FindTrial
Sign In

Development and Validation of a Machine Learning Model for Predicting Wound Dehiscence after Neonatal Abdominal Surgery: A Multicenter Retrospective Study

Machine Learning-Based Development of a Risk Prediction Model for Wound Dehiscence after Neonatal Abdominal Surgery: A Multicenter Retrospective Study - Machine Learning Model for Neonatal AWD

Status
Active, not recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2500108824
Enrollment
Unknown
Registered
2025-09-05
Start date
2023-10-01
Completion date
Unknown
Last updated
2025-09-08

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

Conditions

Abdominal Wound Dehiscence, AWD

Interventions

AWD/ Non-AWD:none

Sponsors

Shenzhen Nanshan People's Hospital
Lead Sponsor

Eligibility

Sex/Gender
All

Inclusion criteria

Inclusion criteria: (1) Newborns undergoing open abdominal surgery; (2) Children with complete medical data required for this study; (3) Admission age <=28 days.

Exclusion criteria

Exclusion criteria: (1) Children who have been discharged from hospital without treatment; (2) Children who have received endoscopic or laparoscopic treatment; (3) Children who died after the operation; (4) Children with incomplete medical information

Design outcomes

Primary

MeasureTime frame
Has AWD occurred;—

Secondary

MeasureTime frame
ROC-AUC;PR-AUC;F1 score;Accuracy;Sensitivity;Specificity;—

Countries

China

Contacts

Public ContactDuan Shouxing

Shenzhen Nanshan People's Hospital

sxduan@email.szu.edu.cn+86 136 4141 3856

Outcome results

None listed

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