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A machine learning-based model for predicting postpartum hemorrhage after cesarean delivery

A machine learning-based model for predicting postpartum hemorrhage after cesarean delivery

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2400088573
Enrollment
Unknown
Registered
2024-08-21
Start date
2024-04-02
Completion date
Unknown
Last updated
2024-08-26

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

Conditions

postpartum hemorrhage

Interventions

all enrolled maternity:none

Sponsors

Nanjing Women and Children's Healthcare Hospital
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
18 Years to No maximum

Inclusion criteria

Inclusion criteria: Maternity after cesarean section; age =18 years; agreed to participate and signed an informed consent form

Exclusion criteria

Exclusion criteria: Severe surgical complications; no serious chronic diseases or comorbidities; severe postoperative complications such as infection and uterine rupture; patients with abnormal uterine anatomy; women with severe blood loss requiring emergency surgery; contraindications to ultrasonography

Design outcomes

Primary

MeasureTime frame
the ratio of uterine size to uterine cavity size;—

Secondary

MeasureTime frame
amount of postpartum hemorrhage;—

Countries

China

Contacts

Public ContactFengchan Xi

Nanjing Women and Children's Healthcare Hospital

xifengchan@aliyun.com+86 138 1308 8919

Outcome results

None listed

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