Heart Failure Clinical Trial
Official title:
Bayesian Hemodynamics Model for Personalized Monitoring of Congestive Heart Failure Patients - Translating Physician's Reasoning Into Computational Models, Part II
Heart failure (HF) is a serious and challenging syndrome. Globally 26 million people are living with this chronic disease and the prevalence is still increasing. Besides this growing number in prevalence, HF is also responsible for almost 1 million hospitalizations a year in the US and in Europe. Consequently, this has a major economic impact especially due to recurrent admissions of these patients. Adequate prediction of decompensation could prevent (un)necessary admissions as a result of heart failure. Philips is developing a Bayesian Hemodynamics model for general practitioners. This model uses different observables, which can be measured at home. The outcome of the model could be used as an aid in clinical decision making in HF patients.
Heart failure (HF) is a world-wide problem. At the moment 26 million people are living with
this chronic disease and the prevalence is still increasing. Besides this growing number in
prevalence, HF is also responsible for almost 1 million hospitalizations a year in the US and
in Europe. Consequently, this has also a major economic impact especially due to recurrent
admissions of these patients. Adequate prediction of decompensation could prevent
(un)necessary admissions as a result of heart failure. Philips is developing a Bayesian model
for chronic heart failure, enabling monitoring of patients with heart failure in the hospital
and at home. An important characteristic of such a Bayesian model is that it is a
knowledge-based model, in contrast to data-mining based models, and requires only a few
patient data to get started (10-20 patients). Another important characteristic is that these
'knowledge-based models' are applicable in any setting, again in contrast to data-mining
based models. This makes the proposed model different from conventional data-mining
approaches to modelling. During a hospital admission, the model will be "filled in" with
personal patient data. Subsequently, during the rest of the hospital stay or after release
from the hospital, a number of symptoms and lab measurement variables ("observables"), will
be the input for the model. The output of the model (the result) will be a probability of
improvement (versus worsening) of the condition of the patient or the status of the heart
failure condition on a scale (from 1-10). The model can deal with less input variables than
the number it has been "personalized" with. With less input measurements, naturally the
reliability of the result will be reduced. This modelling approach basically captures the
clinical way of thinking into a model. If interpreted in the right way using smart Bayesian
modelling, the GP or geriatrician will be able to monitor and treat the majority of heart
failure patients. This fits in current thinking to reduce HC costs by keeping patients at
home and out of the hospital.
The clinical investigation is designed to evaluate whether the outcome of the "Bayesian
Hemodynamics model" compares with the cardiologist's status assessment. The purpose of this
study is to validate the computer model that has been developed to assess the status of a
heart failure patient. With the model, the investigators aim to support healthcare
professionals with early detection of deterioration of heart failure patients and with
providing the right treatment when it is needed. If successful, this could help heart failure
patients to stay at home longer and reduce hospital admissions.
The clinical literature review is documented in report, Personalized Heart Failure Monitoring
using a Bayesian network, Anja v.d. Stolpe, Wim Verhaegh, Folke Noertemann, PR-TN 2017/00180.
This clinical investigation is needed, because no complete datasets, including ground truth
assessments by cardiologists, are available, neither in existing databases, nor in clinical
literature.
The clinical investigation needs to be performed on a population that fulfills the
inclusion/exclusion criteria described in Chapter 6, because the "Bayesian Hemodynamics
model" is only valid for these cases.
;
Status | Clinical Trial | Phase | |
---|---|---|---|
Recruiting |
NCT05650307 -
CV Imaging of Metabolic Interventions
|
||
Recruiting |
NCT05654272 -
Development of CIRC Technologies
|
||
Recruiting |
NCT05196659 -
Collaborative Quality Improvement (C-QIP) Study
|
N/A | |
Active, not recruiting |
NCT05896904 -
Clinical Comparison of Patients With Transthyretin Cardiac Amyloidosis and Patients With Heart Failure With Reduced Ejection Fraction
|
N/A | |
Completed |
NCT05077293 -
Building Electronic Tools To Enhance and Reinforce Cardiovascular Recommendations - Heart Failure
|
||
Recruiting |
NCT05631275 -
The Role of Bioimpedance Analysis in Patients With Chronic Heart Failure and Systolic Ventricular Dysfunction
|
||
Enrolling by invitation |
NCT05564572 -
Randomized Implementation of Routine Patient-Reported Health Status Assessment Among Heart Failure Patients in Stanford Cardiology
|
N/A | |
Enrolling by invitation |
NCT05009706 -
Self-care in Older Frail Persons With Heart Failure Intervention
|
N/A | |
Recruiting |
NCT04177199 -
What is the Workload Burden Associated With Using the Triage HF+ Care Pathway?
|
||
Terminated |
NCT03615469 -
Building Strength Through Rehabilitation for Heart Failure Patients (BISTRO-STUDY)
|
N/A | |
Recruiting |
NCT06340048 -
Epicardial Injection of hiPSC-CMs to Treat Severe Chronic Ischemic Heart Failure
|
Phase 1/Phase 2 | |
Recruiting |
NCT05679713 -
Next-generation, Integrative, and Personalized Risk Assessment to Prevent Recurrent Heart Failure Events: the ORACLE Study
|
||
Completed |
NCT04254328 -
The Effectiveness of Nintendo Wii Fit and Inspiratory Muscle Training in Older Patients With Heart Failure
|
N/A | |
Completed |
NCT03549169 -
Decision Making for the Management the Symptoms in Adults of Heart Failure
|
N/A | |
Recruiting |
NCT05572814 -
Transform: Teaching, Technology, and Teams
|
N/A | |
Enrolling by invitation |
NCT05538611 -
Effect Evaluation of Chain Quality Control Management on Patients With Heart Failure
|
||
Recruiting |
NCT04262830 -
Cancer Therapy Effects on the Heart
|
||
Completed |
NCT06026683 -
Conduction System Stimulation to Avoid Left Ventricle Dysfunction
|
N/A | |
Withdrawn |
NCT03091998 -
Subcu Administration of CD-NP in Heart Failure Patients With Left Ventricular Assist Device Support
|
Phase 1 | |
Recruiting |
NCT05564689 -
Absolute Coronary Flow in Patients With Heart Failure With Reduced Ejection Fraction and Left Bundle Branch Block With Cardiac Resynchronization Therapy
|