Development and validation of a predictive model for the risk of endocervical curettage positivity.

Abstract

Objective: This study aimed to analyze the clinical characteristics of patients undergoing endocervical curettage (ECC), identify factors influencing ECC positivity, and develop a predictive model to assess the risk of positive ECC results. The goal was to assist clinicians in making ECC decisions and reduce missed diagnoses of cervical lesions.

Methods: A retrospective analysis was performed on 953 patients who underwent colposcopically directed biopsy and ECC at the gynecology clinic of the First Affiliated Hospital of Xi'an Jiaotong University between October 2021 and September 2023 due to abnormal screening results. Univariate and multivariate logistic regression analyses were used to identify predictive factors for ECC positivity. An individualized prediction model for ECC positivity risk was developed using R Studio, and the model was subsequently evaluated and validated.

Results: Among the 953 women, the ECC positive rate was 31.48% (300/953). Logistic regression analysis identified age (P<0.001), human papillomavirus (HPV) status (P<0.01), cytology results (P<0.05), acetowhite changes (P<0.01), Lugol staining (P<0.01), and colposcopic impression (P<0.01) as independent predictors of ECC positivity. These factors were incorporated into the prediction model for ECC positivity risk. The area under the receiver operating characteristic curve (AUC) of the model was 0.792 (95% CI:0.760-0.824). The Hosmer-Lemeshow test yielded a χ² value of 10.489 (P=0.2324), and the calibration and clinical decision curves demonstrated that the model exhibited satisfactory calibration and clinical utility.

Conclusions: The clinical prediction model developed in this study demonstrated good discrimination, calibration, and clinical utility. It can be used to evaluate the risk of ECC positivity in patients undergoing colposcopy, reduce missed diagnoses of cervical lesions, and aid clinicians in making ECC decisions.