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Development and Validation of a Machine Learning-Based Predictive Model for Neonatal Vascular Pathology-Related Thrombosis Risk

Development and Validation of a Machine Learning-Based Predictive Model for Neonatal Vascular Pathology-Related Thrombosis Risk

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500099630
Enrollment
Unknown
Registered
2025-03-26
Start date
2025-04-10
Completion date
Unknown
Last updated
2025-03-31

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

Conditions

Vascular access catheterized newborn

Interventions

Modeling group:None
Internal verification group:None
External verification group:None

Sponsors

Peking University Shenzhen Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 1 Years

Inclusion criteria

Inclusion criteria: 1.Hospitalized neonates; 2.Vascular access was placed during hospitalization; 3.Ultrasound scanning, magnetic resonance or venography were performed during catheterization to screen vascular access;

Exclusion criteria

Exclusion criteria: 1.Cases are missing and case information is seriously incomplete; 2.Infants with pre-existing vascular access; 3.No ultrasound scanning, magnetic resonance or venography imaging was performed during the catheterization;

Design outcomes

Primary

MeasureTime frame
Degree of model fit;—

Countries

China

Contacts

Public ContactHewan xiang

Peking University Shenzhen Hospital

504619292@qq.com+86 755 8392 3333

Outcome results

None listed

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