Clinical trial NCT06372938: Role of Inflammatory Markers and Doppler Parameters in Late-Onset Fetal Growth Restriction: A Machine Learning Approach
Official title: Role of Inflammatory Markers and Doppler Parameters in Late-Onset Fetal Growth Restriction: A Machine Learning Approach
Study type: OBSERVATIONAL
Overall status: ACTIVE_NOT_RECRUITING
Conditions: Fetal Growth Restriction
Inflammatory Response
Keywords: fetal growth restriction
inflammatory markers
machine learning algorithms
Lead sponsor: Ankara Etlik City Hospital
Sponsor class: OTHER_GOV
First posted: 2024-04-18
Enrollment: 240
Sex: FEMALE
Minimum age (years): 18
Maximum age (years): 45
Standard ages: ADULT
Accepts healthy volunteers: yes
Observational model: CASE_CONTROL
Time perspective: RETROSPECTIVE
Sampling method: PROBABILITY_SAMPLE
Study population: This study will include 240 patients between 32-37 weeks of gestational age who were admitted to the Perinatology Clinic, Ministry of Health, Etlik City Hospital, Ankara/Turkey between 2023 and 2024. Head-hip length in the first trimester was used to confirm gestational age. Of the patients included in the study, 120 patients diagnosed with late-onset FGR and 120 patients with developing fetuses according to gestational age will be included in the control group.

Brief 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.

Detailed 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.

Eligibility criteria: Inclusion Criteria:

* Between the ages of 18-45
* Completed their pregnancy follow-up in our center
* Pregnant women whose data can be accessed
* Singleton pregnancies without systemic maternal comorbidities other than FGR

Exclusion Criteria:

* Multiple pregnancies
* Having a maternal disease
* Fetal congenital and chromosomal anomalies
* Chronic drug use, alcohol and cigarette use
* Accompanying additional pregnancy complications during follow-up
* Cases whose data cannot be accessed

Primary outcomes:
measure: Evaluation of data
timeFrame: Within 1 month of data collection
Secondary outcomes:
measure: Machine learning modeling
timeFrame: Within 1 month of data after data analysis
Other outcomes:
Locations:
city: Ankara
country: Turkey
facility: Etlik City Hospital
state: Yenimahalle
Study officials:
References: