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Using Ultromics EchoGo HFpEF Algorithm to Identify and Treat High Heart Failure Risk in Patients With Type 2 Diabetes

Identifying Undiagnosed HFpEF Among Patients With Type 2 Diabetes Using Ultromics AI HFpEF Algorithm

Status
Recruiting
Phases
Unknown
Study type
Interventional
Source
ClinicalTrials.gov
Registry ID
NCT06593314
Enrollment
800
Registered
2024-09-19
Start date
2025-08-06
Completion date
2026-10-15
Last updated
2026-05-07

For informational purposes only — not medical advice. Sourced from public registries and may not reflect the latest updates. Terms

Conditions

Diabetes, Heart Failure

Keywords

Subclinical Heart Failure; Electronic Alerts; Diabetes

Brief summary

A pragmatic electronic health record (EHR) based randomized controlled trial to evaluate the utility of providing Ultromics EchoGo analysis results and recommendations for HF risk prevention therapies using an EHR embedded clinical decision support tool.

Detailed description

Historic echocardiograms will be analyzed using the Ultromics EchoGo algorithm. For patients that have a positive EchoGO result i.e. HFpEF detected, the provider will get an clinical decision support alert flagging high risk of HFpEF based on randomized assignment. Experimental: Alert Group Provider will receive a computer-based provider-to-provider message notifying the provider that the patient has subclinical HFpEF as determined by the Ultromics EchoGo algorithm and associated guideline recommendations for the management of these patients. The alert will include guideline-based and standard-of-care recommendations for the use of SGLT-2 inhibitors, non-steroidal MRA, or GLP-1 RA (if obesity is present). The purpose of the alert is to inform the providers about the risk of heart failure and provide them guidance regarding the guideline-recommended standard of care. The providers can choose to provide care as deemed fit based on the information provided. The investigators will assess the practice patterns of providers in response to the EHR-based alert over the study period (3, and 6-month follow-up). The investigators will also assess the downstream hospitalization events for HF within 12 months of the initial alert. Control arm: Standard Message Providers in the control group will receive a standard message that will recommend either SGLT2i, GLP-1RA, and/or ns-MRA for treatment of diabetes and for prevention of heart failure. This group will not receive any information about the presence of subclinical heart failure detected by the EchoGo algorithm. The investigators will monitor the practice pattern in this group as well over the study period. Follow Up. Adherence to SGLT-2i and GLP-1 RA medications will be assessed by evaluating the electronic health record and documenting if the patient had a follow-up with a healthcare provider at 3 and 6 months and medication listed in the active prescription medication list. Sample Size: The investigators plan to enroll 800 anticipated patients using a parallel design with 1:1 allocation and a binary primary endpoint (SGLT2i use). Using a two-sample test for difference in proportions with the normal (Fleiss) approximation, pooled variance without continuity correction, and assuming a control proportion of 30%, α=0.05 (two-sided), and 80% power, an N=800 (400/arm) provides a minimum detectable absolute increase of \ 9.4 percentage points (30.0% to 39.4% in the intervention arm). This corresponds to RR = 1.31(95% CI 1.08, 1.59) and Cohen's h = 0.20.

Interventions

BEHAVIORALMessage with EchoGo

This alert will inform the provider that the patient has subclinical HFpEF

This alert will inform the provider of guideline directed treatment options for patients with diabetes to prevent heart failure.

Sponsors

University of Texas Southwestern Medical Center
Lead SponsorOTHER

Study design

Allocation
RANDOMIZED
Intervention model
PARALLEL
Primary purpose
PREVENTION
Masking
NONE

Intervention model description

Patients with high risk of heart failure (see inclusion/exclusion) as detected by the Ultromics EchoGo algorithm will be included. Each patient will be randomized for their provider to get either a standard message or an Echo-Go message.

Eligibility

Sex/Gender
ALL
Healthy volunteers
No

Inclusion criteria

Clinical cohort inclusion

Exclusion criteria

Inclusion Criteria: * Patients with diagnosis of Type 2 diabetes and High WATCH DM score. * Echocardiogram available in last 6-months.

Design outcomes

Primary

MeasureTime frameDescription
Frequency of prescription of SGLT-2i medication at outpatient clinic visits over 3 months follow-up3-month follow-upFrequency of prescription of SGLT-2i medication at outpatient clinic visits will be measured as the proportion of new prescription for SGLT2inhibitor divided by the number of alerts triggered.
Frequency of prescription of SGLT-2i medication at outpatient clinic visits over 6 months follow-up3-month follow-upFrequency of prescription of SGLT-2i medication at outpatient clinic visits will be measured as the proportion of new prescription for GLP1 divided by the number of alerts triggered.

Secondary

MeasureTime frameDescription
Frequency of prescription of ns-MRA at outpatient clinic visits6-monthsFrequency of prescription of ns-MRA at outpatient clinic visits will be measured as the proportion of new prescription for ns-MRA divided by the number of alerts triggered.
Frequency of prescription of GLP-1 RA medication at outpatient clinic visits over6 months follow-upFrequency of prescription of GLP-1 RA medication at outpatient clinic visits over will be measured as the proportion of new prescription for GLP1 divided by the number of alerts triggered.
Heart Failure hospitalization12 monthsCounts of heart failure hospitalization will be recorded form the electronic health record

Countries

United States

Contacts

CONTACTAmbarish Pandey, MD
Ambarish.Pandey@UTSouthwestern.edu617-869-8957
PRINCIPAL_INVESTIGATORAmbarish Pandey, MD

University of Texas Southwestern Medical Center

Outcome results

None listed

Source: ClinicalTrials.gov · Data processed: May 8, 2026