Feng Fang, Tuo Hui-Hui, Yao Jin-Meng, Wang Wei-Hong, Guo Feng-Lan, An Rui-Fang
Department of Gynecology and Obstetrics, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Department of Dermatology, The First Affiliated Hospital of Xi'an Jiaotong University, Xi'an, Shaanxi, China.
Front Oncol. 2025 Mar 18;15:1559087. doi: 10.3389/fonc.2025.1559087. eCollection 2025.
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.
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.
Among the 953 women, the ECC positive rate was 31.48% (300/953). Logistic regression analysis identified age (<0.001), human papillomavirus (HPV) status (<0.01), cytology results (<0.05), acetowhite changes (<0.01), Lugol staining (<0.01), and colposcopic impression (<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 value of 10.489 (=0.2324), and the calibration and clinical decision curves demonstrated that the model exhibited satisfactory calibration and clinical utility.
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.
本研究旨在分析接受宫颈管搔刮术（ECC）患者的临床特征，确定影响ECC阳性的因素，并建立一个预测模型来评估ECC结果为阳性的风险。目标是协助临床医生做出ECC决策并减少宫颈病变的漏诊。
对2021年10月至2023年9月期间因筛查结果异常在西安交通大学第一附属医院妇科门诊接受阴道镜引导下活检和ECC的953例患者进行回顾性分析。采用单因素和多因素逻辑回归分析来确定ECC阳性的预测因素。使用R Studio开发了一个ECC阳性风险的个体化预测模型，随后对该模型进行评估和验证。
在953名女性中，ECC阳性率为31.48%（300/953）。逻辑回归分析确定年龄（<0.001）、人乳头瘤病毒（HPV）状态（<0.01）、细胞学结果（<0.05）、醋酸白改变（<0.01）、卢戈氏碘染色（<0.01）和阴道镜印象（<0.01）为ECC阳性的独立预测因素。这些因素被纳入ECC阳性风险预测模型。该模型的受试者工作特征曲线（AUC）下面积为0.792（95%CI：0.760-0.824）。Hosmer-Lemeshow检验得出的值为10.489（=0.2324），校准和临床决策曲线表明该模型具有令人满意的校准和临床实用性。
本研究开发的临床预测模型具有良好的区分度、校准度和临床实用性。它可用于评估接受阴道镜检查患者ECC阳性的风险，减少宫颈病变的漏诊，并帮助临床医生做出ECC决策。
