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eISSN: 1643-3750

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A Model to Predict Treatment Failure of Single‑Dose Methotrexate in Patients with Tubal Pregnancy

Si Chen, Fangfang Zhu, Yingxuan Zhang, Jing Li, Jie Gao, Gaopi Deng

(First Clinical Medical College, Guangzhou University of Chinese Medicine, Guangzhou, Guangdong, China (mainland))

Med Sci Monit 2020; 26:e920079

DOI: 10.12659/MSM.920079


BACKGROUND: In China, approximately 15% of tubal pregnancy patients treated with MTX eventually required surgery because the ectopic mass was ruptured; therefore, it is essential to develop a model to predict the risk of failure with methotrexate treatment in tubal pregnancy.
MATERIAL AND METHODS: In this research, 168 patients met the eligibility criteria, and 29 candidate risk factors for treatment failure were collected. Multivariable logistic regression analysis was used to analyze the factors, and a full model was developed. We used a multiple fractional polynomial model and a stepwise model to increase the reliability. Bootstrap resampling for 500 times was used to internally test the prediction model. The integral performance of the model depends on the evaluation of the nomogram, the discriminative performance by receiver operating characteristic (ROC) curve analysis, and calibration.
RESULTS: The model showed excellent discrimination and calibration. The area under the ROC curve for the prediction model, mfp model, and stepwise model were 0.879 (95% CI: 0.812-0.942), 0.872 (95% CI: 0.805-0.931), and 0.880 (95% CI: 0.817-0.949), respectively. At a cutoff value of ≥0.40, sensitivity was 60%, specificity was 91%, positive predictive value (PPV) was 81%, and negative predictive value (NPV) was 77%. The model provides a net benefit when clinical decision thresholds are between 0% and 40% of predicted risk.
CONCLUSIONS: This model indicated good accuracy in predicting methotrexate treatment failure for tubal pregnancy patients.

This paper has been published under Creative Common Attribution-NonCommercial-NoDerivatives 4.0 International (CC BY-NC-ND 4.0) allowing to download articles and share them with others as long as they credit the authors and the publisher, but without permission to change them in any way or use them commercially.
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