Abstract

Misoprostol treatment for early pregnancy loss has varied success demonstrated in previous studies. Incorporating predictors in a single clinical scoring system would be highly beneficial in clinical practice. To develop and evaluate the accuracy of a scoring system to predict misoprostol treatment outcomes for managing early pregnancy loss. Retrospective cohort and validation study. Patients discharged from the gynecologic emergency department from 2013 to 2016, diagnosed with early pregnancy loss, who were treated with 800 mcg misoprostol, administrated vaginally were included. All were sonographically reevaluated within 48-72 hours. Patients in whom the gestational sac was not expelled or with endometrial lining >30 mm were offered a repeat dose and returned for reevaluation after seven days. A successful response was defined as complete expulsion. Clinical data were reviewed to identify predictors for successful responses. The scoring system was then retrospectively evaluated on a second cohort to evaluate its accuracy. Multivariate logistic regression was performed to identify factors most predictive of treatment response. The development cohort included 126 patients. Six factors were found to be most predictive of misoprostol treatment effectiveness: nulliparity, prior complete spontaneous abortion, gestational age, vaginal bleeding, abdominal pain, and mean sac diameter, yielding a score of 0-8 (the MISOPRED score), where 8 represents the highest-likelihood of success. The score was validated retrospectively with 119 participants. Successful response in the group with the lowest likelihood score (score 0-3) was 9%, compared with 82% in the highest likelihood score group (score 7-8). Using the MISOPRED score, approximately 15% of patients previously planned to receive misoprostol treatment can be referred for surgical management. MISOPRED score can be utilized as an adjunct tool for clinical decision-making in cases of Early pregnancy loss. To our knowledge, this is the first scoring system suggested to predict the success rate in these cases.

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