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

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.