用Nomogram模型预测长方案组新鲜周期中重度卵巢过度刺激综合征的发生风险。

IF 3.9 2区 综合性期刊 Q1 MULTIDISCIPLINARY SCIENCES
Huihui Deng, Qian Dou, Peipei Guo, Huanxin Liu, Yungai Xiang, Xujing Geng, Pengfen Li, Dan Zhang
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引用次数: 0

摘要

探讨新鲜周期中使用卵泡早期长效促性腺激素释放激素激动剂长方案(EFLL)组患者发生中至重度卵巢过度刺激综合征(ovarian hyperstimulation syndrome, OHSS)的危险因素,建立预测中至重度OHSS风险的nomogram模型。我们回顾性分析2015年1月至2024年8月在郑州大学第二附属医院生殖医学科接受体外受精/胞浆内单精子注射和胚胎移植(IVF/ICSI-ET)的4204例患者的临床资料。采用EFLL方案的病例共4204例。将临床病例按7:3的比例随机分为建模组(2942例)和验证组(1262例)。采用Logistic回归分析确定与新鲜周期中重度OHSS发生相关的独立危险因素,根据选取的独立危险因素及相关回归系数,建立预测该患者中重度OHSS发生概率的nomogram模型,并采用受试者工作特征(ROC)曲线下面积、校正曲线下面积、以及决策曲线分析。单因素和多因素logistic回归分析显示,心房卵泡计数(AFC) (OR, 1.04;95%置信区间,1.02 - -1.07;P = 0.002),注射hCG当日雌激素水平(OR, 1.01;95%置信区间,1.01 - -1.01;P
本文章由计算机程序翻译,如有差异,请以英文原文为准。

Nomogram model to predict the risk of moderate to severe ovarian hyperstimulation syndrome of long protocol group in fresh cycle.

Nomogram model to predict the risk of moderate to severe ovarian hyperstimulation syndrome of long protocol group in fresh cycle.

Nomogram model to predict the risk of moderate to severe ovarian hyperstimulation syndrome of long protocol group in fresh cycle.

Nomogram model to predict the risk of moderate to severe ovarian hyperstimulation syndrome of long protocol group in fresh cycle.

To explore the risk factors of moderate to severe ovarian hyperstimulation syndrome (Ovarian hyperstimulation syndrome, OHSS) in patients using the early-follicular phase long-acting gonadotropin-releasing hormone agonist long protocol (EFLL) group in fresh cycles, and to establish a nomogram model to predict the risk of moderate to severe OHSS. We retrospectively analyzed clinical data from 4,204 patients who receiving in vitro fertilization/intracytoplasmic sperm injection and embryo transfer (IVF/ICSI-ET) at the Reproductive Medicine Department of the Second Affiliated Hospital of Zhengzhou University from January 2015 to August 2024. A total of 4204 cases using EFLL protocol were included. The clinical cases were randomly divided into a modeling group (2,942 cases) and a verification group (1,262 cases) at a ratio of 7:3. Logistic regression analysis was used to identify the independent risk factors associated with the occurrence of moderate to severe OHSS in fresh cycles, Based on the selected independent risk factors and correlated regression coefficients, we established a nomogram model to predict the probability of moderate to severe OHSS in this patients, and the predictive accuracy of the model was measured using the area under the receiver operating characteristic (ROC) curve, calibration curve, and decision curve analysis. Univariate and multivariate logistic regression analyses showed that Antal follicle count (AFC) (OR, 1.04; 95%CI, 1.02-1.07; P = 0.002), estrogen levels on the day of hCG injection (OR, 1.01; 95%CI, 1.01-1.01; P<0.01), progesterone levels on the day of hCG injection (OR, 1.18; 95%CI, 1.04-1.34; P = 0.011),, whether the patient had a hypothyroidism (OR, 3.62; 95%CI, 2.10-6.23; P < 0.001), and infertility type (OR, 0.59; 95%CI, 0.35-0.99; P = 0.048) are the independent risk factors for the occurrence of moderate to severe OHSS in fresh cycles. The ROC curve (AUC) being 0.83 (95% CI: 0.78-0.88) for the modeling group and 0.84 (95% CI: 0.78-0.90) for the validation group. The calibration curve and decision curve demonstrated good consistency between the predicted rates of moderate to severe OHSS and the actual incidence. AFC, estrogen levels on the day of hCG injection, progesterone levels on the day of hCG injection, whether the patient had a hypothyroidism, and infertility type are the independent risk factors for moderate to moderate to severe OHSS in fresh cycles. The established nomogram model has proven to be a novel tool that can intuitively predict the incidence of moderate to severe OHSS in the patients that receiving EFLL protocol in fresh cycles, and this nomogram model developed in this study showed better net benefit, have a good clinical applicability for decision-making and could help the clinician to set up a better clinical management strategies for conducting a precise personal therapy.

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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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