Applying an Artificial Intelligence-Enabled Electrocardiographic System for Reducing Mortality

NCT ID: NCT05118035

Last Updated: 2023-02-08

Basic Information

• Recruitment Status: COMPLETED
• Clinical Phase: NA
• Total Enrollment: 15965 participants
• Study Classification: INTERVENTIONAL
• Study Start Date: 2021-12-15
• Study Completion Date: 2022-12-31

Brief Summary

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.

Conditions

Cardiovascular Diseases; Intensity Care; Morality

Study Design

• Allocation Method: RANDOMIZED
• Intervention Model: PARALLEL
• Primary Study Purpose: SCREENING
• Blinding Strategy: SINGLE

Study Groups

Intervention

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.

Group Type: EXPERIMENTAL

AI-enabled ECG-based Screening Tool

Intervention Type: OTHER

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.

Control

Patients will continue routine practice.

Group Type: NO_INTERVENTION

No interventions assigned to this group

Interventions

AI-enabled ECG-based Screening Tool

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.

Intervention Type: OTHER

Eligibility Criteria

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.

• Minimum Eligible Age: 18 Years
• Eligible Sex: ALL
• Accepts Healthy Volunteers: No

Sponsors

National Defense Medical Center, Taiwan

OTHER

Sponsor Role: lead

Responsible Party

Chin Lin

Associate Professor

Responsibility Role: PRINCIPAL_INVESTIGATOR

Locations

National Defense Medical Center

Taipei, , Taiwan

Countries

Taiwan

References

Hsieh PH, Lin C, Lin CS, Liu WT, Lin TK, Tsai DJ, Hung YJ, Chen YH, Lin CY, Lin SH, Tsai CS. Economic analysis of an AI-enabled ECG alert system: impact on mortality outcomes from a pragmatic randomized trial. NPJ Digit Med. 2025 Jun 11;8(1):348. doi: 10.1038/s41746-025-01735-7.

Reference Type: DERIVED

PMID: 40494963 (View on PubMed)

Lin CS, Liu WT, Tsai DJ, Lou YS, Chang CH, Lee CC, Fang WH, Wang CC, Chen YY, Lin WS, Cheng CC, Lee CC, Wang CH, Tsai CS, Lin SH, Lin C. AI-enabled electrocardiography alert intervention and all-cause mortality: a pragmatic randomized clinical trial. Nat Med. 2024 May;30(5):1461-1470. doi: 10.1038/s41591-024-02961-4. Epub 2024 Apr 29.

Reference Type: DERIVED

PMID: 38684860 (View on PubMed)

Other Identifiers

NDMC2021005

Identifier Type: -

Identifier Source: org_study_id