TPOAb阳性患者不良生殖结局的影响因素分析及nomogram预测模型的建立

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Zhengwen Qin, Yamin Qiu, Jie Lin, Yi Yang, Li Hao, Lina He
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引用次数: 0

摘要

识别影响tpoab阳性患者生殖不良结局的因素,并基于这些因素建立预测模型,评估患者生殖不良结局的风险。对2020年1月至2022年12月在我院生殖医学门诊就诊的326例tpoab阳性女性患者进行回顾性队列研究。根据临床结果将患者分为有不良生殖结局和无不良生殖结局两组。采用SPSS 26.0版软件和R软件进行数据分析,通过单因素和多因素logistic回归分析,找出影响生殖不良结局的独立危险因素,构建nomogram预测模型。采用ROC曲线评估模型的预测性能。此外,在不良生殖结果组中进行了亚组分析。对反复流产、反复植入失败和无可用胚胎三个亚组进行Logistic回归分析,探讨每个亚组的具体危险因素,并比较每个亚组的预测模型的性能。单因素分析显示,年龄、AMH水平、TPOAb浓度、TSH水平和子宫内膜异位症是影响不良生殖结局的重要因素(P
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Analysis of the influencing factors for adverse reproductive outcomes in patients with positive TPOAb and the establishment of a nomogram prediction model.

Analysis of the influencing factors for adverse reproductive outcomes in patients with positive TPOAb and the establishment of a nomogram prediction model.

Analysis of the influencing factors for adverse reproductive outcomes in patients with positive TPOAb and the establishment of a nomogram prediction model.

Analysis of the influencing factors for adverse reproductive outcomes in patients with positive TPOAb and the establishment of a nomogram prediction model.

To identify the factors affecting adverse reproductive outcomes in TPOAb-positive patients and to establish a predictive model based on these factors to assess the risk of adverse reproductive outcomes in patients. A retrospective cohort study was conducted, including 326 TPOAb-positive female patients who visited the reproductive medicine clinic of our hospital from January 2020 to December 2022. Patients were divided into groups with adverse reproductive outcomes and without adverse reproductive outcomes based on clinical outcomes. Data analysis was performed using SPSS software version 26.0 and R software, and independent risk factors for adverse reproductive outcomes were identified through univariate and multivariate logistic regression analysis, followed by the construction of a nomogram predictive model. The predictive performance of the model was assessed using the ROC curve. Additionally, a subgroup analysis was conducted within the adverse reproductive outcomes group. Logistic regression analyses were performed for the three subgroups: recurrent miscarriage, repeated implantation failure, and no usable embryos, to explore specific risk factors for each subgroup and compare the performance of predictive models for each subgroup. Univariate analysis showed that age, AMH levels, TPOAb concentration, TSH levels, and endometriosis are significant factors affecting adverse reproductive outcomes (P < 0.05). Multivariate logistic regression analysis further confirmed these factors as independent risk factors for adverse reproductive outcomes. The established nomogram predictive model showed good predictive performance in both the training set (AUC = 0.901) and the validation set (AUC = 0.858). Subgroup analysis showed that TSH levels, TPOAb concentration, age, AMH levels, and endometriosis were common risk factors for the three groups, but their weights differed. The nomogram model demonstrated the best predictive performance in the RIF group (AUC = 0.926), while its predictive performance was relatively lower in the RPL group (AUC = 0.869). This study successfully established a nomogram predictive model for adverse reproductive outcomes in TPOAb-positive patients. Through subgroup analysis, we identified the specific risk factors and predictive performance for subgroups of recurrent miscarriage, repeated implantation failure, and unavailable embryos, providing a reference for precise clinical assessment and intervention.

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来源期刊
Scientific Reports
Scientific Reports Natural Science Disciplines-
CiteScore
7.50
自引率
4.30%
发文量
19567
审稿时长
3.9 months
期刊介绍: We publish original research from all areas of the natural sciences, psychology, medicine and engineering. You can learn more about what we publish by browsing our specific scientific subject areas below or explore Scientific Reports by browsing all articles and collections. Scientific Reports has a 2-year impact factor: 4.380 (2021), and is the 6th most-cited journal in the world, with more than 540,000 citations in 2020 (Clarivate Analytics, 2021). •Engineering Engineering covers all aspects of engineering, technology, and applied science. It plays a crucial role in the development of technologies to address some of the world''s biggest challenges, helping to save lives and improve the way we live. •Physical sciences Physical sciences are those academic disciplines that aim to uncover the underlying laws of nature — often written in the language of mathematics. It is a collective term for areas of study including astronomy, chemistry, materials science and physics. •Earth and environmental sciences Earth and environmental sciences cover all aspects of Earth and planetary science and broadly encompass solid Earth processes, surface and atmospheric dynamics, Earth system history, climate and climate change, marine and freshwater systems, and ecology. It also considers the interactions between humans and these systems. •Biological sciences Biological sciences encompass all the divisions of natural sciences examining various aspects of vital processes. The concept includes anatomy, physiology, cell biology, biochemistry and biophysics, and covers all organisms from microorganisms, animals to plants. •Health sciences The health sciences study health, disease and healthcare. This field of study aims to develop knowledge, interventions and technology for use in healthcare to improve the treatment of patients.
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