Clinical Trial Details
— Status: Completed
Administrative data
| NCT number |
NCT06237998 |
| Other study ID # |
Sahlgrenska UH |
| Secondary ID |
|
| Status |
Completed |
| Phase |
|
| First received |
|
| Last updated |
|
| Start date |
April 15, 2020 |
| Est. completion date |
December 31, 2023 |
Study information
| Verified date |
February 2024 |
| Source |
Sahlgrenska University Hospital, Sweden |
| Contact |
n/a |
| Is FDA regulated |
No |
| Health authority |
|
| Study type |
Observational
|
Clinical Trial Summary
Heart failure is a common and serious disease responsible for significant healthcare costs
and the need of hospitalizastions. The course of the disease is characterized by periods of
progressive deterioration with repeated hospital admissions, especially in the final stages
of life. Telemedical self-monitoring is a promising alternative for remote monitoring that
can provide individualized treatment, smooth titration of medications and reduce hospital
stays. However, the evidence for its benefits is limited, which requires further research.
Our hypotheses are that self-monitoring in heart failure can:
1. Reduce avoidable inpatient care and mortality.
2. Optimize the escalation of medications to optimal medical therapy.
3. Increase self-care and security.
4. Improve the prediction of deterioration in heart failure. Work Plan: We will compare six
months of telemedical monitoring with standard care, and integrate telemedical data with
electronic health records (EHR) for analysis and development of prognostic models for
clinical outcomes (data collection is ongoing). Consecutive heart failure patients
(target 300) will receive digital equipment for reporting vital parameters, experiences,
and symptoms over six months. Medication adjustments are made remotely, and physical
visits as needed. Data on mortality, healthcare needs, and health economics will be
collected over two years after the monitoring period. We plan to retrieve a matched
control population from the Swedish heart failure registry (SwedeHF). Telemonitoring
data and EHR will be analyzed with traditional regression models and machine learning
for identifying predictive factors for i) death, ii) readmission for heart failure or
other cardiovascular disease.
Significance: The study can contribute to more cost-effective, patient-centered, and
medically purposeful care of heart failure.
Description:
Heart failure (HF) affects 2% of the population, rising to 10% in individuals over 80 years,
causing considerable healthcare costs and suffering. In newly diagnosed HF titration of
medication to optimal medical therapy (OMT) usually requires multiple physical visits in
specialized clinics. Recent advances have enabled telemonitoring, wireless transmission of
sensor-generated health data as an alternative which need scientific evaluation.
HF is characterized by periods of stability followed by episodic deterioration, with reduced
quality of life (QoL) and hospitalizations. Decline in vital signs appear long before
symptoms occur, successively leading to a point when hospitalization cannot be avoided.
Telemonitoring allows for consistent monitoring of vital parameters, which potentially may
guide treatment and prevent hospitalizations. It also promotes patient empowerment and
personalized treatment. The European Society of Cardiology's (ESC) guidelines for HF, 2021
calls for further research on real world telemedicine data to optimize patient selection,
equipment, and protocols. The Swedish national HF register, SwedeHF, contains detailed data
on the majority of acute HF patients, providing an excellent opportunity to generate a well
characterized matched real world population, including yearly up-dates of clinical
information. In addition, electronic health records (EHRs) are an increasingly important
source for studies of routinely collected healthcare data, offering granular real world data
for research and prognostic modeling.
Work plan: Can RPM/telemonitoring for HF provide effective, safe and cost-efficient care with
high patient satisfaction? We will compare six months of telemonitoring intervention with
standard care to study clinical outcomes, patient experience and health economics (data
collection in progress).
Participants: Consecutive HF patients (≥18 years) at specialized HF clinics are being
recruited for intervention. Inclusion criteria are new or worsening HF in patients requiring
monitoring due to a) titration of medication to OMT or b) decompensation requiring regular
physical monitoring.
Intervention: Patients receive equipment for report of vital signs and patient reported
experience measures (PREMS), automatically transmitted via smartphone/bluetooth for six
months (possible extension to 12 months in case of instability). A digital smartphone
application is used for registration and transmission of data, including modes for
asynchronous (chat) communication with the healthcare provider. Planned titration to OMT or
diuretic adjustments may be done remotely depending on clinical assessment. The digital
sensors include an electronic scale, pulse and blood pressure monitor for daily measurements
by the patient. The healthcare provider is notified of abnormal values, with services
provided during office hours. Physical visits will be planned on a need-to basis. A care plan
for target OMT or adjustments of diuretic treatment is established.
A matched control population (5:1, pseudonymized data) will be obtained from the SwedeHF
registry (national heart failure registry).
Data at baseline and outcomes is collected from medical records, questionnaires, and national
registers:
At baseline from medical records (risk factors, comorbidities, laboratory data, and clinical
findings). Psychometric rating scales are administered weekly and on deterioration. General
health status, QoL questionnaires (EQ5D), assessments of comfort with the technology,
satisfaction with the healthcare provider and method, validated protocols for evaluation of
self-efficacy (Swedish version of the general self-efficacy scale), and trust in the
healthcare system (Sense of security in care) will be completed according to schedule. The
questionnaires are distributed via the software solution or by 1177, and SwedeHF is updated
with yearly clinical data. Final data will be collected after 2 years. Health care contacts
will be measured by national registers: Inpatient Register, Outpatient Register, SwedeHF, and
the Cause of Death Register. Clinical data is registered in a web-based case record form
(eCRF) via REDCap, a secure web-based database management application, compiling variables
corresponding to SwedeHF data, which includes variables not automatically identified from
medical records such as: type of HF, date of first diagnosis, LVEF and NYHA at inclusion,
etiology, medications, dosages at start of intervention and OMT, graded symptoms of
tiredness, dyspnea och swelling and estimated overall QoL (1-100%). Economic data will be
collected from administrative systems and managerial information from interviews with staff
and management using predefined forms.
Outcomes until 2 years include: mortality, healthcare needs (re-hospitalizations, days
hospitalized, out-patient visits), PREMs, time to and dosages at OMT. Health economic
outcomes are costs (direct, indirect), efficiency (technical and allocative) and description
of management systems. Outcome measures as previously outlined will be combined with EHR data
including structured and unstructured clinical information and metadata tracing the
trajectory of HF over time.
Analyses: Data will be analyzed using traditional regression models and machine learning
techniques aiming to identify predictive factors for i) death, ii) rehospitalization for HF
or other cardiovascular disease.
Clinical data from the intervention population will be matched (5:1) for age, sex, LVEF and
duration of HF, to a pseudonymized population identified from SwedeHF. Data will be
summarized and presented using traditional statistical methods, linear and logistic
regression, and survival analysis. Analysis models for the main research questions will be
adjusted for possible confounders.
For the telemedicine study, based on a calculation with 80% power, significance level 0.05,
and an intervention effect of 20% (death and unplanned hospitalization) over 2 years of
follow-up, a sample size of 300 is required. The effect measure is the composite "time to
hospitalization due to heart failure or death." The expected frequency of events is based on
outcomes in a heart failure database from 1987-2010 of 361,000 patients.
