Risk and Benefit Informed MTM Pharmacist Intervention in Heart Failure

NCT03804606 | Terminated

• 1 other identifier: 2018-0735
• Study Type: interventional
• Target: 100
• Locations: 1 country
• Sites: 1

Brief 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 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.

Conditions

Heart Failure

Keywords

Heart Failure; Machine Learning; Medication Therapy Management; Supervised Machine Learning; Population Health Management

Outcome Measures

Primary Outcomes (2)

• All-cause mortality

  Death following randomization

  1 year

• Hospital admission

  Number of admissions to the hospital

  1 year

Secondary Outcomes (5)

• Healthcare utilization - Total cost of care

  1 year

• Incidence of flu vaccine care gap closure; relationship to mortality

  1 year

• Incidence of evidence-based beta blocker care gap closure; relationship to mortality

  1 year

• Incidence of ACE inhibitor/ARB care gap closure; relationship to mortality

  1 year

• Incidence of diabetic a1C "in goal" care gap closure; relationship to mortality

  1 year

Study Arms (3)

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.

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.

Other: Referral to MTM Pharmacist

Interventions

Referral to MTM Pharmacist OTHER

Patients will be referred for an encounter with a medication therapy management pharmacist.

High benefit, MTM; Low benefit, MTM

Eligibility Criteria

• Age: 18 Years+
• Sex: all
• Healthy Volunteers: No
• Age Groups: Adult (18-64), Older Adult (65+)

You may qualify if:

• 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.

You may not qualify if:

• 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

Sponsors & Collaborators

• Geisinger Clinic: lead

Study Sites (1)

Geisinger Health System

Danville, Pennsylvania, 17822, United States

Location

Related Publications (3)

• 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.

  PMID: 28263938 BACKGROUND

• 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.

  PMID: 29169478 BACKGROUND

• 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.

  PMID: 22422744 BACKGROUND

MeSH Terms

Conditions

Heart Failure

Condition Hierarchy (Ancestors)

Heart Diseases; Cardiovascular Diseases

Study Officials

• Christopher M Haggerty, PhD

  Geisinger Clinic

  PRINCIPAL INVESTIGATOR

• Brandon K Fornwalt, MD, PhD

  Geisinger Clinic

  PRINCIPAL INVESTIGATOR

Study Design

• Study Type: interventional
• Phase: not applicable
• Allocation: RANDOMIZED
• Masking: DOUBLE
• Who Masked: PARTICIPANT, CARE PROVIDER
• Purpose: HEALTH SERVICES RESEARCH
• Intervention Model: PARALLEL
• Sponsor Type: OTHER
• Responsible Party: SPONSOR

Study Record Dates

• First Submitted: January 11, 2019
• First Posted: January 15, 2019
• Study Start: February 28, 2019
• Primary Completion: September 1, 2023
• Study Completion: September 1, 2023
• Last Updated: March 28, 2025

Record last verified: 2025-03

Data Sharing

• IPD Sharing: Will share

Locations

US (1)