Clinical Trials Logo

Clinical Trial Summary

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.


Clinical Trial Description

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. ;


Study Design


Related Conditions & MeSH terms


NCT number NCT03575533
Study type Observational
Source Leiden University Medical Center
Contact
Status Not yet recruiting
Phase
Start date August 1, 2018
Completion date January 1, 2020

See also
  Status Clinical Trial Phase
Active, not recruiting NCT03549169 - Decision Making for the Management the Symptoms in Adults of Heart Failure N/A
Withdrawn NCT03091998 - Subcu Administration of CD-NP in Heart Failure Patients With Left Ventricular Assist Device Support Phase 1
Recruiting NCT03087084 - RESpiration deTection From Implanted Cardiac Devices in Subjects With Heart Failure (REST-HF) N/A
Recruiting NCT03300791 - Predictive Models of Readmission in Heart Failure N/A
Recruiting NCT03294512 - Pilot and Feasibility Study of a MAWDS (Medications, Activity, Weight, Diet and Symptoms) Heart Failure Mobile Platform N/A
Recruiting NCT03281122 - A Study of BMS-986224 in Healthy Subjects and Heart Failure Patients With Reduced Ejection Fraction Phase 1
Recruiting NCT02275819 - Exercise Training in Heart Failure: Changes in Cardiac Structure and Function N/A
Completed NCT03238729 - Proof-of-Concept Study of Heart Habits Application for Patients With Heart Failure N/A
Not yet recruiting NCT03360448 - AC6 Gene Transfer in Patients With Reduced Left Ventricular Ejection Fraction Heart Failure Phase 3
Not yet recruiting NCT03587064 - Comparison of CRT-D and CRT-DX Systems (CRT-NEXT) N/A
Not yet recruiting NCT02784912 - Biomarkers in Risk Stratification of Sustainted Ventricular Tachycardia or Electrical Storm After Ablation N/A
Recruiting NCT02921607 - Development of Scalable New Model(s) Focused on Care Co-ordination and Care Provision for Medically Complex, Co-morbid Chronic Disease Patient Segments Focusing on Heart Failure N/A
Recruiting NCT03013270 - Aerobic, Resistance, Inspiratory Training Outcomes in Heart Failure N/A
Recruiting NCT02674438 - Comparison of Outcomes and Access to Care for Heart Failure Trial Phase 3
Recruiting NCT02877914 - China PEACE 5r-HF Study N/A
Recruiting NCT02823795 - The Supporting Patient Activation in Transition to Home Intervention N/A
Recruiting NCT02922478 - Role of Comorbidities in Chronic Heart Failure Study N/A
Recruiting NCT02911493 - Reducing Sedentary Time in Patients With Heart Failure N/A
Recruiting NCT02713126 - Inorganic Nitrite to Amplify the Benefits and Tolerability of Exercise Training in HFpEF (INABLE-Training) Phase 2
Not yet recruiting NCT02899364 - Sodium Thiosulfate to Preserve Cardiac Function in STEMI Phase 2