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Machine Learning-Based Prediction Model for Adverse Outcomes After Pediatric Congenital Heart Disease Surgery

Machine Learning-Based Prediction Model for Adverse Outcomes After Pediatric Congenital Heart Disease Surgery

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400094303
Enrollment
Unknown
Registered
2024-12-19
Start date
2024-12-31
Completion date
Unknown
Last updated
2025-01-06

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

Conditions

Congenital Heart Disease

Interventions

Training group:None
Testing group:None

Sponsors

Kunming Children's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All
Age
No minimum to 18 Years

Inclusion criteria

Inclusion criteria: Children under 18 years old undergoing cardiopulmonary bypass-assisted surgery

Exclusion criteria

Exclusion criteria: 1.Children with missing outcome data. 2.Children undergoing multiple surgeries.

Design outcomes

Primary

MeasureTime frame
Postoperative adverse outcomes;—

Secondary

MeasureTime frame
Early in-hospital mortality;Prolonged hospitalization;Prolonged mechanical ventilation time;Reintubation after extubation;—

Countries

China

Contacts

Public ContactCheng Liming

Kunming Children's Hospital

medcheng@163.com+86 138 8876 4840

Outcome results

None listed

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