Clinical trial NCT05630170: The SMART-LV Pilot Study
Official title: Pilot Evaluation for SMartphone-adaptable Artificial Intelligence for PRediction and DeTection of Left Ventricular Systolic Dysfunction
Study type: INTERVENTIONAL
Overall status: COMPLETED
Phase: NA
Conditions: Left Ventricular Systolic Dysfunction
Lead sponsor: Yale University
Sponsor class: OTHER
First posted: 2022-11-29
Enrollment: 10
Sex: ALL
Minimum age (years): 18
Standard ages: ADULT
OLDER_ADULT
Accepts healthy volunteers: no
Allocation: NA
Intervention model: SINGLE_GROUP
Intervention model description: In the ECG repository of Yale New Haven Hospital, all patients undergoing a 12-lead screen in an outpatient setting, from whom 20 individuals, 10 each with high and low predicted probability of LVSD, will be invited for a limited echocardiogram to definitively evaluate for LVSD. The investigators will assess whether the AI-ECG model continues to have the reported discrimination and sensitivity of \>90% for LVSD diagnosis in a screening setting in outpatient routine clinical care.
Primary purpose: DEVICE_FEASIBILITY

Brief summary: The goal of this pilot study is to evaluate the prospective performance of an image-based, smartphone-adaptable artificial intelligence electrocardiogram (AI-ECG) strategy to predict and detect left ventricular systolic dysfunction (LVSD) in a real-world setting.

Detailed description: The SMART-LV pilot study will be a prospective cohort study in outpatient clinics at the Yale New Haven Hospital. Participants who have undergone a 12-lead electrocardiogram (ECGs) with either a high (≥80%) or low (\<10%) probability of LVSD on AI-ECG algorithm, but without an echocardiogram done in the clinical setting for at least 90 days after the ECG, will be identified by electronic health record (EHR) and invited for a limited echocardiogram/cardiac ultrasonogram for assessing LV ejection fraction. The goal of the study is to evaluate the feasibility of recruiting patients and performing the study after pursuing a screening on 12-lead ECGs. The procedure currently used for detection of LVSD, echocardiograms, are inaccessible and expensive. Therefore, while AI-ECG-based algorithms using a smartphone- or web-based application can broaden access to screening, a thorough evaluation for this indication is needed before clinical adoption. The investigators intend to use the results as pilot data for sample size and drop-off rate estimation for a subsequent larger prospective cohort study aimed at validating the performance characteristics of the model in a screening setting.

The validation of this accessible ECG-based screening strategy, that can be directly used by clinicians using a smartphone or web-based application, can transform the early identification of LVSD before the development of symptoms, thereby allowing broader utilization of evidence-based therapies to prevent symptomatic heart failure and premature death.

Eligibility criteria: Inclusion Criteria:

* Provision of signed and dated informed consent form.
* Stated willingness to comply with all study procedures and availability for the duration of the study

Exclusion Criteria:

* Patients who have undergone a prior echocardiogram.
* Patients with a prior diagnosis of left ventricular dysfunction, based on a documented low ejection fraction (EF) in the medical record.
* Patients with an intermediate predicted probability of low EF (10 to 80%)
* Patients with a prior diagnosis of heart failure as determined by International Classification of Diseases-10 diagnosis code for heart failure.
* Research opt-out patients

Primary outcomes:
measure: Successful detection of asymptomatic LVSD by AI-ECG
timeFrame: During study visit approximately 50 minutes
Secondary outcomes:
Other outcomes:
Locations:
city: New Haven
country: United States
facility: Yale New Haven Hospital
state: Connecticut
Study officials:
affiliation: Yale University
name: Rohan Khera, MD, MS
role: PRINCIPAL_INVESTIGATOR
References: