Cardiovascular DiseasesASE-INNOVATENCT03713333
Implementing Digital Health in a Learning Health System
A study testing Digital Health Device Diagnostics in people with Cardiovascular Diseases and Hypertension, run by Scripps Health at 1 site in United States.
Trial summary
The need for new models of integrated care that can improve the efficiency of healthcare and reduce the costs are key priorities for health systems across the United States. Treatment costs for patients with at least one chronic medical or cardiovascular condition make up over 4-trillion dollars in spending on… Read more about this study
- Who is it for?
- Ages 18 years and older
- Conditions the study is for
- Cardiovascular Diseases, Hypertension, Heart Failure
- What is being tested?
- Digital Health Device Diagnostics
- Could I receive a placebo?
- No, everyone receives an active treatment
- How many people
- 374 participants
- Where
- 1 siteUnited States
- Study dates
- 20 Oct 2018to 20 Mar 2020
- Phase
- Interventional
- Are there results?
- None posted in the registry
Key requirements
Who can take part, as listed by the sponsor. Only the study team can confirm whether you are eligible, so take this list to your doctor.
- Ages 18 years and older
- All sexes
- No healthy volunteers
- Adult, Older adult
Who may be able to join
- All participants of the ASE 2018 Outreach Event who are at least 18 years old who are referred for a cardiac evaluation
Who cannot join
- Those not willing to consent
Trial locations and contacts
1 site in 1 country.
United States1 site
West Virgina University
Morgantown, West Virginia, 26506
About this study
Official title: Implementation of High Definition Screening Using Handheld Imaging and Digital Health Technologies Within a Learning Health System to Identify Cardiovascular Disease at the Point-of-care: The ASE-INNOVATE Program
The need for new models of integrated care that can improve the efficiency of healthcare and reduce the costs are key priorities for health systems across the United States. Treatment costs for patients with at least one chronic medical or cardiovascular condition make up over 4-trillion dollars in spending on healthcare, with estimations of a population prevalence of 100-million affected individuals within the next decade. Therefore, the management of chronic conditions requires innovative and new implementation methods that improve outcomes, reduce costs, and increase healthcare efficiencies. Digital health, the use of mobile computing and communication technologies as an integral new models of care is seen as one potential solution. Despite the potential applications, there is limited data to support that new technologies improve healthcare outcomes. To do so requires; 1) robust methods to determine the impact of new technologies on healthcare outcomes and costs; and 2) evaluative mechanisms for how new devices are integrated into patient care. In this regard, the proposed clinical trial aims to advance the investigator's knowledge and to demonstrate the pragmatic utilization of new technologies within a learning healthcare system providing services to high-risk patient populations.
Read the detailed description
Objective #1: Determine the effectiveness of handheld imaging and digital health devices on long term health and patient-reported outcomes through pragmatic and randomized clinical trial designs.
Objective #2: Assess the impact of digital health devices and remote patient monitoring (RPM) on measures of healthcare efficiency. Measures of healthcare efficiency directly related to digital health technologies and RPM include: identify which interventions can improve care; define the variations in care and; demonstrate within which patient populations digital health technologies are most effective.
Objective #3: Apply integration methods for handheld imaging and digital health devices used for clinical decisions.
Achieving integration and interoperability-the ability of different information technology systems and software applications to communicate and exchange data with each other-requires identification for precisely how new innovations merge into systems of care and are applied to various practice settings.
- Conditions
- Cardiovascular Diseases, Hypertension, Heart Failure, Atrial Fibrillation, Metabolic Syndrome, Genetic Disease
- Being tested
- Digital Health Device Diagnostics
- Keywords
- digital health, handheld ultrasound, smartphone ECG, mobile health, point of care genomics, big data, artificial intelligence, learning health system
- Medical subject headings
- Cardiovascular Diseases, Hypertension, Heart Failure, Atrial Fibrillation, Metabolic Syndrome, Genetic Diseases, Inborn
What participants receive
What participants receive. In a randomised study each person is assigned to one group by chance.
Digital Health Device DiagnosticsDiagnostic test
Technology-enabled visitations with digital health will include the following devices used at the time of a patient-physician encounter. These findings will be available to the treating physician at the time the visitation and to be used for clinical decisions.
- Handheld imaging - focused echocardiographic examination (Butterfly IQ)
- Smartphone iECG for cardiac rhythm assessments (Alivecor)
- Blood Pressure (CloudDX)
- Oxygen Saturation (CloudDX)
- Weight (CloudDX)
- Point-of-Care Genetic Testing (Phosphorous)
| Study group | Role | What they receive |
|---|---|---|
| Technology-Enabled Visitations | Experimental | Technology-enabled visitations with digital health will include the following devices used at the time of a patient-physician encounter. These findings will be available to the treating physician at the time the visitation and to be used for clinical decisions. Receives: Diagnostic Test: Digital Health Device Diagnostics |
| Standard-Care Visitations | No intervention | Standard-care is defined as the range of services available during usual patient care. Handheld Imaging and digital health screening will be performed in the control group after the patient-physician encounter. As such, patients and physicians will be blinded to the diagnostic findings unless an abnormal finding is detected that requires physician review and triage for further care. |
What is being measured
The primary outcome is the main question the study is designed to answer.
Health Economic Outcomes
Economic difference between the total costs of care between randomized arms including; clinic visitations, hospitalizations, emergency room visitations, and diagnostic testing. Collected as cumulative diagnosis-related group (DRG) and current procedural terminology (CPT) amounts in United States Dollars
Measured: 180 days
Secondary outcomes (13)
Mobile Cardiac Telemetry
Number of referrals for mobile cardiac telemetry monitoring between randomized arms
Measured: 180 days
Health Economic Outcomes
Economic difference between the total costs of care between randomized arms including; clinic visitations, hospitalizations, emergency room visitations, and diagnostic testing. Collected as cumulative diagnosis-related group (DRG) and current procedural terminology (CPT) amounts in United States Dollars
Measured: 30 days
Patient-Reported Outcome Measures
Veterans Research and Development Corporation-12 Patient Reported Outcomes (mean total score 50 +/- 10) where higher values are associated with greater mental and physical debility
Measured: 30 days
Patient-Reported Outcome Measures
Veterans Research and Development Corporation-12 Patient Reported Outcomes (mean total score 50 +/- 10) where higher values are associated with greater mental and physical debility
Measured: 180 days
Patient-Reported Experience Measures
Agency for Healthcare Research and Quality Consumer Assessment of Healthcare Providers and Systems (average scores and difference between randomized arms) where higher scores are associated with greater patient satisfaction and patient experience
Measured: 30 days
Patient-Reported Experience Measures
Agency for Healthcare Research and Quality Consumer Assessment of Healthcare Providers and Systems (average scores and difference between randomized arms) where higher scores are associated with greater patient satisfaction and patient experience
Measured: 180 days
Diagnostic Imaging
Number of referrals for diagnostic imaging with transthoracic echocardiography between randomized arms
Measured: 180 days
Heart Failure
Incidence of heart failure diagnosed between randomized arms
Measured: 180 days
Atrial Fibrillation
Incidence of atrial fibrillation diagnosed between randomized arms
Measured: 180 days
Emergency Department Visitations
Percentage of patients presenting to the emergency department for a cardiac condition (example; myocardial infarction, heart failure, atrial fibrillation, and stroke) between randomized arms
Measured: 180 days
Hospitalization
Percentage of patients hospitalized for a cardiac condition (example; myocardial infarction, heart failure, atrial fibrillation, and stroke) between randomized arms
Measured: 180 days
Clinic Visitations
Percentage of patients presenting for a clinical visitation for a cardiac condition (example; myocardial infarction, heart failure, atrial fibrillation, and stroke) between randomized arms
Measured: 180 days
Medical Therapy
Percentage of patients initiating medical therapy for a cardiac condition including: heart failure, coronary artery disease, atrial fibrillation, and/or hypertension between randomized arms
Measured: 180 days
Timeline
From the start of the study to its completion; the mark is today. Estimated dates are the sponsor’s plan.
- First posted19 Oct 2018
- Study start20 Oct 2018
- Primary completion20 Dec 2019
- Study completion20 Mar 2020
Resources
- Official record on ClinicalTrials.govThe registry entry this page is built from, with its full change history.
- Publications (28)Papers the sponsor has linked to this study.
- Other trials for Cardiovascular DiseasesStudies of the same condition, open and under way.
- What the trial phases meanHow a treatment moves from first tests in people to approval.
More details
For doctors and researchers: how the study is designed, who runs it, and its registry IDs.
Study design
- Study type
- Interventional
- Phase
- Phase N/A
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Sequential
- Model description
- Pragmatic, multisite, cluster randomized trial comparing point of care and remote patient monitoring with digital health devices and handheld imaging to standard-care
- Masking
- Triple blind
- Who is masked
- Care provider, Investigator, Outcomes assessor
- Enrolment
- 374 participants (actual)
Sponsor and investigators
- Lead sponsor
- Scripps Health
- Sponsor type
- University, hospital or other
- Collaborators
- West Virginia University
- Responsible party
- Principal investigator, Sanjeev Bhavnani MD, Scripps Health
- Investigators
- Partho Sengupta, MD, Principal investigator, West Virginia University Heart and Vascular InstituteSanjeev Bhavnani, MD, Study director, Scripps Clinic
IDs, dates and publications
- ClinicalTrials.gov ID
- NCT03713333
- Sponsor's protocol ID
- Pro00029622
- Acronym
- ASE-INNOVATE
- Status last verified
- Apr 2024
- First submitted
- 15 Oct 2018
- Last updated
- 18 Apr 2024
- Results posted
- No
- Data monitoring committee
- Yes
- FDA-regulated drug
- No
- FDA-regulated device
- Yes
- Individual data sharing
- Undecided
Publications
Singh S, Bansal M, Maheshwari P, Adams D, Sengupta SP, Price R, Dantin L, Smith M, Kasliwal RR, Pellikka PA, Thomas JD, Narula J, Sengupta PP; ASE-REWARD Study Investigators. American Society of Echocardiography: Remote Echocardiography with Web-Based Assessments for Referrals at a Distance (ASE-REWARD) Study. J Am Soc Echocardiogr. 2013 Mar;26(3):221-33. doi: 10.1016/j.echo.2012.12.012.
PubMed 23439071Bansal M, Sengupta PP. How to interpret an echocardiography report (for the non-imager)? Heart. 2017 Nov;103(21):1733-1744. doi: 10.1136/heartjnl-2016-309443. Epub 2017 Sep 7. No abstract available.
PubMed 28883036Bansal M, Sengupta PP. Setting global standards in adult echocardiography: Where are we? Indian Heart J. 2015 Jul-Aug;67(4):298-301. doi: 10.1016/j.ihj.2015.07.020. Epub 2015 Aug 21. No abstract available.
PubMed 26304560Bhavnani SP, Parakh K, Atreja A, Druz R, Graham GN, Hayek SS, Krumholz HM, Maddox TM, Majmudar MD, Rumsfeld JS, Shah BR. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017 Nov 28;70(21):2696-2718. doi: 10.1016/j.jacc.2017.10.018. No abstract available.
PubMed 29169478Chamsi-Pasha MA, Sengupta PP, Zoghbi WA. Handheld Echocardiography: Current State and Future Perspectives. Circulation. 2017 Nov 28;136(22):2178-2188. doi: 10.1161/CIRCULATIONAHA.117.026622.
PubMed 29180495Levine DM, Linder JA, Landon BE. The Quality of Outpatient Care Delivered to Adults in the United States, 2002 to 2013. JAMA Intern Med. 2016 Dec 1;176(12):1778-1790. doi: 10.1001/jamainternmed.2016.6217.
PubMed 27749962Maddox TM, Albert NM, Borden WB, Curtis LH, Ferguson TB Jr, Kao DP, Marcus GM, Peterson ED, Redberg R, Rumsfeld JS, Shah ND, Tcheng JE; American Heart Association Council on Quality of Care and Outcomes Research; Council on Cardiovascular Disease in the Young; Council on Clinical Cardiology; Council on Functional Genomics and Translational Biology; and Stroke Council. The Learning Healthcare System and Cardiovascular Care: A Scientific Statement From the American Heart Association. Circulation. 2017 Apr 4;135(14):e826-e857. doi: 10.1161/CIR.0000000000000480. Epub 2017 Mar 2.
PubMed 28254835Loudon K, Treweek S, Sullivan F, Donnan P, Thorpe KE, Zwarenstein M. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ. 2015 May 8;350:h2147. doi: 10.1136/bmj.h2147. No abstract available.
PubMed 25956159Henderson C, Knapp M, Fernandez JL, Beecham J, Hirani SP, Beynon M, Cartwright M, Rixon L, Doll H, Bower P, Steventon A, Rogers A, Fitzpatrick R, Barlow J, Bardsley M, Newman SP. Cost-effectiveness of telecare for people with social care needs: the Whole Systems Demonstrator cluster randomised trial. Age Ageing. 2014 Nov;43(6):794-800. doi: 10.1093/ageing/afu067. Epub 2014 Jun 20.
PubMed 24950690Delaney SK, Hultner ML, Jacob HJ, Ledbetter DH, McCarthy JJ, Ball M, Beckman KB, Belmont JW, Bloss CS, Christman MF, Cosgrove A, Damiani SA, Danis T, Delledonne M, Dougherty MJ, Dudley JT, Faucett WA, Friedman JR, Haase DH, Hays TS, Heilsberg S, Huber J, Kaminsky L, Ledbetter N, Lee WH, Levin E, Libiger O, Linderman M, Love RL, Magnus DC, Martland A, McClure SL, Megill SE, Messier H, Nussbaum RL, Palaniappan L, Patay BA, Popovich BW, Quackenbush J, Savant MJ, Su MM, Terry SF, Tucker S, Wong WT, Green RC. Toward clinical genomics in everyday medicine: perspectives and recommendations. Expert Rev Mol Diagn. 2016;16(5):521-32. doi: 10.1586/14737159.2016.1146593. Epub 2016 Feb 24.
PubMed 26810587Orlando LA, Sperber NR, Voils C, Nichols M, Myers RA, Wu RR, Rakhra-Burris T, Levy KD, Levy M, Pollin TI, Guan Y, Horowitz CR, Ramos M, Kimmel SE, McDonough CW, Madden EB, Damschroder LJ. Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group. Genet Med. 2018 Jun;20(6):655-663. doi: 10.1038/gim.2017.144. Epub 2017 Sep 14.
PubMed 28914267Via G, Hussain A, Wells M, Reardon R, ElBarbary M, Noble VE, Tsung JW, Neskovic AN, Price S, Oren-Grinberg A, Liteplo A, Cordioli R, Naqvi N, Rola P, Poelaert J, Gulic TG, Sloth E, Labovitz A, Kimura B, Breitkreutz R, Masani N, Bowra J, Talmor D, Guarracino F, Goudie A, Xiaoting W, Chawla R, Galderisi M, Blaivas M, Petrovic T, Storti E, Neri L, Melniker L; International Liaison Committee on Focused Cardiac UltraSound (ILC-FoCUS); International Conference on Focused Cardiac UltraSound (IC-FoCUS). International evidence-based recommendations for focused cardiac ultrasound. J Am Soc Echocardiogr. 2014 Jul;27(7):683.e1-683.e33. doi: 10.1016/j.echo.2014.05.001.
PubMed 24951446Spencer KT, Kimura BJ, Korcarz CE, Pellikka PA, Rahko PS, Siegel RJ. Focused cardiac ultrasound: recommendations from the American Society of Echocardiography. J Am Soc Echocardiogr. 2013 Jun;26(6):567-81. doi: 10.1016/j.echo.2013.04.001. No abstract available.
PubMed 23711341Bhavnani SP, Sola S, Adams D, Venkateshvaran A, Dash PK, Sengupta PP; ASEF-VALUES Investigators. A Randomized Trial of Pocket-Echocardiography Integrated Mobile Health Device Assessments in Modern Structural Heart Disease Clinics. JACC Cardiovasc Imaging. 2018 Apr;11(4):546-557. doi: 10.1016/j.jcmg.2017.06.019. Epub 2017 Oct 5.
PubMed 28917688Bhavnani SP, Narula J, Sengupta PP. Mobile technology and the digitization of healthcare. Eur Heart J. 2016 May 7;37(18):1428-38. doi: 10.1093/eurheartj/ehv770. Epub 2016 Feb 11.
PubMed 26873093
Study at a glance
From the registry record, last updated by the sponsor on 18 Apr 2024. Recruitment status is as the sponsor reported it: confirm with the study team.
- Official title
- Implementation of High Definition Screening Using Handheld Imaging and Digital Health Technologies Within a Learning Health System to Identify Cardiovascular Disease at the Point-of-care: The ASE-INNOVATE Program
- ClinicalTrials.gov ID
- NCT03713333
- Acronym / sponsor ID
- ASE-INNOVATE / Pro00029622
- Conditions
- Cardiovascular Diseases, Hypertension, Heart Failure, Atrial Fibrillation, Metabolic Syndrome, Genetic Disease
- Keywords
- digital health, handheld ultrasound, smartphone ECG, mobile health, point of care genomics, big data, artificial intelligence, learning health system
- Study type
- Interventional
- Recruitment status
- Completed
- Enrolment
- 374 participants (actual)
- Lead sponsor
- Scripps HealthUniversity, hospital or other
- Collaborators
- West Virginia University
- Study start
- 20 Oct 2018 (actual)
- Primary completion
- 20 Dec 2019 (actual)
- Study completion
- 20 Mar 2020 (actual)
- First posted
- 19 Oct 2018 (actual)
- Results
- Not posted in the source record
Clinical summary
The research question as the sponsor registered it. This describes what the study plans to test, not what it found.
The need for new models of integrated care that can improve the efficiency of healthcare and reduce the costs are key priorities for health systems across the United States. Treatment costs for patients with at least one chronic medical or cardiovascular condition make up over 4-trillion dollars in spending on healthcare, with estimations of a population prevalence of 100-million affected individuals within the next decade. Therefore, the management of chronic conditions requires innovative and new implementation methods that improve outcomes, reduce costs, and increase healthcare efficiencies. Digital health, the use of mobile computing and communication technologies as an integral new models of care is seen as one potential solution. Despite the potential applications, there is limited data to support that new technologies improve healthcare outcomes. To do so requires; 1) robust methods to determine the impact of new technologies on healthcare outcomes and costs; and 2) evaluative mechanisms for how new devices are integrated into patient care. In this regard, the proposed clinical trial aims to advance the investigator's knowledge and to demonstrate the pragmatic utilization of new technologies within a learning healthcare system providing services to high-risk patient populations.
Detailed description
Objective #1: Determine the effectiveness of handheld imaging and digital health devices on long term health and patient-reported outcomes through pragmatic and randomized clinical trial designs.
Objective #2: Assess the impact of digital health devices and remote patient monitoring (RPM) on measures of healthcare efficiency. Measures of healthcare efficiency directly related to digital health technologies and RPM include: identify which interventions can improve care; define the variations in care and; demonstrate within which patient populations digital health technologies are most effective.
Objective #3: Apply integration methods for handheld imaging and digital health devices used for clinical decisions.
Achieving integration and interoperability-the ability of different information technology systems and software applications to communicate and exchange data with each other-requires identification for precisely how new innovations merge into systems of care and are applied to various practice settings.
Study design
- Primary purpose
- Screening
- Allocation
- Randomised
- Intervention model
- Sequential
- Masking
- Triple blind (care provider, investigator, outcomes assessor)
- Number of arms
- 2
- Model description
- Pragmatic, multisite, cluster randomized trial comparing point of care and remote patient monitoring with digital health devices and handheld imaging to standard-care
| Arm | Type | Receives |
|---|---|---|
| Technology-Enabled Visitations | Experimental | Diagnostic Test: Digital Health Device DiagnosticsTechnology-enabled visitations with digital health will include the following devices used at the time of a patient-physician encounter. These findings will be available to the treating physician at the time the visitation and to be used for clinical decisions. |
| Standard-Care Visitations | No intervention | –Standard-care is defined as the range of services available during usual patient care. Handheld Imaging and digital health screening will be performed in the control group after the patient-physician encounter. As such, patients and physicians will be blinded to the diagnostic findings unless an abnormal finding is detected that requires physician review and triage for further care. |
Who may qualify
- Ages
- 18 years and older
- Sex
- All
- Healthy volunteers
- Not accepted
- Age groups
- Adult, Older adult
Key inclusion criteria (1 listed)
- All participants of the ASE 2018 Outreach Event who are at least 18 years old who are referred for a cardiac evaluation
Key exclusion criteria (1 listed)
- Those not willing to consent
Eligibility is decided by the study team after screening; this overview does not establish that a patient qualifies. Full inclusion and exclusion criteria.
Interventions
| Intervention | Type | Arms |
|---|---|---|
| Digital Health Device DiagnosticsTechnology-enabled visitations with digital health will include the following devices used at the time of a patient-physician encounter. These findings will be available to the treating physician at the time the visitation and to be used for clinical decisions. * Handheld imaging - focused echocardiographic examination (Butterfly IQ) * Smartphone iECG for cardiac rhythm assessments (Alivecor) * Blood Pressure (CloudDX) * Oxygen Saturation (CloudDX) * Weight (CloudDX) * Point-of-Care Genetic Testing (Phosphorous) | Diagnostic test | Technology-Enabled Visitations |
Primary outcome
Health Economic Outcomes
Time frame: 180 days
Economic difference between the total costs of care between randomized arms including; clinic visitations, hospitalizations, emergency room visitations, and diagnostic testing. Collected as cumulative diagnosis-related group (DRG) and current procedural terminology (CPT) amounts in United States Dollars
13 secondary outcomes
Mobile Cardiac Telemetry
Time frame: 180 days
Health Economic Outcomes
Time frame: 30 days
Patient-Reported Outcome Measures
Time frame: 30 days
Patient-Reported Outcome Measures
Time frame: 180 days
Patient-Reported Experience Measures
Time frame: 30 days
Patient-Reported Experience Measures
Time frame: 180 days
Diagnostic Imaging
Time frame: 180 days
Heart Failure
Time frame: 180 days
Atrial Fibrillation
Time frame: 180 days
Emergency Department Visitations
Time frame: 180 days
Hospitalization
Time frame: 180 days
Clinic Visitations
Time frame: 180 days
Medical Therapy
Time frame: 180 days
Results and safety
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
Research links
- Official record on ClinicalTrials.govLast updated by the sponsor 18 Apr 2024.
- Background: PubMed 23439071Singh S, Bansal M, Maheshwari P, Adams D, Sengupta SP, Price R, Dantin L, Smith M, Kasliwal RR, Pellikka PA, Thomas JD, Narula J, Sengupta PP; ASE-REWARD Study Investigators. American Society of Echocardiography: Remote Echocardiography with Web-Based Assessments for Referrals at a Distance (ASE-REWARD) Study. J Am Soc Echocardiogr. 2013 Mar;26(3):221-33. doi: 10.1016/j.echo.2012.12.012.
- Background: PubMed 28883036Bansal M, Sengupta PP. How to interpret an echocardiography report (for the non-imager)? Heart. 2017 Nov;103(21):1733-1744. doi: 10.1136/heartjnl-2016-309443. Epub 2017 Sep 7. No abstract available.
- Background: PubMed 26304560Bansal M, Sengupta PP. Setting global standards in adult echocardiography: Where are we? Indian Heart J. 2015 Jul-Aug;67(4):298-301. doi: 10.1016/j.ihj.2015.07.020. Epub 2015 Aug 21. No abstract available.
- Background: PubMed 29169478Bhavnani SP, Parakh K, Atreja A, Druz R, Graham GN, Hayek SS, Krumholz HM, Maddox TM, Majmudar MD, Rumsfeld JS, Shah BR. 2017 Roadmap for Innovation-ACC Health Policy Statement on Healthcare Transformation in the Era of Digital Health, Big Data, and Precision Health: A Report of the American College of Cardiology Task Force on Health Policy Statements and Systems of Care. J Am Coll Cardiol. 2017 Nov 28;70(21):2696-2718. doi: 10.1016/j.jacc.2017.10.018. No abstract available.
- Background: PubMed 29180495Chamsi-Pasha MA, Sengupta PP, Zoghbi WA. Handheld Echocardiography: Current State and Future Perspectives. Circulation. 2017 Nov 28;136(22):2178-2188. doi: 10.1161/CIRCULATIONAHA.117.026622.
- Background: PubMed 27749962Levine DM, Linder JA, Landon BE. The Quality of Outpatient Care Delivered to Adults in the United States, 2002 to 2013. JAMA Intern Med. 2016 Dec 1;176(12):1778-1790. doi: 10.1001/jamainternmed.2016.6217.
- Background: PubMed 28254835Maddox TM, Albert NM, Borden WB, Curtis LH, Ferguson TB Jr, Kao DP, Marcus GM, Peterson ED, Redberg R, Rumsfeld JS, Shah ND, Tcheng JE; American Heart Association Council on Quality of Care and Outcomes Research; Council on Cardiovascular Disease in the Young; Council on Clinical Cardiology; Council on Functional Genomics and Translational Biology; and Stroke Council. The Learning Healthcare System and Cardiovascular Care: A Scientific Statement From the American Heart Association. Circulation. 2017 Apr 4;135(14):e826-e857. doi: 10.1161/CIR.0000000000000480. Epub 2017 Mar 2.
- Background: PubMed 25956159Loudon K, Treweek S, Sullivan F, Donnan P, Thorpe KE, Zwarenstein M. The PRECIS-2 tool: designing trials that are fit for purpose. BMJ. 2015 May 8;350:h2147. doi: 10.1136/bmj.h2147. No abstract available.
- Background: PubMed 24950690Henderson C, Knapp M, Fernandez JL, Beecham J, Hirani SP, Beynon M, Cartwright M, Rixon L, Doll H, Bower P, Steventon A, Rogers A, Fitzpatrick R, Barlow J, Bardsley M, Newman SP. Cost-effectiveness of telecare for people with social care needs: the Whole Systems Demonstrator cluster randomised trial. Age Ageing. 2014 Nov;43(6):794-800. doi: 10.1093/ageing/afu067. Epub 2014 Jun 20.
- Background: PubMed 26810587Delaney SK, Hultner ML, Jacob HJ, Ledbetter DH, McCarthy JJ, Ball M, Beckman KB, Belmont JW, Bloss CS, Christman MF, Cosgrove A, Damiani SA, Danis T, Delledonne M, Dougherty MJ, Dudley JT, Faucett WA, Friedman JR, Haase DH, Hays TS, Heilsberg S, Huber J, Kaminsky L, Ledbetter N, Lee WH, Levin E, Libiger O, Linderman M, Love RL, Magnus DC, Martland A, McClure SL, Megill SE, Messier H, Nussbaum RL, Palaniappan L, Patay BA, Popovich BW, Quackenbush J, Savant MJ, Su MM, Terry SF, Tucker S, Wong WT, Green RC. Toward clinical genomics in everyday medicine: perspectives and recommendations. Expert Rev Mol Diagn. 2016;16(5):521-32. doi: 10.1586/14737159.2016.1146593. Epub 2016 Feb 24.
- Background: PubMed 28914267Orlando LA, Sperber NR, Voils C, Nichols M, Myers RA, Wu RR, Rakhra-Burris T, Levy KD, Levy M, Pollin TI, Guan Y, Horowitz CR, Ramos M, Kimmel SE, McDonough CW, Madden EB, Damschroder LJ. Developing a common framework for evaluating the implementation of genomic medicine interventions in clinical care: the IGNITE Network's Common Measures Working Group. Genet Med. 2018 Jun;20(6):655-663. doi: 10.1038/gim.2017.144. Epub 2017 Sep 14.
- Background: PubMed 24951446Via G, Hussain A, Wells M, Reardon R, ElBarbary M, Noble VE, Tsung JW, Neskovic AN, Price S, Oren-Grinberg A, Liteplo A, Cordioli R, Naqvi N, Rola P, Poelaert J, Gulic TG, Sloth E, Labovitz A, Kimura B, Breitkreutz R, Masani N, Bowra J, Talmor D, Guarracino F, Goudie A, Xiaoting W, Chawla R, Galderisi M, Blaivas M, Petrovic T, Storti E, Neri L, Melniker L; International Liaison Committee on Focused Cardiac UltraSound (ILC-FoCUS); International Conference on Focused Cardiac UltraSound (IC-FoCUS). International evidence-based recommendations for focused cardiac ultrasound. J Am Soc Echocardiogr. 2014 Jul;27(7):683.e1-683.e33. doi: 10.1016/j.echo.2014.05.001.
- Related InfoLink provided by the sponsor.
- Individual participant dataSharing planned: Undecided
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Referral and contact
AzTrials does not enrol participants or take referrals. Contact the study team or a site listed below; they decide who can take part. This study is not recruiting at the moment.
Partho Sengupta, MD principal investigator
West Virginia University Heart and Vascular Institute
Sanjeev Bhavnani, MD study director
Scripps Clinic
1 site in 1 country: see every site and its status.
Questions about this trial
What is this clinical trial studying?
A study testing Digital Health Device Diagnostics in people with Cardiovascular Diseases and Hypertension, run by Scripps Health at 1 site in United States.
The full registered description is in the About this study section and the official record.
Who may be eligible to take part?
The sponsor lists 1 inclusion and 1 exclusion criteria. Eligibility is decided by the study team after screening, so read the full criteria and contact a listed site.
Is this clinical trial currently recruiting?
The record says: finished, as of the sponsor's last update on 18 Apr 2024. Status can change between updates, so confirm with the study contact before making plans.
Where is the study taking place?
The record lists 1 site in United States. Whether a site is taking part right now should be confirmed with that site. See every site.
What are the possible benefits and risks?
They depend on the study and the treatment, and no study can promise a benefit. The study team explains the known risks during informed consent, before anyone joins; the record itself does not list them.
Are results available for this study?
The study is completed, but results are not available in the source record. That does not mean the treatment did or did not work.
How can a doctor refer a patient or contact the research team?
The record lists no contact. The official record names the sponsor, who can be asked directly. AzTrials does not enrol participants or take referrals.
What does the primary outcome measure?
Health Economic Outcomes (time frame: 180 days). It is what the study was designed to measure, not a finding.
Where can I read the original study information?
On the ClinicalTrials.gov record for NCT03713333, which also lists 28 linked publications.
Does a listing mean the treatment is approved or proven to work?
No. A listing means the study is registered. It does not show that the treatment is safe, works, or is approved by a regulator.
How often is this information updated?
AzTrials copies changed records from ClinicalTrials.gov every night and reloads the whole registry every week. The sponsor last updated this record on 18 Apr 2024.