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已发表论文
基于机器学习的异位妊娠破裂预测临床模型的开发与验证：一种基于网络的诺模图方法
Authors Zhao X, Wu T, Zeng S, Yuan X, Liang X, Yang H, Ye L
Received 24 April 2025
Accepted for publication 2 September 2025
Published 13 September 2025 Volume 2025:18 Pages 5781—5799
DOI https://doi.org/10.2147/JMDH.S536476
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Jagdish Khubchandani
Xiongying Zhao,1,* Tianchen Wu,2,* Simin Zeng,1 Xiaoyun Yuan,1 Xiaoying Liang,1 Hui Yang,3 Lihui Ye1
1Department of Ultrasound Diagnosis, Panyu Maternal and Child Care Service Centre of Guangzhou, Guangzhou, Guangdong, 511495, People’s Republic of China; 2Department of Neurology, Nanjing Hospital of Chinese Medicine, Nanjing, Jiangsu, 210001, People’s Republic of China; 3School of Nursing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210046, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Lihui Ye, Department of Ultrasound Diagnosis, Panyu Maternal and Child Care Service Centre of Guangzhou, No. 2 Qinghe East Road, Panyu District, Guangzhou, Guangdong, 511495, People’s Republic of China, Tel +862039152303, Email lihui_yel@126.com Hui Yang, School of Nursing, Nanjing University of Chinese Medicine, No. 138 of Xianlin Road, Qixia District, Nanjing, Jiangsu, 210023, People’s Republic of China, Tel +862585811993, Email yanghuiyhcc@163.com
Objective: The aim of this study is to develop a predictive model for rupture-associated bleeding in ectopic pregnancy (EP) and to construct a web-based nomogram to support early clinical intervention in women at elevated risk.
Methods: Clinical data were retrospectively collected from 543 women with EP at Hexian Memorial Affiliated Hospital of Southern Medical University, Guangzhou, China, between June 2019 and June 2022. Among these, 58 cases were confirmed intraoperatively to have experienced rupture with bleeding. The cohort was randomly divided into training (70%) and validation (30%) subsets. Key predictive variables were selected using the Extreme Gradient Boosting (XGBoost) algorithm, guided by SHapley Additive exPlanations (SHAP) values. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve, calibration analysis, decision curve analysis (DCA), and clinical impact curve (CIC). A web-based nomogram was subsequently developed for clinical implementation.
Results: Seven predictive variables were identified and used to construct the model. The ROC curve yielded an area under the curve (AUC) of 0.941 (95% CI: 0.882– 0.968) in the training subset and 0.970 (95% CI: 0.9405– 0.990) in the validation subset. Calibration curves demonstrated strong concordance between predicted probabilities and observed outcomes. DCA indicated a clinically meaningful predictive probability range between 1% and 94.82%. A dynamic, web-based nomogram was created to facilitate practical application.
Conclusion: A clinically applicable predictive model for rupture in EP was developed and validated, incorporating seven key variables. The web-based nomogram enables early risk stratification and intervention, potentially reducing the incidence of rupture-related complications.
Keywords: ectopic pregnancy, machine learning, nomogram, prediction model
1Department of Ultrasound Diagnosis, Panyu Maternal and Child Care Service Centre of Guangzhou, Guangzhou, Guangdong, 511495, People’s Republic of China; 2Department of Neurology, Nanjing Hospital of Chinese Medicine, Nanjing, Jiangsu, 210001, People’s Republic of China; 3School of Nursing, Nanjing University of Chinese Medicine, Nanjing, Jiangsu, 210046, People’s Republic of China
*These authors contributed equally to this work
Correspondence: Lihui Ye, Department of Ultrasound Diagnosis, Panyu Maternal and Child Care Service Centre of Guangzhou, No. 2 Qinghe East Road, Panyu District, Guangzhou, Guangdong, 511495, People’s Republic of China, Tel +862039152303, Email lihui_yel@126.com Hui Yang, School of Nursing, Nanjing University of Chinese Medicine, No. 138 of Xianlin Road, Qixia District, Nanjing, Jiangsu, 210023, People’s Republic of China, Tel +862585811993, Email yanghuiyhcc@163.com
Objective: The aim of this study is to develop a predictive model for rupture-associated bleeding in ectopic pregnancy (EP) and to construct a web-based nomogram to support early clinical intervention in women at elevated risk.
Methods: Clinical data were retrospectively collected from 543 women with EP at Hexian Memorial Affiliated Hospital of Southern Medical University, Guangzhou, China, between June 2019 and June 2022. Among these, 58 cases were confirmed intraoperatively to have experienced rupture with bleeding. The cohort was randomly divided into training (70%) and validation (30%) subsets. Key predictive variables were selected using the Extreme Gradient Boosting (XGBoost) algorithm, guided by SHapley Additive exPlanations (SHAP) values. Model performance was assessed using the area under the receiver operating characteristic (ROC) curve, calibration analysis, decision curve analysis (DCA), and clinical impact curve (CIC). A web-based nomogram was subsequently developed for clinical implementation.
Results: Seven predictive variables were identified and used to construct the model. The ROC curve yielded an area under the curve (AUC) of 0.941 (95% CI: 0.882– 0.968) in the training subset and 0.970 (95% CI: 0.9405– 0.990) in the validation subset. Calibration curves demonstrated strong concordance between predicted probabilities and observed outcomes. DCA indicated a clinically meaningful predictive probability range between 1% and 94.82%. A dynamic, web-based nomogram was created to facilitate practical application.
Conclusion: A clinically applicable predictive model for rupture in EP was developed and validated, incorporating seven key variables. The web-based nomogram enables early risk stratification and intervention, potentially reducing the incidence of rupture-related complications.
Keywords: ectopic pregnancy, machine learning, nomogram, prediction model