Mei Ling, Gao Linbo, Wang Tao, Yang Dong, Chen Weixing, Niu Xiaoyu
Department of Obstetrics and Gynecology, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
Center of Translational Medicine, Key Laboratory of Birth Defects and Related Diseases of Women and Children, West China Second University Hospital, Sichuan University, Chengdu, 610041, China.
Int Urogynecol J. 2025 Feb 3. doi: 10.1007/s00192-025-06046-9.
We aimed to develop and validate a clinically applicable risk assessment model for identifying women at a high risk of pelvic organ prolapse (POP) based on a retrospective practice.
This study enrolled patients with and without POP between January 2019 and December 2021. Clinical data were collected and machine learning models were applied, such as multilayer perceptron, logistic regression, random forest (RF), light gradient boosting machine and extreme gradient boosting. Two datasets were constructed, one comprising all variables and the other excluding physical examination variables. Two versions of the machine learning model were developed. One was for professional doctors, and the other was for community-health providers. The area under the curve (AUC) and its confidence interval (CI), accuracy, F1 score, sensitivity, and specificity were calculated to evaluate the model's performance. The Shapley Additive Explanations method was used to visualize and interpret the model output.
A total of 16,416 women were recruited, with 8,314 and 8,102 in the POP and non-POP groups respectively. Eighty-seven variables were recorded. Among all candidate models, the RF model with 13 variables showed the best performance, with an AUC of 0.806 (95% CI 0.793-0.817), accuracy of 0.723, F1 of 0.731, sensitivity of 0.742, and specificity of 0.703. Excluding the physical examination variables, the RF model with 11 variables showed an AUC, accuracy, F1 score, sensitivity, and specificity of 0.716, 0.652, 0.688, 0.757, and 0.545 respectively.
We constructed a clinically applicable risk warning system that will help clinicians to identify women at a high risk of POP.
我们旨在基于一项回顾性实践，开发并验证一种临床适用的风险评估模型，以识别盆腔器官脱垂（POP）高危女性。
本研究纳入了2019年1月至2021年12月期间患有和未患有POP的患者。收集临床数据并应用机器学习模型，如多层感知器、逻辑回归、随机森林（RF）、轻量级梯度提升机和极限梯度提升。构建了两个数据集，一个包含所有变量，另一个排除体格检查变量。开发了两个版本的机器学习模型。一个供专业医生使用，另一个供社区卫生服务提供者使用。计算曲线下面积（AUC）及其置信区间（CI）、准确率、F1分数、敏感性和特异性，以评估模型性能。使用Shapley值法对模型输出进行可视化和解释。
共招募了16416名女性，POP组和非POP组分别有8314名和8102名。记录了87个变量。在所有候选模型中，包含13个变量的RF模型表现最佳，AUC为0.806（95%CI 0.793-0.817），准确率为0.723，F1为0.731，敏感性为0.742，特异性为0.703。排除体格检查变量后，包含11个变量的RF模型的AUC、准确率、F1分数、敏感性和特异性分别为0.716、0.652、0.688、0.757和0.545。
我们构建了一个临床适用的风险预警系统，这将有助于临床医生识别POP高危女性。
