Analysis of dysmenorrhea-related factors in adenomyosis and development of a risk prediction model

IF 2.1 3区 医学 Q2 OBSTETRICS & GYNECOLOGY
Yudan Fu, Xin Wang, Xinchun Yang, Ruihua Zhao
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引用次数: 0

Abstract

Objective

To explore factors related to dysmenorrhea in adenomyosis and construct a risk prediction model.

Methods

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.

Results

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.

Conclusion

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.

分析子宫腺肌症患者痛经的相关因素并建立风险预测模型。
目的:探讨子宫腺肌症患者痛经的相关因素并建立风险预测模型。方法:对全国37家医院(2019年11月- 2022年2月)1636例患者进行横断面调查。收集人口统计学、疾病史、月经和生育史、治疗史等资料。患者分为痛经组和非痛经组。多因素logistic回归分析影响痛经的因素,采用nomogram建立痛经风险预测模型。通过ROC曲线分析、c -指数、Hosmer-Lemeshow检验和bootstrap方法对模型的性能进行评价,采用nomogram function建立nomogram model。采用ROC曲线下面积(AUC)、c指数、Hosmer-Lemeshow拟合优度检验和自举法对模型进行评价。根据nomogram对患者进行评分,并划分高危人群。结果:61.31%(1003/1636)的患者出现痛经。单因素分析显示差异有统计学意义(P = 88.5分为高危组)。结论:子宫腺肌症患者以痛经为主。识别和减轻风险因素,同时利用保护因素,有助于预防和管理。该模型能有效预测痛经风险,便于早期干预和治疗。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
CiteScore
4.70
自引率
15.40%
发文量
493
审稿时长
1 months
期刊介绍: Founded in 1870 as "Archiv für Gynaekologie", Archives of Gynecology and Obstetrics has a long and outstanding tradition. Since 1922 the journal has been the Organ of the Deutsche Gesellschaft für Gynäkologie und Geburtshilfe. "The Archives of Gynecology and Obstetrics" is circulated in over 40 countries world wide and is indexed in "PubMed/Medline" and "Science Citation Index Expanded/Journal Citation Report". The journal publishes invited and submitted reviews; peer-reviewed original articles about clinical topics and basic research as well as news and views and guidelines and position statements from all sub-specialties in gynecology and obstetrics.
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