Wang Shuhua, Ma Zhaopeng, Dong Jing, Zhang Na, Zhang Xuemei, Li Heying, Chen Li
Department of Gynecology, Baoding First Central Hospital, Baoding, Hebei, China.
Department of Endocrinology, Baoding First Central Hospital, Baoding, Hebei, China.
Medicine (Baltimore). 2025 Jun 6;104(23):e42759. doi: 10.1097/MD.0000000000042759.
The objective was to develop a nomogram for predicting positive margins after cold knife conization (CKC) in patients with high-grade squamous intraepithelial lesion (HSIL). This retrospective study included patients who underwent CKC at Baoding No. 1 Central Hospital between December 2013 and March 2024. Patients were divided into training (between December 2013 and December 2022) and validation (between January 2023 and March 2024) sets. The least absolute shrinkage and selection operator regression was applied to filter and select relevant variables. Multivariable logistic regression was used for nomogram construction. The model performance was evaluated using various methods, including receiver operating characteristics, decision curve analysis, and calibration analysis. The training and validation sets included 985 and 227 patients, respectively. Age (OR = 1.046, 95% CI: 1.028-1.064, P < .001), cervical intraepithelial neoplasia quadrants by punch biopsy (OR = 1.561, 95% CI: 1.348-1.808, P < .001), HSIL type (OR = 1.711, 95% CI: 1.102-2.657, P = .017), and gland involvement (OR = 1.552, 95% CI: 1.073-2.247, P = .020) were associated with positive margins and used for nomogram construction. The predictive model yielded area under the curves of 0.744 and 0.754 in the training and validation sets, respectively. Decision curve analysis indicated a net benefit when using the nomogram, and the calibration curves demonstrated a good fit. This study constructed a nomogram model for predicting positive margins after CKC in patients with HSIL. This nomogram may enable early and accurate patient evaluation, potentially improving clinical outcomes.
目的是开发一种列线图，用于预测高级别鳞状上皮内病变（HSIL）患者冷刀锥切术（CKC）后切缘阳性情况。这项回顾性研究纳入了2013年12月至2024年3月期间在保定市第一中心医院接受CKC的患者。患者被分为训练组（2013年12月至2022年12月）和验证组（2023年1月至2024年3月）。应用最小绝对收缩和选择算子回归来筛选和选择相关变量。使用多变量逻辑回归构建列线图。采用多种方法评估模型性能，包括受试者工作特征曲线、决策曲线分析和校准分析。训练组和验证组分别包括985例和227例患者。年龄（OR = 1.046，95%CI：1.028 - 1.064，P <.001）、经穿刺活检的宫颈上皮内瘤变象限（OR = 1.561，95%CI：1.348 - 1.808，P <.001）、HSIL类型（OR = 1.711，95%CI：1.102 - 2.657，P = 0.017）和腺体受累情况（OR = 1.552，95%CI：1.073 - 2.247，P = 0.020）与切缘阳性相关，并用于构建列线图。预测模型在训练组和验证组中的曲线下面积分别为0.744和0.754。决策曲线分析表明使用列线图有净获益，校准曲线显示拟合良好。本研究构建了一种用于预测HSIL患者CKC后切缘阳性的列线图模型。该列线图可能有助于早期准确地评估患者，潜在地改善临床结局。
