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Using Machine Learning to Model Early-onset Neonatal Sepsis Risk in Uganda and Zimbabwe

NCT06411405 · Status: RECRUITING · Type: OBSERVATIONAL · Enrollment: 2500

Last updated 2024-05-17

No results posted yet for this study

Summary

The goal of this observational study is to develop a risk prediction model for early-onset neonatal sepsis in term and late preterm neonates in Uganda and Zimbabwe.

The main questions it aims to answer are:

* What are the risk factors for early-onset neonatal sepsis in low-resource settings?
* How can these be combined into a risk prediction model?

Mother-baby pairs will be recruited in Uganda. They will have extensive data taken on their medical and obstetric histories and lifestyles, and their newborns will have a blood sample taken just after birth for culture. Machine learning techniques will be used to create the risk prediction model, which will then be validated in a second population in Zimbabwe.

Conditions

  • Neonatal Sepsis

Interventions

OTHER

Maternal medical history

See above; multiple factors to be collected broadly grouped into maternal medical, obstetric, and sociodemographic/lifestyle factors.

Sponsors & Collaborators

  • MU-JHU CARE

    collaborator OTHER

  • Biomedical Research and Training Institute, Zimbabwe

    collaborator OTHER

  • St George's, University of London

    lead OTHER

Eligibility

Sex
FEMALE
Healthy Volunteers
Yes

Timeline & Regulatory

Start
2024-04-11
Primary Completion
2024-12-31
Completion
2025-03-01

Countries

  • Uganda
  • United Kingdom

Study Locations

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Read the full study record

This page highlights key information. For complete eligibility criteria, study locations, investigator contacts, and the full protocol, visit the original record on ClinicalTrials.gov.

View NCT06411405 on ClinicalTrials.gov