NCT Number: NCT07103057
To describe the clinical, economic, and population characteristics of newly diagnosed, previously diagnosed, and suspected patients evaluated by Viz HCM. HCM is underdiagnosed in the community and AI algorithms have been developed as screening tools. However, it is not well understood how to best integrate AI screening tools and their potential impact.
Interested in participating?
Request Info18 year and older
All sexes
Observational
Emory University, Atlanta, Georgia, United States
Healthy volunteers accepted: No
Only the study team can determine whether someone qualifies for participation.
All Cohorts
Additional cohort-specific criteria:
Cohort 1 - Newly Diagnosed Patients
Cohort 2 - Previously Diagnosed Patients ● Prior diagnosis of HCM as evidenced by clinical diagnosis documentation prior to Viz HCM implementation
Cohort 3 - Suspected and Not Diagnosed Patients
● Patients did not receive sufficient clinical workup for HCM diagnosis confirmation
Cohort 4 - Unlikely HCM ● Patient ECG moved to 'Unlikely HCM' group within Viz by site study staff following HCM alert review
Cohort 5 - Alerts Not Reviewed
● HCM alert not reviewed by site study staff during study enrollment period
Viz HCM is a Software as a Medical Device (SaMD) intended to receive 12-lead ECG recordings collected as part of a routine clinical assessment and analyze them in parallel to the standard of care. The device uses a machine learning based algorithm to analyze 12-lead ECGs and identify ECGs with suspected HCM.
Time frame: Up to 3 years
To describe the clinical characteristics of patients who were alerted for suspected HCM by an AI-based ECG tool and newly diagnosed, previously diagnosed, or suspected for HCM.
Time frame: Up to 3 years
To describe the clinical characteristics of medical workup for patients who were alerted for suspected HCM by an AI-based ECG tool and newly diagnosed, previously diagnosed, or suspected for HCM.
Time frame: Up to 3 years
To describe the clinical characteristics of treatment plans for patients who were alerted for suspected HCM by an AI-based ECG tool and newly diagnosed, previously diagnosed, or suspected for HCM.
Time frame: Up to 3 years
To describe the clinical characteristics of closed care pathways (patients diagnosed who receive treatment) for patients who were alerted for suspected HCM by an AI-based ECG tool and newly diagnosed, previously diagnosed, or suspected for HCM.
Time frame: Up to 3 years
To describe HCM access to care by socioeconomic status at the time of diagnosis.
Time frame: Up to 3 years
Population evaluation for access to care by clinical stage of HCM at time of diagnosis.
Time frame: Up to 3 years
To describe healthcare utilization and economic outcomes as captured by medical clinic visits, associated testing (e.g., echocardiogram, cardiac MRI, laboratory and genetic screening), hospitalization, and ICD placement for primary and secondary prevention for sudden cardiac death.
Time frame: Up to 3 years
To describe healthcare utilization and economic outcomes as captured by associated testing
Time frame: Up to 3 years
To describe healthcare utilization and economic outcomes as captured by hospitalization data
Time frame: Up to 3 years
To describe healthcare utilization and economic outcomes as captured by ICD placement for primary and secondary prevention for sudden cardiac death.
Time frame: Up to 2 years
Time frame: Up to 2 years
Time frame: Up to 2 years
Contact information is provided by the study sponsor or research team.
Ethan Carter, BSN
CONTACT
(415) 409-9369
Sloane Smith-Saunders, MBA, MPH
CONTACT
(754) 307-6336
Viz.ai, Inc.
Industry
ACCESS HCM: Real World Evidence for Artificial-Intelligence-assisted Screening and Access to Care for HCM - A Multi-Site Registry
OpenTrials presents study information sourced from ClinicalTrials.gov. The official registry record should be consulted for the latest information.
View the official ClinicalTrials.gov record (opens in a new tab)This listing is for discovery and informational purposes only. It is not medical advice, does not guarantee that a study is recruiting, and does not determine eligibility. Contact the study team and a qualified healthcare professional when considering participation.
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