Clinical Trial Details
— Status: Active, not recruiting
Administrative data
NCT number |
NCT06372938 |
Other study ID # |
AESH-BADEK-2024-43 |
Secondary ID |
|
Status |
Active, not recruiting |
Phase |
|
First received |
|
Last updated |
|
Start date |
January 31, 2024 |
Est. completion date |
April 30, 2024 |
Study information
Verified date |
April 2024 |
Source |
Ankara Etlik City Hospital |
Contact |
n/a |
Is FDA regulated |
No |
Health authority |
|
Study type |
Observational
|
Clinical Trial Summary
Fetal growth restriction (FGR) is a serious complication in pregnancy that can lead to
various adverse outcomes. It's classified into early-onset (before 32 weeks) and late-onset
(after 32 weeks), with late-onset associated with long-term risks like hypoxemia and
developmental delays. The study focuses on the role of inflammation in FGR, introducing new
blood markers for better understanding and diagnosis. It also addresses the challenges of
using advanced diagnostic tools in low-resource settings and explores the use of machine
learning to predict FGR based on inflammatory markers, highlighting the potential of
artificial intelligence in overcoming these challenges.
Description:
Fetal growth restriction (FGR), also known as intrauterine growth restriction, is a prevalent
pregnancy complication with potentially negative outcomes for newborns. The condition's
causes are varied, involving genetic factors, maternal inflammation, infections, and other
pathologies. FGR is categorized based on its onset: early-onset FGR occurs before 32 weeks'
gestation, while late-onset happens after 32 weeks. Late-onset FGR, though less risky in
perinatal complications compared to early-onset, is linked to an increased risk of hypoxemia
and neurodevelopmental delays. Diagnosis primarily relies on ultrasound measurements and
Doppler flow analysis of specific arteries. The study highlights the complexity of diagnosing
and managing late-onset FGR, emphasizing the unclear pathophysiological mechanisms. It
proposes the exploration of inflammatory processes and the potential role of new markers such
as the systemic immune inflammation index (SII), systemic inflammatory response index (SIRI),
and neutrophil-percentage-to-albumin ratio (NPAR) for understanding FGR. These markers are
easily measured through blood tests and are significant in various diseases. The text also
discusses the challenges of applying advanced diagnostic methods in low-income countries due
to the need for sophisticated equipment, contrasting with the accessibility of artificial
intelligence and machine learning models via the internet. The study aimed to assess the
impact of inflammatory processes on late-onset FGR by analyzing NPAR, along with other
markers, and evaluating their predictive value using machine learning algorithms.