Development and Validation of a Machine Learning-Based Risk Prediction Model for Medication Deviation in Coronary Heart Disease Patients During Hospital-to-Home Transition

Status

Active, not recruiting

Phases

Early Phase 1

Study type

Observational

Source

ChiCTR

Registry ID

ChiCTR2600126545

Enrollment

Unknown

Registered

2026-06-11

Start date

2026-06-15

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

Coronary Atherosclerotic Heart Disease

Interventions

Case series :None

Eligibility

Sex/Gender

All

Age

18 Years to 80 Years

Inclusion criteria

Inclusion criteria: 1. Diagnosed with coronary heart disease confirmed by coronary angiography showing at least one coronary artery with stenosis >50%; 2. Aged >=18 years; 3. Able to communicate with others independently or with the help of a caregiver; 4. Informed of the study content and willing to participate.

Exclusion criteria

Exclusion criteria: 1. Presence of cognitive, language, hearing, or visual impairments that prevent effective communication and interaction; 2. Concomitant severe organic diseases.

Design outcomes

Primary

Measure	Time frame
Medication Discrepancy;	—

Secondary

Measure	Time frame
Beliefs about Medicines;Self-Efficacy for Appropriate Medication Use;Medication Adherence;Area Under the Curve (AUC);Brier score;Net benefit of decision curve analysis;	—

Countries

China

Contacts

Public ContactWu Qing

The First Affiliated Hospital of Soochow University

2909890878@qq.com+86 152 5146 5270

Outcome results

None listed

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