Deployment and Evaluation of Artificial Intelligence Software for Electrocardiogram Analysis and Management in Primary Care
For patients and families
In plain language
An automatic summary of structured registry data. It is an orientation aid, not a substitute for the official protocol or a physician assessment.
- What is being studied
- The protocol lists: DeepECG plateform diagnosis & recommendations.
- Who it may be relevant to
- Registry conditions: Primary Care Provider, Structural Heart Disease. Basic parameters: from 18 years · All.
- What needs checking
- Age, condition and sex are only basic indicators. Prior treatment, laboratory values and other mandatory requirements appear in the eligibility criteria below.
- Where it takes place
- Canada
- Next step
- Save the trial, show it to the treating physician, and confirm current recruitment with the study center. Costs, documents and travel →
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Overview
The DAISEA-ECG project aims to improve the diagnosis of heart diseases in primary care through the DeepECG platform, which combines ECG-AI and ECHONeXT algorithms. This study uses a stepped wedge design, where each Family Medicine Group acts as its own control. The FMGs will gradually transition from the control period (without AI recommendations) to the intervention period (with AI recommendations activated) in a randomized sequence. The primary objective is to compare the sensitivity of family physicians in detecting cardiac pathologies, with and without the assistance of the DeepECG platform. Sensitivity is defined as the proportion of patients correctly referred to cardiology or for transthoracic echocardiography (TTE) among those who indeed required cardiovascular evaluation, as confirmed by an independent adjudication committee.
Detailed description
Mathematically, sensitivity is calculated as True Positive / (True Positive + False Negative), where True Positive represents correctly referred patients and false negatives represents patients who should have been referred but were not.
The secondary objectives include determining the rate of cardiovascular evaluation referrals before and after the intervention (implementation of the DeepECG platform), the individual characteristics of the intervention (PPV, NPV, and specificity), as well as evaluating the feasibility of implementing AI-based automatic ECG interpretation in primary care through surveys of family physicians and cardiologists.
PPV: Positive predictive value NPV: Negative predictive value
Interventions
- Device DeepECG plateform diagnosis & recommendations
EchoNeXT\& ECG-AI algorithm
Primary outcome measures
- sensitivity of cardiology referrals [Time frame: 18 months]
Secondary outcome measures (1)
- specificity, negative predictive value, and positive predictive value of cardiology referrals [Time frame: 18 months]
Eligibility criteria
Inclusion criteria
Family Physicians or Nurse Practitioners
Family physicians or nurse practitioners (NPs) practicing in one of the participating FMGs.
Family physicians who have given their free and informed consent. Patients
Adult patients (18 years or older). Patients without follow-up in cardiology or internal medicine for cardiovascular issues (arrhythmia, heart failure, myocardial infarction, atherosclerotic coronary artery disease, valvular heart disease) or those who had a negative investigation in the past with no additional follow-up.
ECG
Any 12-lead ECG performed with the MUSE GE 360 machine. ECG of adequate technical quality for interpretation (otherwise, it will be automatically rejected by the platform).
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Exclusion criteria
- Family Physicians or Nurse Practitioners
Family physicians practicing exclusively in pediatrics (patients under 18 years old).
Family physicians unable to follow the project guidelines.
Criteria are shown verbatim from the registry (in English). Final eligibility is always assessed by the study center.
Healthy volunteers: Yes
Study design
- Allocation
- Randomized
- Model
- Parallel assignment
- Masking
- Single blind
- Primary purpose
- Diagnostic
Study locations
Canada · 1 center
- Montreal Heart Institute — Montreal
Identifiers
NCT: NCT06637293 · DAISEA-ECG