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Clinical trial Recruiting at 1 site Observational Ages 18 to 80

Machine learning-based cardiovascular risk assessment for Chinese individuals

Official title: Harnessing Evidence And Real-world Data To Improve Cardiovascular Health and Services (HEARTWISE) - The Personalised - CARdiovascular DIsease Risk Assessment for Chinese (P-CARDIAC) Real World Evidence Study - Population-based Study

Run by The University of Hong Kong · NCT06262828 · Data updated 2026-11-12

This study is testing a new machine learning tool to see if it can better predict heart risks for people in Hong Kong who have a history of heart problems.

Quick facts

Study typeObservational
Ages18 Years to 80 Years
SexAll
Enrollment3800 (estimated)
SponsorThe University of Hong Kong
Academic / other
Locations1 site (Hong Kong)
StatusRecruiting
StartedApril 25, 2024
Main results expectedMarch 31, 2027
Study endsApril 30, 2028
Registry record updatedOctober 24, 2024
Trial IDNCT06262828 on ClinicalTrials.gov

Is this trial a fit for you?

Likely a good fit

Ideal candidates are individuals aged 18 to 80 who have experienced prior cardiovascular events and can communicate in English or Traditional Chinese.

Probably not a fit

Patients with a history of dementia, psychiatric diseases, or physical impairments may not benefit from this study.

A plain-English reading of the study's rules. Only the study team can confirm whether you qualify.

What this trial studies

This observational study aims to evaluate the accuracy of a machine learning-based risk prediction model, P-CARDIAC, designed specifically for a predominantly Chinese population in Hong Kong. It involves a prospective cohort study to assess the correlation between reported cardiac symptoms and the P-CARDIAC risk score, as well as a Delphi technique to establish risk thresholds based on current clinical guidelines. The study will take place in various outpatient clinics, focusing on individuals with a history of cardiovascular disease. The goal is to improve risk assessment and management for patients at high risk of recurrent cardiovascular events.

Why it matters

Potential benefit: If successful, this study could enhance the accuracy of cardiovascular risk assessments, leading to better prevention and management strategies for patients.

How similar studies have gone: Other studies have shown success with machine learning approaches in risk prediction, but this specific model for the Chinese population is novel.

Who can join: the full rules

Show full inclusion / exclusion criteria 
Inclusion Criteria:

* Individuals aged 18 - 80 years old who visit the medical or cardiology SOPC clinics at the Queen Mary Hospital (QMH), Pok Oi Hospital (POH), Kwong Wah Hospital (KWH), Princess Margaret Hospital (PMH), Queen Elizabeth Hospital (QEH), Tuen Mun Hospital (TMH) and experienced prior CVD event (such as peripheral artery diseases, coronary heart diseases, myocardial infarction, stroke, revascularization) before the scheduled SOPC clinic visit date are eligible to participate in this study. Participants will be also able to comprehend English or Traditional Chinese as well.

Exclusion Criteria:

* People with history of dementia, psychiatric diseases or physical impairment.

Where it's running

  • School of Nursing, The University of Hong Kong
    Hong Kong, Hong Kong     Recruiting

Study contacts

  • Principal investigator: Sze Ling Celine Chui, PhD — School of Nursing, The University of Hong Kong

Similar studies

Other studies for the same condition or treatment. Recruiting studies are listed first.

How to take part

  1. Review the eligibility criteria above with your treating physician.
  2. Check the official listing on ClinicalTrials.gov for the latest contact details and recruitment status.
  3. Contact the study coordinator or principal investigator to ask for pre-screening. Pre-screening is free and never obligates you to enroll.
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Cardiovascular Diseases 365The University of Hong Kong 192
Last reviewed 2026-11-14 by the Find a Trial editorial team. Information on this page is for educational purposes and is not medical advice. Always consult qualified healthcare professionals about clinical trial participation.