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Development of an AI-Driven Predict–Prescribe–Feedback Cyclical Decision Support System for Precision Cardiac Rehabilitation

Development of an AI-Driven Predict–Prescribe–Feedback Cyclical Decision Support System for Precision Cardiac Rehabilitation

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
CRIS
Registry ID
KCT0012090
Enrollment
250
Registered
2026-06-08
Start date
2026-09-01
Completion date
Unknown
Last updated
2026-06-15

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

Conditions

None listed

Interventions

None listed

Sponsors

Korea University Anam Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: Adults aged 19 years or older. Patients who have undergone cardiac surgery or cardiovascular procedures at the Department of Cardiology or the Department of Cardiovascular and Thoracic Surgery of Korea University Anam Hospital.

Exclusion criteria

Exclusion criteria: Patients with unstable vital signs or medical conditions that preclude participation in inpatient cardiac rehabilitation exercise therapy. Patients who are unable to undergo cardiopulmonary exercise testing (CPET). Patients who do not provide informed consent for study participation.

Design outcomes

Primary

MeasureTime frame
Peak oxygen uptake—

Secondary

MeasureTime frame
Maximum metabolic equivalent—

Countries

Korea, Republic of

Contacts

Public ContactBo Ryun Kim

Korea University Anam Hospital

brkim08@gmail.com+82-2-920-6471

Outcome results

None listed

Source: CRIS (via WHO ICTRP) · Data processed: Jun 21, 2026