Construction and Validation of a Clinical Prediction Model for Predicting Tubal Pregnancy Rupture Based on Nomogram.

Aim: To explore the risk factors for tubal rupture in tubal pregnancy, construct and validate a prediction model for tubal rupture. Methods: Clinical data from 517 patients with tubal pregnancy from January 2020 to December 2022 were collected. The patients were divided into two groups: the tubal rupture group and the unruptured group. The general clinical data of both groups were analyzed using univariate analysis and multivariate logistic regression analysis. Subsequently, a risk prediction model was constructed. Results: Univariate analysis revealed that amenorrhea duration, maximum diameter of the mass, pregnancy site, serum β-HCG levels, and maximum diameter of pelvic hematocele were identified as potential risk factors for tubal pregnancy rupture. Multivariate logistic regression analysis confirmed these variables, except for the maximum diameter of pelvic hematocele, as independent risk factors for tubal pregnancy rupture. A prediction model for tubal pregnancy rupture was established and validated. The area under the receiver operating characteristic curve was 0.861 for the training set and 0.887 for the validation set, indicating good discriminative ability of the model. The calibration curves of the training set and validation set showed a good fit between the actual values and the predicted values. Moreover, the decision curve analysis suggested that the model had good clinical applicability. To facilitate the use of the nomogram, a web server was developed at https://ep10.shinyapps.io/DynNomapp/. Conclusions: The prediction model for tubal pregnancy rupture, based on the four predictors: amenorrhea duration, pregnancy site, serum β-HCG levels, and maximum diameter of the mass, demonstrated good predictive efficacy.