Fu Yudan, Wang Xin, Yang Xinchun, Zhao Ruihua
Department of Gynecology, Guang 'Anmen Hospital, Chinese Academy of Chinese Medical Sciences, No. 5, North Line Ge Street, Beijing, 10053, Xicheng District, PR China.
Arch Gynecol Obstet. 2025 Apr;311(4):1081-1089. doi: 10.1007/s00404-025-07967-y. Epub 2025 Mar 17.
To explore factors related to dysmenorrhea in adenomyosis and construct a risk prediction model.
A cross-sectional survey involving 1636 adenomyosis patients from 37 hospitals nationwide (November 2019-February 2022) was conducted. Data on demographics, disease history, menstrual and reproductive history, and treatment history was collected.Patients were categorized into dysmenorrhea and non-dysmenorrhea groups. Multivariate logistic regression analyzed factors influencing dysmenorrhea, and a risk prediction model was created using a nomogram. The model's performance was evaluated through ROC curve analysis, C-index, Hosmer-Lemeshow test, and bootstrap method The nomogram function was used to establish a nomogram model. The model was evaluated using the area under the ROC curve (AUC), C-index, Hosmer-Lemeshow goodness-of-fit test, and bootstrap method. Patients were scored based on the nomogram, and high-risk groups were delineated.
Dysmenorrhea was present in 61.31% (1003/1636) of the patients. Univariate analysis showed significant differences (P < 0.05) between groups in age at onset, course of disease, oligomenorrhea, menorrhagia, number of deliveries, pelvic inflammatory disease, family history of adenomyosis, exercise, and excessive menstrual fatigue. Significant factors included menorrhagia, multiple deliveries, pelvic inflammatory disease, and family history of adenomyosis as risk factors. Older age at onset, oligomenorrhea, and exercise were identified as protective factors. The model's accuracy, discrimination, and reliability were acceptable, and a risk score > 88.5 points indicated a high-risk group.
Dysmenorrhea is prevalent among adenomyosis patients. Identifying and mitigating risk factors, while leveraging protective factors, can aid in prevention and management. The developed model effectively predicts dysmenorrhea risk, facilitating early intervention and treatment.
探讨子宫腺肌病痛经的相关因素并构建风险预测模型。
对全国37家医院的1636例子宫腺肌病患者进行横断面调查（2019年11月至2022年2月）。收集人口统计学、疾病史、月经和生殖史以及治疗史的数据。患者被分为痛经组和非痛经组。采用多因素logistic回归分析影响痛经的因素，并使用列线图创建风险预测模型。通过ROC曲线分析、C指数、Hosmer-Lemeshow检验和自抽样法评估模型性能。使用列线图函数建立列线图模型。采用ROC曲线下面积（AUC）、C指数、Hosmer-Lemeshow拟合优度检验和自抽样法对模型进行评估。根据列线图对患者进行评分，并划定高危组。
61.31%（1003/1636）的患者存在痛经。单因素分析显示，两组在发病年龄、病程、月经过少、月经过多、分娩次数、盆腔炎、子宫腺肌病家族史、运动和经期过度疲劳方面存在显著差异（P < 0.05）。显著因素包括月经过多、多次分娩、盆腔炎和子宫腺肌病家族史为危险因素。发病年龄较大、月经过少和运动被确定为保护因素。该模型的准确性、区分度和可靠性均可接受，风险评分>88.5分表示为高危组。
痛经在子宫腺肌病患者中普遍存在。识别和减轻危险因素，同时利用保护因素，有助于预防和管理。所建立的模型能有效预测痛经风险，便于早期干预和治疗。
