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Development and validation of a dynamic prediction model for single-dose methotrexate treatment success in tubal ectopic pregnancy: a multicentre cohort study in Chinese hospitals
Development and validation of a dynamic prediction model for single-dose methotrexate treatment success in tubal ectopic pregnancy: a multicentre cohort study in Chinese hospitals
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Development and validation of a dynamic prediction model for single-dose methotrexate treatment success in tubal ectopic pregnancy: a multicentre cohort study in Chinese hospitals
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Development and validation of a dynamic prediction model for single-dose methotrexate treatment success in tubal ectopic pregnancy: a multicentre cohort study in Chinese hospitals
Development and validation of a dynamic prediction model for single-dose methotrexate treatment success in tubal ectopic pregnancy: a multicentre cohort study in Chinese hospitals

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Development and validation of a dynamic prediction model for single-dose methotrexate treatment success in tubal ectopic pregnancy: a multicentre cohort study in Chinese hospitals
Development and validation of a dynamic prediction model for single-dose methotrexate treatment success in tubal ectopic pregnancy: a multicentre cohort study in Chinese hospitals
Journal Article

Development and validation of a dynamic prediction model for single-dose methotrexate treatment success in tubal ectopic pregnancy: a multicentre cohort study in Chinese hospitals

2025
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Overview
IntroductionTubal ectopic pregnancy (TEP) frequently presents as a gynaecological emergency, often necessitating prompt medical intervention. Although methotrexate therapy offers the advantage of being non-invasive, it still poses the risk of tubal rupture and consequent haemorrhage, which may require emergency surgery. The major challenges encountered in practice are the lack of reliable methods to anticipate the likelihood of failure of single-dose methotrexate therapy and identify associated risk factors and thereby preclude unnecessary medical procedures. Effective counselling, therefore, remains a pivotal step in clinical management. This study is aimed at developing a predictive model to assess response to single-dose methotrexate therapy in cases of TEP by employing a dynamic prediction model approach based on the clinical data of a large cohort.Methods and analysisThis multicentre study will encompass 100 and 900 participants for the prospective and retrospective cohorts with TEP, respectively. Between 2010 and 2024, eligible patients who are diagnosed with TEP and opt for single-dose methotrexate therapy will be recruited as part of a retrospective cohort, from three participating hospitals in Zhejiang province, China. The comprehensive medical and obstetric histories of the patients will be collected, along with their clinical data, including the results of serological tests following the initial consultation. The control group will comprise patients who achieve uneventful, complete resolution of TEP, as evidenced by serum human chorionic gonadotropin level of <5 IU/L, without necessitating further treatment. The case group will include individuals requiring additional medical (further methotrexate) or surgical interventions. The dynamic prediction model developed in this manner will be employed on the longitudinal data. Additionally, data will be collected from two other hospitals between 2024 and 2026, as an external prospective cohort to validate the accuracy of the prediction model.Ethics and disseminationEthical approval for data collection has been obtained from the Ethics Committee of the First Affiliated Hospital of Zhejiang Chinese Medical University (No. 2024-KLS-346–02), Hangzhou First People’s Hospital (No. ZN-20240627-0219-01), Anji Hospital of Chinese Medicine (No. 2025年第5号), Qingyuan Hospital of Chinese Medicine (No. (2024)伦审第(9)号) and Beilun Hospital of Chinese Medicine (No. 2024年伦审第12号). Additionally, written informed consent will be obtained from all participants and they will retain the right to withdraw from the study at any stage. The findings of this study will be disseminated through publication in a peer-reviewed journal.Trial registration numberChiCTR2400085829.