Machine Learning From Fetal Flow Waveforms to Predict Adverse Perinatal Outcomes

NCT03398551 | Unknown

• 1 other identifier: 4872-Ped-ERC-17
• Study Type: observational
• Target: 525
• Locations: 0 countries
• Sites: N/A

Brief Summary

The aim of this study is to get a proof of concept for using a computational model of fetal haemodynamics, combined with machine learning based on Doppler patterns of the fetal cardiovascular, cerebral and placental flows, to identify those at increased risk of adverse perinatal outcomes such as stillbirth, perinatal mortality and other neonatal morbidities. We will also compare the sensitivity and specificity of UmbiFlow device with the machine learning model in predicting adverse perinatal outcomes

Conditions

Perinatal Mortality; Neonatal Morbidities

Keywords

pregnancy; machine learning; fetal Doppler echocardiography

Outcome Measures

Primary Outcomes (2)

• Stillbirth

  Baby born with no signs of life at or after 28 weeks of gestation

  At birth

• Early neonatal mortality

  Death of a baby within the first 7 days of life

  Within the first 7 days of life

Secondary Outcomes (5)

• IUGR

  At birth

• Prematurity

  At birth

• Birth asphyxia

  At birth

• Neonatal sepsis

  Within the first 7 days of life

• Low Birth Weight

  At birth

Eligibility Criteria

• Gender Eligibility Details: This study will recruit pregnant women only.
• Healthy Volunteers: Yes
• Age Groups: Child (0-17), Adult (18-64), Older Adult (65+)
• Sampling Method: Non-Probability Sample

Study Population

Pregnant women between 22-34 weeks who reside in Ibrahim Hyderi Goth which is a peri-urban settlement of approximately 70,000 on the south east of Karachi. It is one of the seven union councils in Bin Qasim town which has a population of one million.

You may qualify if:

• Pregnant woman coming to the ultrasound clinic between 22-34 weeks of gestation.
• Written informed consent
• Resident of the study area

You may not qualify if:

• Multiple gestation
• Known congenital anomaly in the fetus or newborn
• Refusal for the ultrasound
• Poor echocardiographic images for Doppler acquisition

Sponsors & Collaborators

• Aga Khan University: lead
• Universitat Pompeu Fabra: collaborator

MeSH Terms

Conditions

Perinatal Death

Condition Hierarchy (Ancestors)

Pregnancy Complications; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Death; Pathologic Processes; Pathological Conditions, Signs and Symptoms

Central Study Contacts

Zahra Hoodbhoy

CONTACT

92-21-34930051; zahra.hoodbhoy@aku.edu

Babar Hasan

CONTACT

92-21-34930051; babar.hasan@aku.edu

Study Design

• Study Type: observational
• Observational Model: COHORT
• Time Perspective: PROSPECTIVE
• Sponsor Type: OTHER
• Responsible Party: PRINCIPAL INVESTIGATOR
• PI Title: Dr Zahra Hoodbhoy

Study Record Dates

• First Submitted: January 6, 2018
• First Posted: January 12, 2018
• Study Start: February 1, 2018
• Primary Completion: September 1, 2018
• Study Completion: December 1, 2018
• Last Updated: January 12, 2018

Record last verified: 2018-01