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Published on: September 16, 2022
A risk prediction model for medical treatment failure in tubal pregnancy
Jae Hoon Lee1, Sunghoon Kim1, Inha Lee1
1Department of Obstetrics and Gynecology, Severance Hospital, Yonsei University College of Medicine, Seoul, Republic of Korea; Institute of Women's Life Medical Science, Yonsei University College of Medicine, Seoul, Republic of Korea.
Objective:
Methotrexate is an alternative treatment for tubal pregnancy. However, despite initial treatment, ∼15% of women eventually require surgery. This study aimed to identify the risk factors for medical treatment failure in tubal pregnancy and apply them to a risk prediction model.
Study Design:
This single-center retrospective cohort study included 123 participants initially treated medically for tubal pregnancy between January 2006 and December 2015. Logistic regression analysis was used to construct a risk prediction model (visually presented as a nomogram) for medical treatment failure. Model performance was assessed using discrimination and calibration. The medical treatment failure rate was 36.6%. The prediction model integrated the presence of a gestational sac, ectopic mass size, and follow-up β-human chorionic gonadotropin levels above cut-off values on days 4 and 7. The model used the following cut-off values: increased β-human chorionic gonadotropin levels by 1028.6 mIU/mL, 1.0457-fold higher than baseline level on day 4; and increased β-human chorionic gonadotropin levels by 1233 mIU/mL, 1.3025-fold higher than baseline level on day 7.
Results:
The corresponding areas under the receiver-operating characteristic curves were 0.8135 (95% confidence interval, 0.733-0.893) for the day 4 model and 0.8600 for the day 7 model (95% confidence interval, 0.788-0.932). Comparison of the day 4 and 7 models revealed no significant difference in their predictive abilities (P = 0.4318).
Conclusions:
This model identified a substantial proportion of the participants who experienced medical treatment failure for tubal pregnancy. It was visualized as a nomogram, facilitating clinical application.
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