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基于LASSO逻辑回归的列线图预测多囊卵巢综合征女性活产情况的开发与验证:一项回顾性队列研究

Development and validation of a LASSO logistic regression based nomogram for predicting live births in women with polycystic ovary syndrome: a retrospective cohort study.

作者信息

Liu Yue, Gao Jingshu, Ge Hang, Feng Jiaxing, Wang Yu, Wu Xiaoke

机构信息

Graduate School, Heilongjiang University of Chinese Medicine, Harbin, China.

The First Affiliated Hospital, Heilongjiang University of Chinese Medicine, Harbin, China.

出版信息

Front Endocrinol (Lausanne). 2025 May 27;16:1525823. doi: 10.3389/fendo.2025.1525823. eCollection 2025.

Abstract

OBJECTIVE

There is limited study on predictive models for live births in patients with polycystic ovarian syndrome (PCOS). The study aimed to develop and validate a nomogram for predicting live births in Chinese women with PCOS, as well as to identify the risk factors affecting live births in this population.

METHODS

Data for this study were obtained from a large clinical trial known as PCOSAct in mainland China. A total of 927 patients with PCOS were investigated using stratified random sampling. The mean-filling method was used to address missing data, and Lasso-Logistic regression was combined with machine learning models to identify the most significant predictors of live births. A nomogram was constructed based on a multivariate logistic regression model and was evaluated using receiver operating characteristic curves (ROC), calibration curves and clinical decision curves to assess model discrimination, calibration and clinical validity.

RESULTS

In the training set, estradiol, potassium ion, total cholesterol, alanine aminotransferase, and free androgen index were identified as key risk factors influencing live birth rates in PCOS patients. In this study, we demonstrated that elevated levels of estradiol and potassium ions, coupled with reduced levels of total cholesterol, alanine aminotransferase, and free androgen index (FAI), significantly improved insulin resistance (IR) in patients with polycystic ovary syndrome (PCOS). These biochemical alterations facilitated weight reduction and normalized endocrine function, thereby enhancing the live birth rate among PCOS patients. The nomogram developed from this multivariate model underwent validation by both internal (AUC:0.649; 95% CI [0.6050.694]) and external (AUC:0.709; 95% CI [0.6160.801]) assessments. The results have strong predictive accuracy, reliability, and generalizability for live birth.

CONCLUSION

The nomogram developed in this study is a valid tool for assessing live births in patients with PCOS. This tool will assist clinicians in assessing the risks associated with live births in patients with PCOS and enable them to implement more effective preventive measures.

摘要

目的

关于多囊卵巢综合征(PCOS)患者活产预测模型的研究有限。本研究旨在开发并验证一种用于预测中国PCOS女性活产情况的列线图,同时识别影响该人群活产的危险因素。

方法

本研究数据来自中国大陆一项名为PCOSAct的大型临床试验。采用分层随机抽样法对927例PCOS患者进行调查。使用均值填充法处理缺失数据,并将套索逻辑回归与机器学习模型相结合,以识别活产的最显著预测因素。基于多变量逻辑回归模型构建列线图,并使用受试者操作特征曲线(ROC)、校准曲线和临床决策曲线进行评估,以评估模型的辨别力、校准度和临床有效性。

结果

在训练集中,雌二醇、钾离子、总胆固醇、丙氨酸转氨酶和游离雄激素指数被确定为影响PCOS患者活产率的关键危险因素。在本研究中,我们证明,雌二醇和钾离子水平升高,同时总胆固醇、丙氨酸转氨酶和游离雄激素指数(FAI)水平降低,可显著改善多囊卵巢综合征(PCOS)患者的胰岛素抵抗(IR)。这些生化改变有助于体重减轻和内分泌功能正常化,从而提高PCOS患者的活产率。由该多变量模型开发的列线图通过内部(AUC:0.649;95%CI[0.6050.694])和外部(AUC:0.709;95%CI[0.6160.801])评估进行了验证。结果对活产具有很强的预测准确性、可靠性和可推广性。

结论

本研究开发的列线图是评估PCOS患者活产情况的有效工具。该工具将帮助临床医生评估PCOS患者活产相关风险,并使他们能够实施更有效的预防措施。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/2186/12148842/6575781d9917/fendo-16-1525823-g001.jpg

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