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

IF 2.5 3区医学 Q3 IMMUNOLOGY American Journal of Reproductive Immunology Pub Date : 2024-10-18 DOI:10.1111/aji.70004

Can Ozan Ulusoy, Ahmet Kurt, Zeynep Seyhanli, Burak Hizli, Mevlut Bucak, Recep Taha Agaoglu, Yüksel Oguz, Kadriye Yakut Yucel

{"title":"Role of Inflammatory Markers and Doppler Parameters in Late-Onset Fetal Growth Restriction: A Machine-Learning Approach","authors":"Can Ozan Ulusoy, Ahmet Kurt, Zeynep Seyhanli, Burak Hizli, Mevlut Bucak, Recep Taha Agaoglu, Yüksel Oguz, Kadriye Yakut Yucel","doi":"10.1111/aji.70004","DOIUrl":null,"url":null,"abstract":"<div>\n \n \n <section>\n \n <h3> Objectives</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Materials and Methods</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Results</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Conclusions</h3>\n \n <p>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.</p>\n </section>\n \n <section>\n \n <h3> Trial Registration</h3>\n \n <p>ClinicalTrials.gov identifier: NCT06372938</p>\n </section>\n </div>","PeriodicalId":7665,"journal":{"name":"American Journal of Reproductive Immunology","volume":"92 4","pages":""},"PeriodicalIF":2.5000,"publicationDate":"2024-10-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"American Journal of Reproductive Immunology","FirstCategoryId":"3","ListUrlMain":"https://onlinelibrary.wiley.com/doi/10.1111/aji.70004","RegionNum":3,"RegionCategory":"医学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"IMMUNOLOGY","Score":null,"Total":0}

引用次数: 0

Abstract

Objectives

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.

Materials and Methods

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.

Results

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.

Conclusions

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.

Trial Registration

ClinicalTrials.gov identifier: NCT06372938

查看原文

微信好友朋友圈 QQ好友复制链接

本刊更多论文

炎症标记物和多普勒参数在晚期胎儿生长受限中的作用：机器学习方法

目的本研究利用机器学习方法提高预测的准确性，评估晚发 FGR（胎儿生长受限）中新型炎症标记物与多普勒参数的关联。材料与方法一项回顾性病例对照研究于 2023 年至 2024 年在安卡拉埃特里克市卫生部医院围产医学科进行。该研究包括 240 名妊娠 32 至 37 周的患者，诊断为晚发性 FGR 的患者和对照组各占一半。我们重点研究了新型炎症标记物--系统免疫炎症指数（SII）、系统炎症反应指数（SIRI）和中性粒细胞百分比与白蛋白比率（NPAR）--及其与脐动脉和子宫动脉多普勒参数的相关性。采用机器学习算法分析收集到的数据，包括人口统计学、新生儿和临床参数，以建立 FGR 的预测模型。结果机器学习模型，特别是随机森林算法，有效地整合了炎症标记物和多普勒参数来预测 FGR。NPAR 与 FGR 存在明显的相关性，为预测模型提供了强有力的工具（准确率 77%，曲线下面积 [AUC] 0.851）。相比之下，SII 和 SIRI 虽然有用，但预测准确率却没有达到同样的水平（准确率分别为 75% AUC 0.818 和 73% AUC 0.793）。该模型强调了将超声测量与炎症标志物相结合以提高晚发 FGR 诊断准确性的潜力。结论本研究说明了将机器与传统诊断方法相结合以提高晚发 FGR 预测的有效性。建议对更大的队列进行进一步研究，以验证这些发现并完善预测模型，从而改善受影响妊娠的临床结果。试验注册 ClinicalTrials.gov identifier：NCT06372938

本文章由计算机程序翻译，如有差异，请以英文原文为准。

求助全文

约1分钟内获得全文去求助

来源期刊

American Journal of Reproductive Immunology 医学-免疫学

CiteScore

6.20

自引率

5.60%

发文量

314

审稿时长

2 months

期刊介绍： The American Journal of Reproductive Immunology is an international journal devoted to the presentation of current information in all areas relating to Reproductive Immunology. The journal is directed toward both the basic scientist and the clinician, covering the whole process of reproduction as affected by immunological processes. The journal covers a variety of subspecialty topics, including fertility immunology, pregnancy immunology, immunogenetics, mucosal immunology, immunocontraception, endometriosis, abortion, tumor immunology of the reproductive tract, autoantibodies, infectious disease of the reproductive tract, and technical news.