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Prediction of spontaneous preterm birth using machine learning method based on vaginal microecological characteristics

Prediction of spontaneous preterm birth using machine learning method based on vaginal microecological characteristics

Status
Recruiting
Phases
Unknown
Study type
Observational
Source
ChiCTR
Registry ID
ChiCTR2300068569
Enrollment
Unknown
Registered
2023-02-23
Start date
2023-02-26
Completion date
Unknown
Last updated
2023-05-22

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

Conditions

Spontaneous preterm birth

Interventions

The preterm group:N/A
The term group:N/A

Sponsors

Xiamen Maternal and Child Health Care Hospital affiliated to Xiamen University
Lead Sponsor

Eligibility

Sex/Gender
Female
Age
18 Years to 45 Years

Inclusion criteria

Inclusion criteria: ? Pregnant women with single child and signed informed consent; ? Pregnant women aged between 18 and 45 years old; ? Pregnant women with cervical insufficiency; ? pregnant women with threatened preterm labor; (5) Pregnant women who received regular medical check-up in our hospital and delivered in our hospital.

Exclusion criteria

Exclusion criteria: ? Pregnant women with infectious diseases; ? Antibiotic/immunosuppressive therapy for pregnant women within one month; ? Pregnant women had sex three days before the test; (4) Pregnant women with serious medical and surgical diseases; (5) Pregnant women received HPV vaccine in the past six months; ? The fetus has serious chromosomal abnormalities or malformations.

Design outcomes

Primary

MeasureTime frame
vaginal microecosystem ;Length of cervical canal;

Secondary

MeasureTime frame
white blood cell count;neonate birth weight;

Countries

China

Contacts

Public Contactaimei zhang

Xiamen Maternal and Child Health Care Hospital affiliated to Xiamen University

2418826177@qq.com18972767350

Outcome results

None listed

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