Applying an Artificial Intelligence-Enabled Electrocardiographic System for Reducing Mortality

Study Overview
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Brief Summary
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This is a randomized controlled trial (RCT) to test a novel artificial intelligence (AI)-enabled electrocardiogram (ECG)-based screening tool for early detection of clinical deterioration for reducing mortality.

Official Title
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Applying an Artificial Intelligence-Enabled Electrocardiographic System for Reducing Mortality: a Pragmatic Randomized Clinical Trial

Conditions
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Cardiovascular Diseases, Intensity Care, Morality

Intervention / Treatment
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* Other: AI-enabled ECG-based Screening Tool


Participation Criteria
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Eligibility Criteria
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Inclusion Criteria: Patients in emergency department or inpatient department. Patients recieved at least 1 ECG examination. Exclusion Criteria: The patients recieved ECG at the period of inactive AI-ECG system.

Ages Eligible for Study
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Minimum Age: 18 Years

Sexes Eligible for Study
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All

Accepts Healthy Volunteers
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No

Study Plan
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How is the study designed?
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Design Details

Primary Purpose: Screening
Allocation: Randomized
Intervention Model: Parallel Assignment
Interventional Model Description: intervention group:8001 control group:7964
Masking: Single


Arms and Interventions

| Participant Group/Arm | Intervention/Treatment |
| --- | --- |
| Experimental: Intervention<br>Patients randomized to intervention will have access to the screening tool. Once the AI-ECG indicates high risk of mortality, a warning message would be immediately triggered and sent to the corresponding attending physicians. Notifications appear in the recipient's smartphone message system for the prompt attention. The message notified the physician that, An ECG was received for patient X. An ECG indicates high risk of mortality. Please intensively attend to patient's conditions. If the physicians need to further identify the ECG, click on the following link to connect the ECG and the result of AI-ECG prediction. Of note, although we will actively send a warning message for high risk cases, the AI-ECG report for low risk cases still presented the degree of risk. Physicians can check the relative severity by access EHR for patients in the intervention group. | Other: AI-enabled ECG-based Screening Tool<br>* Primary care clinicians in the intervention group had access to the report, which shows the risk prediction results for each patients. Moreover, the clinicians will recieve a short message when patients with a high risk ECG identified by AI.<br>|
| No Intervention: Control<br>Patients will continue routine practice. | |

What is the study measuring?
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Primary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| All cause mortality (death) | After performing an electrocardiogram, the patient's survival is tracked. | Within 90 days | 

Secondary Outcome Measures

| Outcome Measure | Measure Description | Time Frame |
| --- | --- | --- |
| Cardiovascular cause mortality (death) | After performing an electrocardiogram, the patient's survival is tracked. | Within 90 days | 
| Arrhythmia medication | After performing an electrocardiogram, the patient recieved related intervention. | Within 12 hours | 
| Electrolyte examination | After performing an electrocardiogram, the patient recieved electrolyte examination | Within 3 days | 
| Cadiac examination | After performing an electrocardiogram, the patient recieved cadiac examination | Within 3-7 days |