Role of Inflammatory Markers and Doppler Parameters in Late-Onset Fetal Growth Restriction: A Machine-Learning Approach.
Doppler ultrasound
fetal growth restriction
inflammatory markers
late‐onset FGR
machine learning
predictive model
Journal
American journal of reproductive immunology (New York, N.Y. : 1989)
ISSN: 1600-0897
Titre abrégé: Am J Reprod Immunol
Pays: Denmark
ID NLM: 8912860
Informations de publication
Date de publication:
Oct 2024
Oct 2024
Historique:
revised:
21
09
2024
received:
08
08
2024
accepted:
05
10
2024
medline:
18
10
2024
pubmed:
18
10
2024
entrez:
18
10
2024
Statut:
ppublish
Résumé
This study evaluates the association of novel inflammatory markers and Doppler parameters in late-onset FGR (fetal growth restriction), utilizing a machine-learning approach to enhance predictive accuracy. A retrospective case-control study was conducted at the Department of Perinatology, Ministry of Health Etlik City Hospital, Ankara, from 2023 to 2024. The study included 240 patients between 32 and 37 weeks of gestation, divided equally between patients diagnosed with late-onset FGR and a control group. We focused on novel inflammatory markers-systemic immune-inflammation index (SII), systemic inflammatory response index (SIRI), and neutrophil-percentage-to-albumin ratio (NPAR)-and their correlation with Doppler parameters of umbilical and uterine arteries. Machine-learning algorithms were employed to analyze the data collected, including demographic, neonatal, and clinical parameters, to develop a predictive model for FGR. The machine-learning model, specifically the Random Forest algorithm, effectively integrated the inflammatory markers with Doppler parameters to predict FGR. NPAR showed a significant correlation with FGR presence, providing a robust tool in the predictive model (Accuracy 77%, area under the curve [AUC] 0.851). In contrast, SII and SIRI, while useful, did not achieve the same level of predictive accuracy (Accuracy 75% AUC 0.818 and Accuracy 73% AUC 0.793, respectively). The model highlighted the potential of combining ultrasound measurements with inflammatory markers to improve diagnostic accuracy for late-onset FGR. This study illustrates the efficacy of integrating machines with traditional diagnostic methods to enhance the prediction of late-onset FGR. Further research with a larger cohort is recommended to validate these findings and refine the predictive model, which could lead to improved clinical outcomes for affected pregnancies. ClinicalTrials.gov identifier: NCT06372938.
Substances chimiques
Biomarkers
0
Banques de données
ClinicalTrials.gov
['NCT06372938']
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
e70004Informations de copyright
© 2024 John Wiley & Sons A/S. Published by John Wiley & Sons Ltd.
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