Role of Inflammatory Markers and Doppler Parameters in Late-Onset Fetal Growth Restriction: A Machine Learning Approach

Inflammatory Response Clinical Trial

Official title:

Role of Inflammatory Markers and Doppler Parameters in Late-Onset Fetal Growth Restriction: A Machine Learning Approach

Clinical Trial Details — Status: Active, not recruiting

Administrative data

• NCT number: NCT06372938
• Other study ID #: AESH-BADEK-2024-43
• Status: Active, not recruiting
• 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
• 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.

Recruitment information / eligibility

• Status: Active, not recruiting
• Enrollment: 240
• Est. completion date: April 30, 2024
• Est. primary completion date: April 30, 2024
• Accepts healthy volunteers: Accepts Healthy Volunteers
• Gender: Female
• Age group: 18 Years to 45 Years

Eligibility

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

Related Conditions & MeSH terms

• Fetal Growth Restriction
• Fetal Growth Retardation
• Inflammatory Response

Intervention

Diagnostic Test:
• Ultrasound measurement
The diagnosis of FGR was made according to the following Delphi criteria . EFW <3rd percentile or EFW <10th percentile with Doppler evidence of placental dysfunction (Umbilical artery Doppler (UA) pulsatility index (PI) >95th percentile, absence of umbilical artery end-diastolic flow (UAEDF), or reverse-UAEDF and/or cerebroplacental ratio (CPR) <5th percentile).
• Laboratory Tests and Inflammatory Markers
The laboratory values were measured at the time of FGR diagnosis (between 32 and 37 weeks of pregnancy). After evaluation of hemoglobin (g/dl), leukocytes (103/µL), monocytes (103/µL), lymphocytes (103/µL), neutrophils (103/µL), platelets (103/µL) and albumin (g/dl), the inflammation values were calculated as follows: ; SII = Absolute platelet count (APC)* Absolute neutrophil count (ANC) / Absolute lymphocyte count (ALC); SIRI = Absolute monocyte count (AMC) * ANC/ ALC; NPAR = Proportion of neutrophils (in total leukocytes) (%) × 100/albumin (g/dL).

Locations

Country	Name	City	State
Turkey	Etlik City Hospital	Ankara	Yenimahalle

Sponsors (1)

Lead Sponsor	Collaborator
Ankara Etlik City Hospital

Country where clinical trial is conducted

Turkey, 

Outcome

Type	Measure	Description	Time frame	Safety issue
Primary	Evaluation of data	To determine the statistical correlation of demographic data and inflammatory indices of pregnancy period with diagnostic ultrasonographic measurements (fetal biometric measurements and fetal doppler findings) related to fetal growth retardation in SPSS environment and to reveal the importance of the relationship.	Within 1 month of data collection
Secondary	Machine learning modeling	The RandomForestClassifier class classification model will be developed by moving the data from the SPSS environment to the Python environment. Machine learning system modeling will be developed where the model will learn from the training set using patient data and use this information to predict future data.	Within 1 month of data after data analysis