 2023
- Phase
- Interventional
- Are there results?
- None posted in the registry
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 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- All adult Geisinger patients with heart failure, as identified by a validated EHR (Electonic Health Record)-based phenotype algorithm,
- Patients with a Geisinger primary care provider (PCP)
- Patients who follow with Geisinger Cardiology (at least 1 visit in past two years).
- Fulfills the specifications for arm assignment based on the results of the care gap benefit model.
Who cannot join
- Patients with a Geisinger PCP or Cardiologist in the South Central Region (part of the Geisinger Holy Spirit footprint) as MTM availability is limited in this service area.
- Patients who have indicated they do not wish to participate in research studies
Trial locations and contacts
1 site in 1 country.
United States1 site
Geisinger Health System
Danville, Pennsylvania, 17822
About this study
Out-of-hospital care of complex diseases, such as heart failure, is transitioning from an individual patient-doctor relationship to population health management strategies. As an example, at our institution, medication therapy management (MTM) pharmacists are being deployed to patients with heart failure with the intent of improving patient outcomes (through proper medication management and adherence) while reducing cost (e.g., keeping these patients out of the hospital). The success of such strategies will be dependent on the ability to effectively direct scarce resources to deliver appropriate/needed care to patients. In this prospective, pragmatic randomized and matched controlled study, the investigators hypothesize that the combination of accurate, data-driven benefit models and MTM pharmacist intervention in patients with heart failure will result in reduced 1-year mortality and hospital admissions. Using our extensive historical electronic health record data, the investigators have developed a machine learning model that, for individual patients with heart failure, predicts risk and benefit (that is, reduction in risk) associated with closing specific "care gaps". These care gaps represent standard evidence-based treatments that may be missing for an individual patient, such as beta blockers or flu shots. The investigators will use this model to define three cohorts to be studied: 1) a high risk/high benefit group to be referred for MTM pharmacist intervention, 2) a high risk/high benefit group to continue with existing standard of care (not necessarily involving MTM pharmacy), and 3) a high risk/low benefit group to be referred for MTM pharmacist intervention. Comparison of groups 1 and 2 (for which assignment is randomized) will evaluate the effectiveness of the MTM pharmacy intervention, while comparison of groups 1 and 3 will evaluate the accuracy of the benefit model prediction and importance of appropriate patient selection for treatment. The primary study outcomes will be mortality and number of hospital admissions during 1-year follow-up following study enrollment.
Read the detailed description
Heart failure is a highly prevalent, complex disease associated with significant morbidity and cost. For example, Geisinger manages over 900 heart failure admissions per year, with each admission costing an estimated $10,000-$12,000. As payment models continue to shift from fee-for-service to value-based, significant investments are occurring in care team resources to help manage populations of patients with heart failure. These care team resources have demonstrated effectiveness. For example, internal Geisinger metrics indicate that interventions led by clinical pharmacists aimed at poorly controlled type II diabetics have resulted in a sustained median 1% (absolute) drop in hemoglobin hemoglobin a1C (glycated hemoglobin). In this new environment, intelligent deployment of limited resources is critical to drive quality and contain costs.
In heart failure, current risk prediction have demonstrated poor prognostic abilities and present a barrier to "precision delivery" of care team resources. Currently approaches are limited due to not fully utilizing rich, highly granular objective data such as imaging, laboratory values, and vital signs, and therefore are not optimized to accurately predict outcomes. The investigators have generated a machine learning model to predict both 1-year survival and heart failure hospitalization within 6 months of echocardiography. This model utilized 169 input variables including clinical data, imaging measures, and 18 care gap variables. Our results showed not only that the machine learning model had far superior accuracy to predict the morbidity endpoints compared to current approaches utilizing billing code data, but also that care gap variables were important for predicting 1-year survival. Moreover, the investigators showed that closing four of the care gap variables (flu vaccination, evidence-based beta blocker treatment, ACE (angiotensin-converting-enzyme) inhibitor/ARB (angiotensin receptor blockers) treatment, and control of diabetic a1C (i.e., values "in goal)) resulted in a predicted improvement in 1-year survival of ~1200 (out of ~11,000) patients. This study therefore aims to apply this machine learning approach to direct care team resources in a clinical setting to evaluate its impact on patient survival and healthcare utilization.
- Conditions
- Heart Failure
- Being tested
- Referral to MTM Pharmacist
- Keywords
- Heart Failure, Machine Learning, Medication Therapy Management, Supervised Machine Learning, Population Health Management
- Medical subject headings
- Heart Failure
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Referral to MTM PharmacistOther
Patients will be referred for an encounter with a medication therapy management pharmacist.
| Study group | Role | What they receive |
|---|---|---|
| High benefit, MTM | Experimental | This arm will comprise patients with heart failure who are predicted to receive high benefit (reduction in mortality risk) by addressing open care gaps. Following randomization, they will be referred to MTM pharmacy for review of treatments in an attempt to close appropriate care gaps. Receives: Other: Referral to MTM Pharmacist |
| High benefit, no MTM | No intervention | This arm will comprise patients with heart failure who are predicted to receive high benefit (reduction in mortality risk) by addressing open care gaps. Following randomization, they will continue to receive clinical standard-of-care: regular follow-ups with Community Medicine (every 3 months) and Cardiology (every six months). Importantly, these individuals are eligible for referral to MTM at the discretion of their physicians. |
| Low benefit, MTM | Active comparator | This arm will comprise patients with heart failure who are predicted to receive low benefit (reduction in mortality risk) by addressing open care gaps. They will be selected based on age, sex, and risk-matching to the High benefit, MTM arm. They will be referred to MTM pharmacy for review of treatments in an attempt to close appropriate care gaps. Receives: Other: Referral to MTM Pharmacist |
What is being measured
The primary outcome is the main question the study is designed to answer.
All-cause mortality
Death following randomization
Measured: 1 year
Hospital admission
Number of admissions to the hospital
Measured: 1 year
Secondary outcomes (5)
Healthcare utilization - Total cost of care
Total cost of care (co-pays, claims paid, co-insurance, out-of-pocket costs) for the subset of patients in the study covered by the Geisinger Health Plan
Measured: 1 year
Incidence of flu vaccine care gap closure; relationship to mortality
The investigators will compare rates of closure for the flu vaccine care gap among arms and compare predicted versus actual mortality as a function of the observed care gap closure.
Measured: 1 year
Incidence of evidence-based beta blocker care gap closure; relationship to mortality
The investigators will compare rates of closure for the evidence-based beta blocker care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.
Measured: 1 year
Incidence of ACE inhibitor/ARB care gap closure; relationship to mortality
The investigators will compare rates of closure for the ACE inhibitor/ARB care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.
Measured: 1 year
Incidence of diabetic a1C "in goal" care gap closure; relationship to mortality
The investigators will compare rates of closure for the diabetic a1C "in goal" care gap among arms and compare predicted versus actual hospitalization as a function of the observed care gap closure.
Measured: 1 year
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted15 Jan 2019
- Study start28 Feb 2019
- Primary completion1 Sep 2023
- Study completion1 Sep 2023
Why it stopped: insufficient participants enrolled and study team has left Geisinger
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (3)Papers the sponsor has linked to this study.
- Other trials for Heart FailureStudies 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
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind
- Who is masked
- Participant, Care provider
- Enrolment
- 100 participants (actual)
Sponsor and investigators
- Lead sponsor
- Geisinger Clinic
- Sponsor type
- University, hospital or other
- Responsible party
- Sponsor
- Investigators
- Christopher M Haggerty, PhD, Principal investigator, Geisinger ClinicBrandon K Fornwalt, MD, PhD, Principal investigator, Geisinger Clinic
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03804606
- Sponsor's protocol ID
- 2018-0735
- Status last verified
- Mar 2025
- First submitted
- 11 Jan 2019
- Last updated
- 28 Mar 2025
- Results posted
- No
- Data monitoring committee
- No
- FDA-regulated drug
- No
- FDA-regulated device
- No
- Individual data sharing
- Yes: Upon reasonable request to the PI, IPD (individual patient data) related to evaluation of the primary outcomes (group designation, vital status, number of hospital admissions, statuses of care gaps) will be made available to other researchers.
Publications
Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):629-640. doi: 10.1161/CIRCOUTCOMES.116.003039. Epub 2016 Nov 8.
PubMed 28263938Bhavnani SP, Parakh K, Atreja A, Druz R, Graham GN, Hayek SS, Krumholz HM, Maddox TM, Majmudar MD, Rumsfeld JS, Shah BR. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017 Nov 28;70(21):2696-2718. doi: 10.1016/j.jacc.2017.10.018. No abstract available.
PubMed 29169478Haga K, Murray S, Reid J, Ness A, O'Donnell M, Yellowlees D, Denvir MA. Identifying community based chronic heart failure patients in the last year of life: a comparison of the Gold Standards Framework Prognostic Indicator Guide and the Seattle Heart Failure Model. Heart. 2012 Apr;98(7):579-83. doi: 10.1136/heartjnl-2011-301021.
PubMed 22422744
Study at a glance
From the registry record, last updated by the sponsor on 28 Mar 2025. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure
- ClinicalTrials.gov ID
- NCT03804606
- Acronym / sponsor ID
- 2018-0735
- Conditions
- Heart Failure
- Keywords
- Heart Failure, Machine Learning, Medication Therapy Management, Supervised Machine Learning, Population Health Management
- Study type
- Interventional
- Recruitment status
- TerminatedReason stopped: insufficient participants enrolled and study team has left Geisinger
- Enrolment
- 100 participants (actual)
- Lead sponsor
- Geisinger ClinicUniversity, hospital or other
- Study start
- 28 Feb 2019 (actual)
- Primary completion
- 1 Sep 2023 (actual)
- Study completion
- 1 Sep 2023 (actual)
- First posted
- 15 Jan 2019 (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.
Out-of-hospital care of complex diseases, such as heart failure, is transitioning from an individual patient-doctor relationship to population health management strategies. As an example, at our institution, medication therapy management (MTM) pharmacists are being deployed to patients with heart failure with the intent of improving patient outcomes (through proper medication management and adherence) while reducing cost (e.g., keeping these patients out of the hospital). The success of such strategies will be dependent on the ability to effectively direct scarce resources to deliver appropriate/needed care to patients. In this prospective, pragmatic randomized and matched controlled study, the investigators hypothesize that the combination of accurate, data-driven benefit models and MTM pharmacist intervention in patients with heart failure will result in reduced 1-year mortality and hospital admissions. Using our extensive historical electronic health record data, the investigators have developed a machine learning model that, for individual patients with heart failure, predicts risk and benefit (that is, reduction in risk) associated with closing specific "care gaps". These care gaps represent standard evidence-based treatments that may be missing for an individual patient, such as beta blockers or flu shots. The investigators will use this model to define three cohorts to be studied: 1) a high risk/high benefit group to be referred for MTM pharmacist intervention, 2) a high risk/high benefit group to continue with existing standard of care (not necessarily involving MTM pharmacy), and 3) a high risk/low benefit group to be referred for MTM pharmacist intervention. Comparison of groups 1 and 2 (for which assignment is randomized) will evaluate the effectiveness of the MTM pharmacy intervention, while comparison of groups 1 and 3 will evaluate the accuracy of the benefit model prediction and importance of appropriate patient selection for treatment. The primary study outcomes will be mortality and number of hospital admissions during 1-year follow-up following study enrollment.
Detailed description
Heart failure is a highly prevalent, complex disease associated with significant morbidity and cost. For example, Geisinger manages over 900 heart failure admissions per year, with each admission costing an estimated $10,000-$12,000. As payment models continue to shift from fee-for-service to value-based, significant investments are occurring in care team resources to help manage populations of patients with heart failure. These care team resources have demonstrated effectiveness. For example, internal Geisinger metrics indicate that interventions led by clinical pharmacists aimed at poorly controlled type II diabetics have resulted in a sustained median 1% (absolute) drop in hemoglobin hemoglobin a1C (glycated hemoglobin). In this new environment, intelligent deployment of limited resources is critical to drive quality and contain costs.
In heart failure, current risk prediction have demonstrated poor prognostic abilities and present a barrier to "precision delivery" of care team resources. Currently approaches are limited due to not fully utilizing rich, highly granular objective data such as imaging, laboratory values, and vital signs, and therefore are not optimized to accurately predict outcomes. The investigators have generated a machine learning model to predict both 1-year survival and heart failure hospitalization within 6 months of echocardiography. This model utilized 169 input variables including clinical data, imaging measures, and 18 care gap variables. Our results showed not only that the machine learning model had far superior accuracy to predict the morbidity endpoints compared to current approaches utilizing billing code data, but also that care gap variables were important for predicting 1-year survival. Moreover, the investigators showed that closing four of the care gap variables (flu vaccination, evidence-based beta blocker treatment, ACE (angiotensin-converting-enzyme) inhibitor/ARB (angiotensin receptor blockers) treatment, and control of diabetic a1C (i.e., values "in goal)) resulted in a predicted improvement in 1-year survival of ~1200 (out of ~11,000) patients. This study therefore aims to apply this machine learning approach to direct care team resources in a clinical setting to evaluate its impact on patient survival and healthcare utilization.
Study design
- Primary purpose
- Health services research
- Allocation
- Randomised
- Intervention model
- Parallel groups
- Masking
- Double blind (participant, care provider)
- Number of arms
- 3
| Arm | Type | Receives |
|---|---|---|
| High benefit, MTM | Experimental | Other: Referral to MTM PharmacistThis arm will comprise patients with heart failure who are predicted to receive high benefit (reduction in mortality risk) by addressing open care gaps. Following randomization, they will be referred to MTM pharmacy for review of treatments in an attempt to close appropriate care gaps. |
| High benefit, no MTM | No intervention | –This arm will comprise patients with heart failure who are predicted to receive high benefit (reduction in mortality risk) by addressing open care gaps. Following randomization, they will continue to receive clinical standard-of-care: regular follow-ups with Community Medicine (every 3 months) and Cardiology (every six months). Importantly, these individuals are eligible for referral to MTM at the discretion of their physicians. |
| Low benefit, MTM | Active comparator | Other: Referral to MTM PharmacistThis arm will comprise patients with heart failure who are predicted to receive low benefit (reduction in mortality risk) by addressing open care gaps. They will be selected based on age, sex, and risk-matching to the High benefit, MTM arm. They will be referred to MTM pharmacy for review of treatments in an attempt to close appropriate care gaps. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (4 listed)
- All adult Geisinger patients with heart failure, as identified by a validated EHR (Electonic Health Record)-based phenotype algorithm,
- Patients with a Geisinger primary care provider (PCP)
- Patients who follow with Geisinger Cardiology (at least 1 visit in past two years).
- Fulfills the specifications for arm assignment based on the results of the care gap benefit model.
Key exclusion criteria (2 listed)
- Patients with a Geisinger PCP or Cardiologist in the South Central Region (part of the Geisinger Holy Spirit footprint) as MTM availability is limited in this service area.
- Patients who have indicated they do not wish to participate in research studies
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 |
|---|---|---|
| Referral to MTM PharmacistPatients will be referred for an encounter with a medication therapy management pharmacist. | Other | High benefit, MTM, Low benefit, MTM |
Primary outcome
All-cause mortality
Time frame: 1 year
Death following randomization
Hospital admission
Time frame: 1 year
Number of admissions to the hospital
5 secondary outcomes
Healthcare utilization - Total cost of care
Time frame: 1 year
Incidence of flu vaccine care gap closure; relationship to mortality
Time frame: 1 year
Incidence of evidence-based beta blocker care gap closure; relationship to mortality
Time frame: 1 year
Incidence of ACE inhibitor/ARB care gap closure; relationship to mortality
Time frame: 1 year
Incidence of diabetic a1C "in goal" care gap closure; relationship to mortality
Time frame: 1 year
Results and safety
The study was stopped early (reason given: insufficient participants enrolled and study team has left Geisinger). Results are not available in the source record.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 28 Mar 2025.
- Background: PubMed 28263938Mortazavi BJ, Downing NS, Bucholz EM, Dharmarajan K, Manhapra A, Li SX, Negahban SN, Krumholz HM. Analysis of Machine Learning Techniques for Heart Failure Readmissions. Circ Cardiovasc Qual Outcomes. 2016 Nov;9(6):629-640. doi: 10.1161/CIRCOUTCOMES.116.003039. Epub 2016 Nov 8.
- Background: PubMed 29169478Bhavnani SP, Parakh K, Atreja A, Druz R, Graham GN, Hayek SS, Krumholz HM, Maddox TM, Majmudar MD, Rumsfeld JS, Shah BR. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017 Nov 28;70(21):2696-2718. doi: 10.1016/j.jacc.2017.10.018. No abstract available.
- Background: PubMed 22422744Haga K, Murray S, Reid J, Ness A, O'Donnell M, Yellowlees D, Denvir MA. Identifying community based chronic heart failure patients in the last year of life: a comparison of the Gold Standards Framework Prognostic Indicator Guide and the Seattle Heart Failure Model. Heart. 2012 Apr;98(7):579-83. doi: 10.1136/heartjnl-2011-301021.
- Individual participant dataSharing planned: Yes. Upon reasonable request to the PI, IPD (individual patient data) related to evaluation of the primary outcomes (group designation, vital status, number of hospital admissions, statuses of care gaps) will be made available to other researchers.
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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. This study is not recruiting at the moment.
Christopher M Haggerty, PhD principal investigator
Geisinger Clinic
Brandon K Fornwalt, MD, PhD principal investigator
Geisinger Clinic
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Referral to MTM Pharmacist in people with Heart Failure, run by Geisinger Clinic at 1 site in United States.
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 4 inclusion and 2 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: stopped early, as of the sponsor's last update on 28 Mar 2025. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
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?
The study was stopped early (reason given: insufficient participants enrolled and study team has left Geisinger). Results are not available in the source record.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
All-cause mortality (time frame: 1 year). The study lists 2 primary outcomes in all. It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03804606, which also lists 3 linked publications.
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 28 Mar 2025.