Heart Failure Clinical Trial
Official title:
Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure
NCT number | NCT03804606 |
Other study ID # | 2018-0735 |
Secondary ID | |
Status | Suspended |
Phase | N/A |
First received | |
Last updated | |
Start date | February 28, 2019 |
Est. completion date | July 2024 |
Verified date | September 2023 |
Source | Geisinger Clinic |
Contact | n/a |
Is FDA regulated | No |
Health authority | |
Study type | Interventional |
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.
Status | Suspended |
Enrollment | 600 |
Est. completion date | July 2024 |
Est. primary completion date | July 2024 |
Accepts healthy volunteers | No |
Gender | All |
Age group | 18 Years and older |
Eligibility | Inclusion Criteria: - 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. Exclusion Criteria: - 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 |
Country | Name | City | State |
---|---|---|---|
United States | Geisinger Health System | Danville | Pennsylvania |
Lead Sponsor | Collaborator |
---|---|
Geisinger Clinic |
United States,
Bhavnani 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. — View Citation
Haga 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. — View Citation
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. — View Citation
Type | Measure | Description | Time frame | Safety issue |
---|---|---|---|---|
Primary | All-cause mortality | Death following randomization | 1 year | |
Primary | Hospital admission | Number of admissions to the hospital | 1 year | |
Secondary | 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 | 1 year | |
Secondary | 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. | 1 year | |
Secondary | 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. | 1 year | |
Secondary | 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. | 1 year | |
Secondary | 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. | 1 year |
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