Phono- and Electrocardiogram Assisted Detection of Valvular Disease

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POLESTAR Trial – An International Multi-center Early Discharge TAVI Program REPRISE EDGE 29 mm EU Study The CONFIDENCE Registry Interest of Cardiac Computed Tomography to Optimize and Improve the Procedure of TAVI Transapical Implantation of the Medtronic Engager Transcatheter Aortic Valve Implantation System – The Engager European Pivotal Trial Post-Market Follow-up of the CENTERA Transcatheter Heart Valve System SecOnd-generation seLf-expandable Versus Balloon-expandable Valves and gEneral Versus Local Anesthesia in TAVI Balloon Aortic Valvuloplasty During Surgical Aortic Valve Replacement The PROTEMBO SF Trial Adjuvant Radiation in Aortic Valvuloplasty – ARAVA Value of Oral Phytate (InsP6) in the Prevention of Progression of the Cardiovascular Calcifications Supra-Annular vs. Annular ValvEs for Small Annuli Heart Leaflet Technologies Valve Study A Study to Evaluate the Neuro-embolic Consequences of TAVR Femoral vs Radial Approach and MRI Evaluation of Strokes Mortality Post-TAVI and Correlation With Haemodynamic Parameters. 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Insertable Cardiac Monitoring Device Reactive Oxygen Species Following Aortic Valve Replacement Biological Factors Associated With Subclinical Valvular Thrombosis Hemodynamic Comparison of Tissue Aortic Valves Volume Challenge Added to DSE in the Diagnosis of Severe LFLGAS Impact of Perioperative Treatment With Intravenous Glutamine on Myocardial Protection in Cardiac Surgery Patients With Aortic Stenosis The Effects of High Spinal Anesthesia on Heart Function, Stress Response and Pain Control in Aortic Valve Surgery A Clinical Trial of Transcatheter Aortic Valves in Dialysis Patients (Japan) Erythropoietin + Iron Therapy for Anemic Patients Undergoing Aortic Valve Replacement A Study Evaluating the Effects of Ataciguat (HMR1766) on Aortic Valve Calcification Efficacy of Angiotensin Receptor Blocker Following aortIc Valve Intervention for Aortic STenOsis: a Randomized mulTi-cEntric Double-blind Phase II Study Prehabilitation to Improve Functional and Clinical Outcomes in Patients With 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Concomitant Coronary Artery Disease in Patients Undergoing TAVI Treatment of Aortic Stenosis in Brazil: Cost-Utility Analysis of TAVI vs SAVR Clinical Evaluation of MDT-2111 in Subjects With Small Aortic Annuli and Symptomatic Severe Aortic Stenosis Valvuloplasty Scoring Balloon Catheter First-in-Man Study Effect of Angiotensin II Receptor Blockers (ARB) on Left Ventricular Reverse Remodelling After Aortic Valve Replacement in Severe Valvular Aortic Stenosis Surgical Treatment of Aortic Stenosis With a Next Generation Surgical Aortic Valve Registry to Evaluate the Impact of a Valve Coordinator on Aortic Stenosis / TAVI Outcome – (German Pilot) Quality of Care in AS IMPULSE Study Biomarkers in Aortic Stenosis – B.A.S.S. 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Evaluated Via Modern Ballistocardiography and Seismocardiography Study of Platelet Activation by Severe Aortic Stenosis and Its Correction by Transcatheter Aortic Valve Implantation Sizing-sTrategy of Bicuspid AoRtic Valve Stenosis With Transcatheter Self-expandable Valve The Plasma Serotonin and Aortic Stenosis: a Pilot Study. 18F-NaF Uptake and Aortic Stenosis Progression Cardiovascular Rehabilitation in Patients With Severe Aortic Stenosis Submitted to Valvar Correction Aortic Stenosis and PhosphodiEsterase Type 5 iNhibition (ASPEN): A Pilot Study Clinical Outcome of Patients With Low-gradient Severe Aortic Stenosis Ultrasonic Markers for Myocardial Fibrosis and Prognosis in Aortic Stenosis Fetal Intervention for Aortic Stenosis and Evolving Hypoplastic Left Heart Syndrome The PARTNER 3 – Trial – The Safety and Effectiveness of the SAPIEN 3 Transcatheter Heart Valve in Low Risk Patients With Aortic Stenosis Ventricular Remodeling In Patients With Aortic Stenosis Assessed 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Brief Title

Phono- and Electrocardiogram Assisted Detection of Valvular Disease

Official Title

Phono- and Electrocardiogram Assisted Detection of Valvular Disease

Brief Summary

      The diagnosis of valvular heart disease (VHD), or its absence, invariably requires cardiac
      imaging. A familiar and inexpensive tool to assist in the diagnosis or exclusion of
      significant VHD could both expedite access to life-saving therapies and reduce the need for
      costly testing. The FDA-approved Eko Duo device consists of a digital stethoscope and a
      single-lead electrocardiogram (ECG), which wirelessly pairs with the Eko Mobile application
      to allow for simultaneous recording and visualization of phono- and electrocardiograms. These
      features uniquely situate this device to accumulate large sets of auscultatory data on
      patients both with and without VHD.

      In this study, the investigators seek to develop an automated system to identify VHD by
      phono- and electrocardiogram. Specifically, the investigators will attempt to develop machine
      learning algorithms to learn the phonocardiograms of patients with clinically important
      aortic stenosis (AS) or mitral regurgitation (MR), and then task the algorithms to identify
      subjects with clinically important VHD, as identified by a gold standard, from naïve
      phonocardiograms. The investigators anticipate that the study has the potential to
      revolutionize the diagnosis of VHD by providing a more accurate substitute to traditional
      auscultation.
    

Detailed Description

      Phono- and Electrocardiogram Assisted Detection of Valvular Disease (PEA-Valve Study)

      Specific aim(s) Aim 1: Can a machine learning algorithm derived from simultaneous phono- and
      electrocardiogram recordings reliably diagnose clinically important aortic stenosis?

      Aim 2: Can a machine learning algorithm derived from simultaneous phono- and
      electrocardiogram recordings reliably diagnose clinically important mitral regurgitation?

      Significance Valvular heart disease (VHD) is a common global health problem, with
      population-based studies showing a prevalence of 10% for aortic stenosis (AS) and 20% for
      mitral regurgitation (MR). New surgical and interventional advances allow for the treatment
      of patients at an older age or whose risk of intervention would previously have been
      untenable. Given that the incidence of both MR and AS increases with increasing age, there is
      a growing need to identify these conditions so as to offer disease-altering therapies.

      In current clinical practice, the diagnosis of VHD relies heavily on echocardiography. This,
      in turn, requires both a referral from a provider with a clinical suspicion for VHD,
      typically from an abnormality on auscultation, as well as access to the echocardiogram
      itself. MR and AS both result in reliably reproducible auscultatory findings: holosystolic
      and systolic crescendo-decrescendo murmurs, respectively. Yet despite this, auscultation as a
      diagnostic tool is notoriously poor: its accuracy to detect MR and AS ranges only from 5-40%.
      These factors all lead to concerns for underdiagnosis of these increasingly treatable
      conditions.

      Here, the investigators will address the needs for both greater access to and improved
      diagnostic accuracy of testing for VHD. The investigators will utilize a combination of
      phonocardiogram (PCG) and single-lead electrocardiogram (ECG) recordings, synced in real-time
      to a secure cloud-based server, using the Eko Duo electronic stethoscope. With these data,
      the investigators will develop and validate a machine learning algorithm to diagnose
      clinically important AS or MR. As the Eko Duo is essentially similar to a traditional
      stethoscope, an iconic tool widely accepted by patients and providers alike, its use to drive
      an automated detection algorithm is both feasible and attractive as a substitute for
      traditional auscultation. Furthermore, by shifting the burden of test interpretation away
      from the clinician and onto the algorithm, the investigators hypothesize that this will
      improve overall diagnostic accuracy.

      Methods Overview of design: Cross-sectional study of all subjects undergoing clinical
      echocardiograms at the UCSF adult echocardiography laboratory

      Study subjects Overview: The investigators will enroll adult subjects undergoing clinical
      echocardiograms at the UCSF Parnassus campus. These subjects will be grouped into derivation
      and validation cohorts sequentially, stratified by case status, so as to reach the expected
      sample size. Such grouping will occur after subject enrollment and data collection.

      Target Population: Adults with either moderate-to-severe to severe AS or moderate-to-severe
      to severe MR (cases) and adults with structurally normal hearts with minimal VHD (controls).
      In a more-stringent, parallel analysis, a target population of controls will be defined as
      having any degree of AS or MR less than moderate-to-severe.

      Accessible Population: Adults meeting the entry criteria undergoing clinical echocardiograms
      at the UCSF echocardiography laboratory amenable to participation.

      Sampling Scheme: The investigators will approach subjects presenting to the adult
      echocardiography laboratory at UCSF Parnassus consecutively. Additionally, the investigators
      will pre-screen subjects for a high likelihood of having AS or MR (based on indication for
      study and prior diagnoses in the APEX medical record) and selectively target their enrollment
      during situations where the enrollment capacity of the study coordinator is saturated.

      Recruitment Strategy: Introduction of study at time of registering for echocardiogram with a
      brochure or flyer, followed by in-person approaching of potential subjects while awaiting the
      clinical echocardiogram.

      Retention Strategy: None. The investigators will retain a master file of the medical record
      numbers to identify contact information in the future if deemed necessary.

      Measurements Overview: The study will focus on two measurements: 1) the gold standard
      assessment of VHD by echocardiogram, as reported by the UCSF echocardiography laboratory. 2)
      30 second simultaneous PCG and single-lead ECG recordings by the Eko Duo device at each of
      the four standard cardiac auscultatory positions, with optional additional recordings with
      the Eko Core device. The study takes advantage of the fact that all clinical echocardiogram
      reports include these valvular assessments.

      Gold Standard: The echocardiogram is accepted as the gold standard for diagnosis of VHD
      severity by the cardiology community. To minimize the burden on the investigators, as well as
      reduce costs, the investigators will take advantage of that all clinical echocardiogram
      reports include assessments of VHD, which will serve as the gold standard. These reports
      follow American Society for Echocardiography (ASE) guidelines, which allow grading of VHD as
      follows: none, mild, moderate, or severe. The UCSF echocardiography laboratory includes
      additional categories of trace, mild-to-moderate, moderate-to-severe, and critical, allowing
      for interpretations where individual parameters within the study conflict. The primary
      measurement will be the final conclusion of severity of VHD for MR or AS, as reported by a
      board-certified cardiologist. The investigators will define "clinically important" VHD as
      that graded moderate-to-severe or worse, as this would encompass all levels of disease which
      could require direct intervention. In addition, the investigators will extract the entire
      echocardiography report, as well as the images of the echocardiogram files, so as to save the
      data for future use as new research questions arise.

      Device Measurements: Recordings of the simultaneous PCG and single-lead ECG will be performed
      for each subject in a standardized manner. Each subject will undergo 30 second recordings
      using the Eko Duo device at the four standard auscultation positions. Observers will be
      trained on the systematic method of obtaining measurements. Time and patient permitting, the
      investigators will also obtain the same recordings using the Eko Core device, which uses the
      same software but does not include ECG recording. As the device will allow visualization of
      the PCG during recording, the observer will get real-time feedback on positioning of the
      device to maximize the quality of the recording at each position. The investigators will plan
      for periodic review of recordings to ensure adequate data quality. The investigators
      anticipate that this flexibility and real-time feedback will improve the generalizability of
      the use of the device to a real-world (i.e. non-study) clinical situation.

      Confounders and Bias: The derivation and validation of the algorithms will occur remotely,
      after the clinical echocardiogram has been performed, and therefore with no effect on the
      outcome of the gold standard. The test may be influenced by the presence of other conditions
      causing systolic murmurs (including VHD other than AS or MR, or congenital heart disease).
      The investigators will include these measures from the echocardiogram report to compare test
      performance in those with and without these other conditions.

      Statistical issues Null Hypothesis: A machine learning algorithm cannot predict the presence
      of clinically important AS or MR.

      Sample Size Justification:

        -  Sensitivity of Algorithm: 90%

        -  Specificity of Algorithm: 90%

        -  Target Likelihood Ratio (+) of Algorithm [LR(R)]: 9 (derived from Sn / [1 - Sp])

        -  Minimum Likelihood Ratio (+) of Algorithm [LR(R)]: 5

        -  Confidence Level = 0.95 (alpha = 0.05)

        -  Confidence Interval [LR(R)]: 5.120-15.820

        -  Sample Size = 110 per group; 330 per cohort (control, AS case, MR case); 660 overall
           (training and validation cohorts)

        -  Summary: Assuming the sensitivity and specificity of the machine learning algorithm for
           detection of clinically important AS or MR are both 0.9, a total sample size of 660 is
           not expected to go below the threshold likelihood ratio of 5.0 in the 95% confidence
           interval in either the derivation or validation cohorts.

        -  Justification of Critical Assumptions: The investigators assume that the algorithm can
           produce a sensitivity and specificity of 90% in detecting aortic stenosis or mitral
           regurgitation compared to hearts with no valvular disease, based upon prior published
           reports using neural networks, using sample sizes of under 100 cases. Furthermore, the
           investigators estimate a minimum likelihood ratio of 5 would be necessary for the test
           to be clinically useful. While this estimate works well for the validation set, the
           number needed for the derivation set is less clear; the estimates above are a
           conservative number. The investigators anticipate training the algorithm after
           enrollment of every 20 cases using a bootstrapping approach; this will provide interim
           test characteristics and help determine the true number needed for the derivation set.
           As this is, in part, a pilot study, identification of the true sensitivity and
           specificity of the test is in itself a valuable result.

      Analysis approach: The investigators will generate ROC curves (plotting Sn vs. 1-Sp) for
      algorithm scores for the validation set. Ultimately the investigators will generate 4 curves:
      two each for MR and AS, using algorithms generated by the primary (defining controls as
      having no greater than mild VHD) and secondary (defining controls as not having
      moderate-to-severe or greater VHD) approaches. Additionally, the investigators anticipate
      performing exploratory, descriptive analyses of the algorithm itself, by identifying clinical
      correlates to the characteristics most heavily weighted in detecting AS or MR.

      Miscellaneous Ethical considerations: No major concerns. Data will be securely stored on
      HIPAA compliant platforms. The study qualifies as minimal risk by UCSF CHR criteria.

      Pretest plans: Prior to study recruitment, study staff will collect data on themselves and
      providers to test the data collection system. During initial subject recruitment, study staff
      will review the process after each day to discuss roadblocks or concerns.

      Data Management Plan: Data from the study will come from two sources. Reports of
      echocardiograms, extracted from the electronic medical record (APEX), will be reviewed by
      study staff to generate the main database of disease characteristics. A master file linking
      subject identifiers with identifiable information, as well as extracted and de-identified
      echocardiogram reports and de-identified raw echocardiogram images will be stored on a secure
      research server used by the Division of Cardiology. Recorded PCG and ECG data (the actual
      study measurements) will be synced in real-time to a secure, HIPAA-compliant, cloud-based
      server managed by Eko Devices. At pre-specified times of algorithm training, the machine
      learning team (coordinated by Eko Devices) will be provided keys to the assignment of subject
      identifier to VHD category.

      Quality control measures: Periodic review of the recorded data will be performed by the study
      PI to ensure appropriate data quality.

      Timetable:

      Contract/Logistics Subject Enrollment Algorithm Development Analysis Publication Overall
      Timetable: 9 Months
    


Study Type

Observational


Primary Outcome

Differentiation of clinically significant aortic stenosis from structurally normal hearts

Secondary Outcome

 Differentiation of clinically significant aortic stenosis from the absence of clinically significant aortic stenosis

Condition

Aortic Valve Stenosis

Intervention

AS Algorithm 1

Study Arms / Comparison Groups

 Control
Description:  Subjects with echocardiographically confirmed valvular disease of less than moderate-to-severe grading with regards to aortic stenosis (AS) and mitral regurgitation (MR). Note that within this cohort will be a sub cohort consisting of subjects with structurally normal hearts, with no greater than mild valvular disease of any valve, no prior valvular intervention, and no evidence of congenital heart disease.

Publications

* Includes publications given by the data provider as well as publications identified by National Clinical Trials Identifier (NCT ID) in Medline.

Recruitment Information


Recruitment Status

Diagnostic Test

Estimated Enrollment

900

Start Date

February 22, 2018

Completion Date

December 31, 2020

Primary Completion Date

December 31, 2020

Eligibility Criteria

        Inclusion Criteria:

          -  Able to provide consent

          -  Undergoing a complete echocardiogram

        Exclusion Criteria:

          -  Refusal to participate
      

Gender

All

Ages

18 Years - N/A

Accepts Healthy Volunteers

Accepts Healthy Volunteers

Contacts

John Chorba, MD, , 

Location Countries

United States

Location Countries

United States

Administrative Informations


NCT ID

NCT03458806

Organization ID

17-21881


Responsible Party

Sponsor

Study Sponsor

University of California, San Francisco

Collaborators

 Eko Devices, Inc.

Study Sponsor

John Chorba, MD, Principal Investigator, University of California, San Francisco


Verification Date

May 2020