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

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.

Study Overview

• Status: Active, not recruiting
• Conditions: Inflammatory Response; Fetal Growth Restriction
• Intervention / Treatment: Diagnostic test: Ultrasound measurement; Diagnostic test: Laboratory Tests and Inflammatory Markers

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.

• Study Type: Observational
• Enrollment (Actual): 240

Contacts and Locations

Study Locations

• Turkey
  • Yenimahalle
    • Ankara, Yenimahalle, Turkey, 06170
      • Etlik City Hospital

Participation Criteria

Eligibility Criteria

• Ages Eligible for Study: Adult
• Accepts Healthy Volunteers: Yes

• 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.

Description

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

Study Plan

How is the study designed?

Number of groups / cohorts

2

Cohorts and Interventions

Group / Cohort	Intervention / Treatment
Pregnant Women with Fetal Growth Restriction; 120 patients will be included diagnosed with late-onset Fetal Growth Restriction.	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).; Diagnostic test: 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).
Healthy Pregnancies; 120 patients will be included in a control group of developing fetuses according to gestational age.	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).; Diagnostic test: 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).

What is the study measuring?

Primary Outcome Measures

Outcome Measure	Measure Description	Time Frame
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 Outcome Measures

Outcome Measure	Measure Description	Time Frame
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

Collaborators and Investigators

• Sponsor: Ankara Etlik City Hospital

Study record dates

Study Major Dates

• Study Start (Actual): January 31, 2024
• Primary Completion (Estimated): July 6, 2024
• Study Completion (Estimated): July 20, 2024

Study Registration Dates

• First Submitted: April 7, 2024
• First Submitted That Met QC Criteria: April 16, 2024
• First Posted (Actual): April 18, 2024

Study Record Updates

• Last Update Posted (Actual): July 8, 2024
• Last Update Submitted That Met QC Criteria: July 4, 2024
• Last Verified: July 1, 2024

More Information

Terms related to this study

• Keywords: inflammatory markers; fetal growth restriction; machine learning algorithms
• Additional Relevant MeSH Terms: Pathologic Processes; Fetal Diseases; Pregnancy Complications; Growth Disorders; Female Urogenital Diseases and Pregnancy Complications; Urogenital Diseases; Fetal Growth Retardation
• Other Study ID Numbers: AEŞH-BADEK-2024-43

Plan for Individual participant data (IPD)

• Plan to Share Individual Participant Data (IPD)?: NO
• IPD Plan Description: Due to hospital policy, data cannot be shared. However, if necessary, the principal investigator can be contacted.

Drug and device information, study documents

• Studies a U.S. FDA-regulated drug product: No
• Studies a U.S. FDA-regulated device product: No