Valvular Heart Disease, Pericardial Disease, Cardiomyopathies
Conditions
Keywords
Structural heart disease, Photoplethysmography, Ejection Fraction
Brief summary
The investigators are aiming to investigate the association between ejection fraction (EF) determined by echocardiography and signals obtained from Photoplethysmography (PPG) in the general population. The investigators are also aiming to investigate the association between blood pressure and signals obtained from PPG in the general population. Finally, the investigators are also aiming to investigate the association between signals obtained from PPG in the general population to cardioechographic findings such as, valvular heart disease, structural heart diseases, cardiomyopathies, pericardial disease etc.
Detailed description
The investigators are proposing a cross-sectional cohort study, where any individual, 18 years old or older, scheduled for an echocardiography at Tulane University's outpatient cardiology clinic can be included. After consenting participants, information on their demographics, personal health habits, medications, and medical history will be collected via reviewing, their electronic medical health records. All participants will undergo an echocardiography as part of their routine medical care by their treating physician. Important echocardiographic findings will be noted such as, EF, valvular heart disease, pericardial disease, cardiomyopathies and structural heart disease. Additionally, PPG recordings will be collected simultaneously while performing echography using the Biostrap wristband. The investigators will analyze the association between each echographic imaging feature, and the PPG waveform obtained. PPG waveform features will be used to develop a predictive model capable of calculating an estimate of ventricular ejection fraction without echocardiography. All echocardiograms will be performed by experienced operators at Tulane Medical Center facilities. The study will occur at a single study site Tulane Medical Center and clinics, with appropriate facilities and equipment to conduct the study accurately and safely. An experienced echocardiogram technician or cardiology fellow will collect the PPG recordings using the Biostrap application. De-identified echocardiogram reports, including information on LVEF will be matched with the participant's pulse report.
Interventions
DIAGNOSTIC_TESTPhotoplethysmography
Photoplethysmography (PPG) is a non-invasive technique that can detect volumetric changes in the peripheral blood. It only requires skin contact and relies on light absorption. PPG is a good indicator of the cardiovascular dynamics in the human body. PPG has been incorporated into various new smartphones and wearable devices such as, smartwatches and fitness wristbands making it readily available to all consumers. This sensor can record biometrics such as, heart rate, blood pressure, oxygen saturation, etc.
Sponsors
Tulane University
Study design
Observational model
OTHER
Time perspective
CROSS_SECTIONAL
Eligibility
Sex/Gender
ALL
Age
18 Years to 120 Years
Healthy volunteers
Yes
Inclusion criteria
* Male or Female aged18 years or older. * Patients scheduled to undergo an echocardiogram at Tulane Medical Center.
Exclusion criteria
* Participants under 18 years of age. * Participants with cognitive impairments. * Participants with a physical inability to wear the Biostrap during the echocardiogram. * Individuals who cannot read, speak, and/or understand English.
Design outcomes
Primary
| Measure | Time frame | Description |
|---|---|---|
| Association between obtained PPG waveforms and recorded ejection (EF) fractions. | Day 1 | Patients who will undergo echocardiogram will wear the biostrap watch and then the PPG waveform will be obtained. Then, the ejection fraction will be collected from the echocardiogram report. Then, the Artificial Intelligence (AI) staff member will do a predictive model to try and estimate the EF based on the PPG waveform. |
Secondary
| Measure | Time frame | Description |
|---|---|---|
| Association between blood pressure and PPG waveforms | Day1 | The investigators will record the blood pressure of the patient twice during their visit. Then, the Artificial Intelligence (AI) staff member will do a predictive model to try and estimate the blood pressure based on the PPG waveform. |
Countries
United States
Contacts
CONTACTMayana Bsoul, MD
CONTACTKunal Sameer, MD, MHA
PRINCIPAL_INVESTIGATORNassir Marrouche, MD
Tulane University
Outcome results
None listed