Heart Failure
Conditions
Keywords
Telemedicine, Observational study, Registry, Multicenter
Brief summary
Telemedicine is gradually becoming accepted in heart failure (HF) management. Meta-analyses show a positive effect of telemedicine on hospital admission, length of stay, mortality, and costs. However, the magnitude of the effect is heterogeneous because of the variety in the HF population using telemedicine, components of telemedicine, and variety in considered costs. Despite the lack of clear guidance how to implement telemedicine within routine HF management, implementation of telemedicine is advocated by payers, private companies, and patient organizations. In this nationwide study the investigators aim to identify in which subgroup of HF patients telemedicine is (cost-)effective, and which intervention components of telemedicine are most (cost-)effective.
Detailed description
The objectives of the study are to examine: 1. which HF patient characteristics are related to an increase in number of days spent outside the hospital within one year of follow-up, when telemedicine is part of regular HF care compared to regular HF care alone? 2. which HF patient characteristics are related to cost-effectiveness when telemedicine is part of regular care compared to regular HF care alone? 3. which components of telemedicine as part of regular HF care lead to an increase in number of days spent outside the hospital within one year of follow-up? 4. which components of telemedicine as part of regular HF care are cost-effective? The main focus of this study is on patient-related subgroup analyses with telemedicine. The patient-related subgroups are identified by a systematic literature review of randomized-controlled trials of telemedicine: (1) age, (2) severity of HF (NYHA class at baseline), (3) sex (female compared to male), (4) socio-economic status (SES) (HF patients with higher SES compared to lower SES), (5) presence of depression, (6) Type of heart failure (LVEF: HFrEF, HFmrEF, HFpEF), (7) presence of atrial fibrillation (AF). In an additional analysis, (8) heterogeneity across time of diagnosis will be explored (recently diagnosed compared to not recently diagnosed). To answer the four research questions a RELEASE-HF database will be set up. The RELEASE-HF database will be composed from various data sources: 1. National Heart Failure Registry (abbreviated as Registry; a patient registry), 2. Interviews with clinicians about telemedicine features on hospital level, 3. Interviews with finance department staff about costs in HF care (including telemedicine use), 4. Electronic Health Record (EHR) data about telemedicine (including supplier system data) 5. External national registries and/or databases as Statistics Netherlands (CBS), declaration data (Vektis), Dutch Hospital Data (DHD) or PHARMO.
Interventions
OTHERUse of telemedicine
Use of telemedicine (any type: telephone only, non-invasive, implantable-cardioverter-defibrillator-based, invasive) in heart failure management
Sponsors
UMC Utrecht
Study design
Observational model
COHORT
Time perspective
PROSPECTIVE
Eligibility
Sex/Gender
ALL
Healthy volunteers
No
Inclusion criteria
* All heart failure patients newly admitted to a hospital outpatient clinic for HF * All phenotypes of heart failure that meet the ESC 2021 guideline * Patient who has been diagnosed with heart failure in a setting other than the one where the patient currently presents (primary, secondary or tertiary care)
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Number of days spent outside the hospital within one year of follow-up | 12 months | The number of days will be derived from the number of hospital days and mortality status between follow-up moments. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Change from baseline in functional status at 12 months | Baseline, 12 months | New York Heart Association classification class (NYHA): a functional classification of patients, based on severity of symptoms and physical activity, with specific attention to fatigue, palpitation, and dyspnea. NYHA exist of four classifacation. A higher class means a patient has more physcial problems caused by heart failure. Class I: No limitation of physical activity. Ordinary physical activity does not cause undue fatigue, palpitation, dyspnea (shortness of breath). Class II: Slight limitation of physical activity. Comfortable at rest. Ordinary physical activity results in fatigue, palpitation, dyspnea (shortness of breath). Class III: Marked limitation of physical activity. Comfortable at rest. Less than ordinary activity causes fatigue, palpitation, or dyspnea. Class IV: Unable to carry on any physical activity without discomfort. Symptoms of heart failure at rest. If any physical activity is undertaken, discomfort increases. |
| Change from baseline in health status at 12 months | Baseline, 12 months | SF-36 or SF-12 questionnaire (subset of SF-36): a validated patient-reported survey of patient health. Both questionnaires consist of eight sections with scores: vitality, physical functioning, bodily pain, general health perceptions, physical role functioning, emotional role functioning, social role functioning, and mental health. Each score is transformed into a 0-100 scale on the assumption that each question carries equal weight. The lower the score the lower the health status. |
| All-cause mortality | 12 months | Mortality status, determined after verification at the Personal Records Database (in Dutch: Basisregistratie Personen). |
| Outpatient visits | 12 months | Number of outpatient visits (planned/unplanned). |
| Costs | 12 months | Costs estimated from patient, disease, and treatment characteristics. Information taken into account includes medication use, whether the patient underwent cardiac interventions (e.g., pacemaker implantation, percutaneous coronary intervention), use of telemedicine, hospital admission days, visits to the outpatient clinic, visits to the emergency room, admission days at an intensive care unit, visits at the GP related to HF |
| Change from baseline in QoL (Quality of Life) at 12 months | Baseline, 12 months | SF-36 or SF-12 questionnaire. Quality Adjusted Life Years (QALY) will be calculated based on the SF-6D: a model in which directly a single, preference-based score can be calculated for the SF-36 and SF-12. Scores range from 0.0 (worst health state) to 1.0 (best health state). |
Countries
Netherlands
Contacts
Primary ContactJorna van Eijk, MSc
Outcome results
None listed