Risk factors and nomogram construction for predicting women with chronic pelvic pain:a cross-sectional population
Mingyue Zhu1, Fei Huang2, Jingyun Xu3,1
1School of Medicine, Southeast University, Nanjing, 210009, Jiangsu, China.
Background:
Chronic pelvic pain (CPP) in women is a critical challenge. Due to the complex etiology and difficulties in diagnosis, it has a greatly negative impact on women's physical and mental health and the healthcare system. At present, there is still a lack of research on the related factors and predictive models of chronic pelvic pain in women. Our study aims to identify risk factors associated with chronic pelvic pain in women and develop a predictive nomogram specifically tailored to high-risk women with CPP.
Materials And Methods:
From May to October 2022, trained interviewers conducted face-to-face questionnaire surveys and pelvic floor surface electromyography assessments on women from community hospitals in Nanjing. We constructed a multivariate logistic regression-based predictive model using CPP-related factors to assess the risk of chronic pelvic pain and create a predictive nomogram. Both internal and external validations were conducted, affirming the model's performance through assessments of discrimination, calibration, and practical applicability using area under the curve, calibration plots, and decision curve analysis.
Results:
1108 women were recruited in total (survey response rate:1108/1200), with 169 (15.3 %) being diagnosed as chronic pelvic pain. Factors contributing to CPP included weight, dysmenorrhea, sexual dysfunction, urinary incontinence, a history of pelvic inflammatory disease, and the surface electromyography value of post-baseline rest. In both the training and validation sets, the nomogram exhibited strong discrimination abilities with areas under the curve of 0.85 (95 % CI: 0.81-0.88) and 0.85 (95 % CI: 0.79-0.92), respectively. The examination of the decision curve and calibration plot showed that this model fit well and would be useful in clinical settings.
Conclusions:
Weight, dysmenorrhea, sexual dysfunction, history of urinary incontinence and pelvic inflammatory disease, and surface electromyography value of post-baseline rest are independent predictors of chronic pelvic pain. The nomogram developed in this study serves as a valuable and straightforward tool for predicting chronic pelvic pain in women.
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