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Referral to MTM Pharmacist for Heart Failure

N/A

Waitlist Available

Led By Christopher M Haggerty, PhD

Research Sponsored by Geisinger Clinic

Eligibility Criteria Checklist

Specific guidelines that determine who can or cannot participate in a clinical trial

Must have

Be older than 18 years old

Timeline

Screening 3 weeks

Treatment Varies

Follow Up 1 year

Awards & highlights

Study Summary

This trial is studying whether a machine learning model that predicts risk and benefit of standard evidence-based treatments can help improve care for patients with heart failure.

Timeline

Screening ~ 3 weeks3 visits

Treatment ~ Varies

Follow Up ~ 1 year

Screening ~ 3 weeks

Treatment ~ Varies

Follow Up ~1 year

This trial's timeline: 3 weeks for screening, Varies for treatment, and 1 year for reporting.

Treatment Details

Study Objectives

Outcome measures can provide a clearer picture of what you can expect from a treatment.

Primary outcome measures

All-cause mortality

Hospital admission

Secondary outcome measures

Healthcare utilization - Total cost of care

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

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

+2 more

Trial Design

3Treatment groups

Experimental Treatment

Active Control

Group I: High benefit, MTMExperimental Treatment1 Intervention

Group II: High benefit, no MTMActive Control1 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.

Group III: Low benefit, MTMActive Control1 Intervention

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.

Do I qualify?Apply below to find out

Find a Location

Who is running the clinical trial?

Geisinger ClinicLead Sponsor

149 Previous Clinical Trials

1,885,535 Total Patients Enrolled

5 Trials studying Heart Failure

6,762 Patients Enrolled for Heart Failure

Christopher M Haggerty, PhDPrincipal InvestigatorGeisinger Clinic

Brandon K Fornwalt, MD, PhDPrincipal InvestigatorGeisinger Clinic

Frequently Asked Questions

These questions and answers are submitted by anonymous patients, and have not been verified by our internal team.

What objectives are the researchers expecting to realize through this investigation?

"The primary aim of this investigation, which will span a 12 month period, is to measure hospital admission rates. Secondary objectives include assessing the correlation between closing evidence-based beta blocker care gaps and mortality; measuring how successful flu vaccine care gap closure can be at reducing death rates; and evaluating healthcare utilisation expenses for participants with Geisinger Health Plan coverage."

Answered by AI

Is recruitment still open for the current experiment?

"According to clinicaltrials.gov, this study has fulfilled its recruitment quota and is no longer recruiting patients. Initially posted on February 28th 2019 and last updated August 3rd 2022, the trial is no longer an option for potential candidates; however 701 other trials are actively searching participants."

Answered by AI

Recent research and studies

Journal of the American College of CardiologyJournal

2017 Roadmap for Innovation—acc Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health

Bhavnani, Sanjeev P., Kapil Parakh, Ashish Atreja, Regina Druz, Garth N. Graham, Salim S. Hayek, Harlan M. Krumholz, et al.. 2017. “2017 Roadmap for Innovation—acc Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health”. Journal of the American College of Cardiology. Elsevier BV. doi:10.1016/j.jacc.2017.10.018.

HeartJournal

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

Haga, Kristin, Scott Murray, Janet Reid, Andrea Ness, Maureen O'Donnell, Diane Yellowlees, and Martin A Denvir. 2012. “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. BMJ. doi:10.1136/heartjnl-2011-301021.

Circulation: Cardiovascular Quality and OutcomesJournal

Analysis of Machine Learning Techniques for Heart Failure Readmissions

Mortazavi, Bobak J., Nicholas S. Downing, Emily M. Bucholz, Kumar Dharmarajan, Ajay Manhapra, Shu-Xia Li, Sahand N. Negahban, and Harlan M. Krumholz. 2016. “Analysis of Machine Learning Techniques for Heart Failure Readmissions”. Circulation: Cardiovascular Quality and Outcomes. Ovid Technologies (Wolters Kluwer Health). doi:10.1161/circoutcomes.116.003039.

clinicaltrials.govStudy