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使用机器学习模型评估绒毛膜下血肿女性的妊娠风险

Evaluation of Pregnancy Risks in Women with Subchorionic Hematoma Using Machine Learning Models.

作者信息

Wang Lan, Qin Aiping, Yang Yihua, Jin Yufu, Huang Qiuyan, Huang Xinyue, Feng Yu, Liang Ting

机构信息

Department of Gynecology, Guilin People's Hospital, Guilin, Guangxi, China.

Reproductive Center, Guangxi Medical University First Affiliated Hospital, Nanning, Guangxi, China.

出版信息

Med Sci Monit. 2024 Dec 21;30:e945472. doi: 10.12659/MSM.945472.

Abstract

BACKGROUND Subchorionic hematoma (SCH) can lead to blood accumulation and potentially affect pregnancy outcomes. Despite being a relatively common finding in early pregnancy, the effects of SCH on pregnancy outcomes such as miscarriage, stillbirth, and preterm birth remain debated. This study aims to address these gaps by systematically evaluating the influence of SCH-related clinical factors on pregnancy outcomes using robust analytical techniques. MATERIAL AND METHODS Data from SCH and non-SCH pregnant women were collected and split into training and test datasets. Machine learning classifiers and regression models were used to assess the impact of clinical indices on outcomes such as delivery type, NICU transfer, gestational age, and birth weight. Results were evaluated using ROC and calibration plots. RESULTS (1) SCH women had a significantly higher risk of stillbirth or miscarriage than non-SCH women (P<0.001). Logistic regression and XGB models showed AUCs of 0.858 and 0.916, respectively. Key factors affecting delivery outcomes included the first positive HCG level, hematoma duration, CA125 level, gestational sac diameter, fibrinogen level, and spouse age. (2) 12.7% of successfully delivered SCH newborns required NICU transfer, but clinical indices did not predict NICU need (AUC 0.589 and 0.629). (3) Successfully delivered SCH women had longer gestational ages than those with miscarriage/stillbirth (38.8 vs 10.1 weeks), but indices did not predict preterm/full-term birth (AUCs 0.449 and 0.503). (4) Birth weight was significantly affected by live birth times and gestational age (P<0.05), though the adjusted R-square was 0.226. CONCLUSIONS (1) SCH increases miscarriage or stillbirth risk. (2) the first positive HCG level, the hematoma duration, serum CA125 level, the gestational sac maximum diameter, fibrinogen, and the spouse age highly impacted the delivery outcome. (3) SCH indices do not affect NICU transfer or birth weight. (4) Miscarriage/stillbirth mainly occurs in the first trimester; passing this stage often leads to successful delivery. (5) The birth weight of full-term newborns is significantly higher than that of preterm infants. The clinical indices of SCH pregnant women have no impact on the birth weight of the newborn.

摘要

背景

绒毛膜下血肿(SCH)可导致血液积聚,并可能影响妊娠结局。尽管在早孕中这是一个相对常见的发现,但SCH对流产、死产和早产等妊娠结局的影响仍存在争议。本研究旨在通过使用稳健的分析技术系统评估与SCH相关的临床因素对妊娠结局的影响,以填补这些空白。

材料与方法

收集SCH孕妇和非SCH孕妇的数据,并分为训练集和测试集。使用机器学习分类器和回归模型评估临床指标对分娩类型、新生儿重症监护病房(NICU)转诊、孕周和出生体重等结局的影响。使用ROC曲线和校准图评估结果。

结果

(1)SCH孕妇死产或流产的风险显著高于非SCH孕妇(P<0.001)。逻辑回归和XGB模型的AUC分别为0.858和0.916。影响分娩结局的关键因素包括首次阳性人绒毛膜促性腺激素(HCG)水平、血肿持续时间、CA125水平、妊娠囊直径、纤维蛋白原水平和配偶年龄。(2)12.7%成功分娩的SCH新生儿需要转入NICU,但临床指标无法预测NICU需求(AUC为0.589和0.629)。(3)成功分娩的SCH孕妇孕周比流产/死产孕妇长(38.8周对10.1周),但指标无法预测早产/足月产(AUC分别为0.449和0.503)。(4)出生体重受活产次数和孕周的显著影响(P<0.05),尽管调整后的决定系数为0.226。

结论

(1)SCH增加流产或死产风险。(2)首次阳性HCG水平、血肿持续时间、血清CA125水平、妊娠囊最大直径、纤维蛋白原和配偶年龄对分娩结局影响很大。(3)SCH指标不影响NICU转诊或出生体重。(4)流产/死产主要发生在孕早期;度过此阶段通常会成功分娩。(5)足月新生儿出生体重显著高于早产儿。SCH孕妇的临床指标对新生儿出生体重无影响。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/cfd6/11669258/312db277d8d6/medscimonit-30-e945472-g001.jpg

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