Sheng Binyue, Yao Dongmei, Du Xin, Chen Dejun, Zhou Limin
Department of Gynaecology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Hongshan, Wuhan, Hubei 430070, PR China.
Department of Gynaecology, Maternal and Child Health Hospital of Hubei Province, Tongji Medical College, Huazhong University of Science and Technology, Hongshan, Wuhan, Hubei 430070, PR China.
Eur J Obstet Gynecol Reprod Biol. 2023 Feb;281:1-6. doi: 10.1016/j.ejogrb.2022.12.005. Epub 2022 Dec 6.
To establish and validate a risk prediction model for cervical high-grade squamous intraepithelial lesions (HSIL).
This retrospective study included patients who underwent cervical biopsies at the Cervical Disease Centre of Maternal and Child Hospital of Hubei Province between January 2021 and December 2021.
A total of 1630 patients were divided into the HSIL + cervical lesion group (n = 186) and the ≤ LSIL cervical lesions group (n = 1444). LSIL, ASC-H, HSIL and SCC, high-risk HPV, HPV16, HPV18/45, multiple HPV strains, acetowhite epithelium, atypical vessels, and mosaicity were independently associated with HSIL + lesions. These factors were used to establish a risk prediction model with a demonstrated area under the curve (AUC) of 0.851 and a C-index of 0.829. Calibration curve analysis showed that the model performed well, with a mean absolute error (MAE) of 0.005. The decision curve showed that the model created by combining the risk factors was more specific and sensitive than each predictive variable.
The model for predicting HSIL demonstrated promising predictive capability and might help identify patients requiring biopsy and treatment.
建立并验证宫颈高级别鳞状上皮内病变（HSIL）的风险预测模型。
本回顾性研究纳入了2021年1月至2021年12月在湖北省妇幼保健院宫颈疾病中心接受宫颈活检的患者。
共1630例患者被分为HSIL + 宫颈病变组（n = 186）和≤ LSIL宫颈病变组（n = 1444）。LSIL、ASC-H、HSIL和SCC、高危型HPV、HPV16、HPV18/45、多种HPV毒株、醋酸白上皮、异型血管和镶嵌征与HSIL + 病变独立相关。利用这些因素建立了一个风险预测模型，其曲线下面积（AUC）为0.851，C指数为0.829。校准曲线分析表明该模型表现良好，平均绝对误差（MAE）为0.005。决策曲线表明，由风险因素组合创建的模型比每个预测变量更具特异性和敏感性。
预测HSIL的模型显示出有前景的预测能力，可能有助于识别需要活检和治疗的患者。
