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Bayesian Hemodynamics Model for Personalized Monitoring of Congestive Heart Failure Patients

NCT03575533 · Status: UNKNOWN · Type: OBSERVATIONAL · Enrollment: 20

Last updated 2019-02-28

No results posted yet for this study

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.

Conditions

Interventions

DIAGNOSTIC_TEST

Bayesian network 'Sherlock'

Primary objective is to validate the Bayesian Hemodynamics model 'Sherlock'. Criterion for validation is a match between Sherlock's estimate of the Hemodynamic status of a patient and a ground truth based on a cardiologist's judgement.

Sponsors & Collaborators

  • Philips Healthcare

    collaborator INDUSTRY

  • Leiden University Medical Center

    lead OTHER

Eligibility

Min Age
45 Years
Sex
ALL
Healthy Volunteers
No

Timeline & Regulatory

Start
2019-01-01
Primary Completion
2019-09-01
Completion
2020-01-01

Countries

  • Netherlands

Study Locations

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Entities

Diseases

Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT03575533 on ClinicalTrials.gov