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Development and Validation of a Machine Learning-Based Clinical Model for Predicting Rupture in Ectopic Pregnancy: A
Xiongying Zhao1, Tianchen Wu2, Simin Zeng1
1Department of Ultrasound Diagnosis, Panyu Maternal and Child Care Service Centre of Guangzhou, Guangzhou, Guangdong, 511495, People's Republic of China.
Journal of Multidisciplinary Healthcare
|September 19, 2025
Summary
This study developed a predictive model and web-based nomogram to identify women at high risk of rupture-associated bleeding in ectopic pregnancy (EP). Early intervention is supported, aiming to reduce complications.
Area of Science:
- Obstetrics and Gynecology
- Medical Informatics
- Predictive Analytics
Background:
- Ectopic pregnancy (EP) poses significant risks, including rupture-associated bleeding, necessitating improved early detection methods.
- Current diagnostic approaches for EP complications may lack precision, leading to delayed interventions.
Purpose of the Study:
- To develop and validate a predictive model for rupture-associated bleeding in ectopic pregnancy.
- To create a user-friendly, web-based nomogram for clinical application in risk stratification.
Main Methods:
- Retrospective analysis of clinical data from 543 women with EP.
- Utilized Extreme Gradient Boosting (XGBoost) and SHapley Additive exPlanations (SHAP) for variable selection.
- Model performance evaluated using ROC curves, calibration, DCA, and CIC; a web-based nomogram was developed.
Main Results:
- A predictive model incorporating seven key variables was successfully developed and validated.
- The model demonstrated high accuracy with AUCs of 0.941 (training) and 0.970 (validation).
- A dynamic, web-based nomogram was created for practical clinical implementation.
Conclusions:
- A clinically applicable and validated predictive model for EP rupture-associated bleeding has been established.
- The web-based nomogram facilitates early risk stratification and timely intervention.
- This tool has the potential to decrease the incidence of severe complications associated with ectopic pregnancy rupture.
