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Building and validating a clinical outcome prediction model for non-valvular atrial fibrillation based on multimodal data and deep machine learning algorithms.

Building and validating a clinical outcome prediction model for non-valvular atrial fibrillation based on multimodal data and deep machine learning algorithms.

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300074493
Enrollment
Unknown
Registered
2023-08-08
Start date
2023-08-15
Completion date
Unknown
Last updated
2023-08-21

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

Conditions

atrial fibrillation

Interventions

nonvalvular atrial fibrillation:None

Sponsors

Beijing Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
18 Years to 95 Years

Inclusion criteria

Inclusion criteria: (1) The diagnosis of atrial fibrillation includes the diagnosis of atrial fibrillation by electrocardiogram or dynamic electrocardiogram, or a clear history of atrial fibrillation in the past. (2) Non-valvular atrial fibrillation is defined as atrial fibrillation other than moderate to severe mitral stenosis (with the possibility of requiring surgical intervention) and post-mechanical valve replacement.

Exclusion criteria

Exclusion criteria: (1) Patients with various advanced cancers and an expected survival duration of less than one year; (2) Patients with mental disorders, who cannot cooperate well with follow-ups and related examinations; (3) Other reasons that the researcher deems inappropriate for participation in this study, beyond the aforementioned.

Design outcomes

Primary

MeasureTime frame
Major Adverse Cardiovascular Events(MACE);—

Countries

China

Contacts

Public ContactDong Min

Beijing Hospital

35133385@qq.com+86 137 0105 4558

Outcome results

None listed

Source: ChiCTR (via WHO ICTRP) · Data processed: Feb 4, 2026