Clinical trial NCT00303212: Tele-HF: Yale Heart Failure Telemonitoring Study
Official title: A Study of Telemonitoring to Improve Heart Failure Outcomes
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Congestive Heart Failure
Keywords: heart failure
CHF
telemedicine
disease management
Lead sponsor: Yale University
Sponsor class: OTHER
Collaborators: name: National Institutes of Health (NIH)
name: National Heart, Lung, and Blood Institute (NHLBI)
First posted: 2006-03-15
Enrollment: 1660
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: RANDOMIZED
Intervention model: PARALLEL
Primary purpose: PREVENTION

Brief summary: The primary purpose of this study is to determine the effectiveness of telemonitoring compared with usual guideline-based care in preventing hospitalization for heart failure patients.

Detailed description: Heart failure (HF) is a common, costly condition characterized by recurrent periods of clinical decompensation that often lead to repeated hospitalizations (1, 2). Despite advances in the care of patients with HF, population-based outcomes such as mortality and hospitalization rates have not improved substantially over the past decade (3). Episodic, infrequent, outpatient visits are the only usual opportunities for clinicians to detect and treat early signs of HF decompensation; this constitutes a major gap in the current medical model. Moreover, opportunities for patients to take an active role in managing their own conditions occur infrequently. Disease management has emerged as a possible solution to the need for better patient surveillance and engagement. It typically involves multidisciplinary efforts to improve the quality and efficiency of care for patients with chronic conditions, with interventions designed to foster adherence of clinicians to scientific guidelines and patients to treatment plans. However, traditional disease-management programs are generally resource-intensive (often relying on nurse case management), difficult to scale for a large population, and inefficient in providing daily patient monitoring.

Telemonitoring, which bridges clinicians and patients with communication technology, holds promise for closing the gap in HF care (4). This technology has the potential for standardized, widespread implementation (and long-term maintenance) in the near future because it can be easily applied to large patient populations and integrated into the current medical care system. Supporting this potential, preliminary evaluations have suggested that telemonitoring is feasible across a broad spectrum of typical HF patients, relatively inexpensive on a per-patient basis, and highly effective in improving health outcomes. Thus, this approach is ready for rigorous evaluation.

Accordingly, we propose an office-based, multicenter, randomized controlled trial (Tele-HF study) to determine the effectiveness of a telemonitoring strategy in decreasing hospital readmissions and death in patients with HF. Many HF patients experience deterioration in their health status and an increase in weight and symptoms over a period of days and weeks before ultimately presenting to medical attention and requiring hospitalization. Our premise is that a frequent monitoring system can alert clinicians to the early signs and symptoms of decompensation, providing the opportunity for intervention before the patient becomes severely ill and requires hospitalization. Moreover, such a system can engage patients in their care and provide instruction about beneficial self-care strategies. This intervention is not intended to substitute for communication relating to acute care or acute, sudden changes in health status. In these cases, patients are instructed to make direct and immediate contact with their doctor or hospital.

We will use the Pharos Tel-AssuranceTM, an in-home communication system that allows patients to transmit information to their clinicians and provides education to enable patients to actively participate in managing their condition. The system uses conventional telephone lines and does not require the patient to have Internet access. Patients are asked a pre-programmed series of questions and the system automatically uploads the responses to a secure data center. A clinician in each practice can then log on to a secure Internet site using a Web browser to review the patients' responses. The system thus serves as an interface between patients at home and their clinicians, facilitating monitoring of chronic conditions and patient education. While many vendors have potential tools to implement this study, we chose to use Pharos Tel-AssuranceTM because it is simple to use, does not require any equipment in patients' homes and substantial preliminary data suggest high patient and clinician satisfaction with its use. The investigators have no financial interest in this company.

Primary Aim Our primary aim is to determine whether telemonitoring by community-based cardiology office practices reduces the risk of hospital readmission (for any cause) or death after an initial "index hospitalization" for HF. We hypothesize that, among patients recently discharged after a hospitalization for HF, telemonitoring will decrease the rate of rehospitalization or death over 6 months by at least 15% (relative risk reduction). This would yield an absolute risk reduction of 7.5%, so that 1 major adverse event would be averted for every 13 patients.

We have chosen all-cause readmission as part of our primary outcome because poorly controlled HF can result in admissions for a variety of reasons, as the patient becomes weak and susceptible to falls, mental status changes, renal dysfunction, and other debilitating conditions that can result in hospitalization. In addition, from a societal and health system perspective, the overall risk of readmission is more important than disease-specific readmission. Moreover, prior studies suggest that telemonitoring can reduce this outcome.

Secondary Aims

In our secondary aims we will determine whether telemonitoring will:

1. Reduce the rate of all-cause hospital readmission
2. Reduce the rate of hospital readmission for HF
3. Reduce the total number of all-cause and HF-specific hospital readmissions
4. Increase office visits with the clinician receiving information from the telemonitoring system
5. Improve survival after index hospitalization
6. Reduce the cost of inpatient medical care
7. Improve health status
8. Improve patient satisfaction with care
9. Improve patients' self-management of HF

Sub-Group Analyses

The following sub-group analyses will be conducted:

1. Age
2. Sex
3. Race
4. HFPEF vs depressed EF
5. Education
6. Insurance status
7. Self-reported access to care
8. Baseline self-efficacy and self-care
9. Socioeconomic Status
10. Site characteristics

Eligibility criteria: Inclusion Criteria:

* age 18 years or older
* hospitalized for heart failure within the past 30 days
* access to telephone line

Exclusion Criteria:

* not expected to survive 6 months due to irreversible, life-threatening condition
* has or scheduled for cardiac transplant or LVAD
* scheduled for CABG or PCI within 90 days
* severe valvular disease
* Folstein MMSE score less than 20
* resident of a nursing home
* currently a prisoner
* does not speak English or Spanish

Primary outcomes:
measure: Rate of all-cause hospital readmission or death during the 6-month follow-up period.
timeFrame: 6 months
Secondary outcomes:
measure: Rate of all-cause hospital readmissions
timeFrame: 6 months
measure: Number of office visits with clinician receiving information from the telemonitoring system
timeFrame: 6 months
measure: Survival after index hospitalization
timeFrame: 6 months
measure: Cost of inpatient medical care
timeFrame: 6 months
measure: Health status
timeFrame: 6 months
measure: Patient satisfaction with care
timeFrame: 6 months
measure: Patients' self-management of heart failure
timeFrame: 6 months
measure: Post index discharge hospital days
timeFrame: 6 months
measure: Post index discharge hospital days/follow up days alive
timeFrame: 6 months
Other outcomes:
Locations:
city: Mobile
country: United States
facility: Cardiology Associates
state: Alabama
city: Orange
country: United States
facility: UC Irvine Medical Center
state: California
city: Torrance
country: United States
facility: UCLA Harbor Medical Center
state: California
city: Bridgeport
country: United States
facility: Department of Cardiology at Bridgeport Hospital
state: Connecticut
city: New Haven
country: United States
facility: Cardiology Associates of New Haven
state: Connecticut
city: Washington
country: United States
facility: Howard University Hospital
state: District of Columbia
city: Tamarac
country: United States
facility: Integrated Care / Cardiovascular Consultants of South Florida
state: Florida
city: Atlanta
country: United States
facility: Piedmont Hospital Research Institute
state: Georgia
city: Atlanta
country: United States
facility: Morehouse School of Medicine/Cardiology
state: Georgia
city: Atlanta
country: United States
facility: Emory University
state: Georgia
city: Honolulu
country: United States
facility: The Queen's Medical Center
state: Hawaii
city: Maywood
country: United States
facility: Loyola University Medical Center
state: Illinois
city: Indianapolis
country: United States
facility: Indiana Heart Physicians
state: Indiana
city: Iowa City
country: United States
facility: Iowa City Heart Center
state: Iowa
city: Houma
country: United States
facility: Chabert Medical Center
state: Louisiana
city: Marrero
country: United States
facility: Heart Clinic of Louisiana
state: Louisiana
city: Tupelo
country: United States
facility: Cardiology Associates, LLC
state: Mississippi
city: Kansas City
country: United States
facility: Truman Medical Center/Cardiology
state: Missouri
city: Kansas City
country: United States
facility: St. Luke's Hospital / Mid-America Heart Institute
state: Missouri
city: Saint Louis
country: United States
facility: Washington University
state: Missouri
city: Saint Louis
country: United States
facility: Cardiology Diagnostics
state: Missouri
city: Camden
country: United States
facility: Cooper Health System Cardiology
state: New Jersey
city: Hackensack
country: United States
facility: Hackensack University Medical Center
state: New Jersey
city: Paterson
country: United States
facility: St. Joseph's Regional Medical Center / Cardiology Associates
state: New Jersey
city: New York
country: United States
facility: New York University/Cardiology
state: New York
city: Winston-Salem
country: United States
facility: Forsyth Medical Center
state: North Carolina
city: Cleveland
country: United States
facility: MetroHealth Medical Center, Heart & Vascular Center
state: Ohio
city: Dayton
country: United States
facility: The Dayton Heart Center
state: Ohio
city: Portland
country: United States
facility: Oregon Health and Science University
state: Oregon
city: Pittsburgh
country: United States
facility: University of Pittsburgh
state: Pennsylvania
city: Westerly
country: United States
facility: Cardiology Specialists, Ltd.
state: Rhode Island
city: Dallas
country: United States
facility: Baylor University Medical Center
state: Texas
city: Norfolk
country: United States
facility: Sentara Cardiology Consultants, Ltd.
state: Virginia
Study officials:
affiliation: Yale University
name: Harlan M. Krumholz, M.D.
role: PRINCIPAL_INVESTIGATOR
affiliation: Yale University
name: Sarwat I Chaudhry, MD
role: STUDY_DIRECTOR
affiliation: Yale University
name: Jennifer Mattera, MPH
role: STUDY_DIRECTOR
References:
citation: Jayaram NM, Khariton Y, Krumholz HM, Chaudhry SI, Mattera J, Tang F, Herrin J, Hodshon B, Spertus JA. Impact of Telemonitoring on Health Status. Circ Cardiovasc Qual Outcomes. 2017 Dec;10(12):e004148. doi: 10.1161/CIRCOUTCOMES.117.004148.
citation: Steventon A, Chaudhry SI, Lin Z, Mattera JA, Krumholz HM. Assessing the reliability of self-reported weight for the management of heart failure: application of fraud detection methods to a randomised trial of telemonitoring. BMC Med Inform Decis Mak. 2017 Apr 18;17(1):43. doi: 10.1186/s12911-017-0426-4.
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.
citation: Krumholz HM, Chaudhry SI, Spertus JA, Mattera JA, Hodshon B, Herrin J. Do Non-Clinical Factors Improve Prediction of Readmission Risk?: Results From the Tele-HF Study. JACC Heart Fail. 2016 Jan;4(1):12-20. doi: 10.1016/j.jchf.2015.07.017. Epub 2015 Dec 2.
citation: Qian F, Parzynski CS, Chaudhry SI, Hannan EL, Shaw BA, Spertus JA, Krumholz HM. Racial Differences in Heart Failure Outcomes: Evidence From the Tele-HF Trial (Telemonitoring to Improve Heart Failure Outcomes). JACC Heart Fail. 2015 Jul;3(7):531-538. doi: 10.1016/j.jchf.2015.03.005.
citation: Bikdeli B, Wayda B, Bao H, Ross JS, Xu X, Chaudhry SI, Spertus JA, Bernheim SM, Lindenauer PK, Krumholz HM. Place of residence and outcomes of patients with heart failure: analysis from the telemonitoring to improve heart failure outcomes trial. Circ Cardiovasc Qual Outcomes. 2014 Sep;7(5):749-56. doi: 10.1161/CIRCOUTCOMES.113.000911. Epub 2014 Jul 29.
citation: Chaudhry SI, Mattera JA, Curtis JP, Spertus JA, Herrin J, Lin Z, Phillips CO, Hodshon BV, Cooper LS, Krumholz HM. Telemonitoring in patients with heart failure. N Engl J Med. 2010 Dec 9;363(24):2301-9. doi: 10.1056/NEJMoa1010029. Epub 2010 Nov 16.