Predicting the success of vaginal surgery: a quantitative risk assessment model for future investigation

Lei Yuan, Hao Zhang, Haosha Tang, Mo Chen, Xishi Liu, Congjian Xu, Liangqing Yao
In: European Journal of Obstetrics & Gynecology and Reproductive Biology · 2013 · vol. 171(2) , pp. 343–347 · doi:10.1016/j.ejogrb.2013.09.024 · PMID:24139132 · W2119291895
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This paper describes the development of a quantitative risk assessment model designed to predict the success of vaginal surgery, intended for future investigation.

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Abstract

ObjectiveTo introduce a model incorporating expected risks for a vaginal procedure based on objective measurements of a patient's characteristics and propose it as a potential tool to assist in the selection of candidates for vaginal surgery.Study designA quantitative model consisting of 13 clinical variables identified as risk factors in a prospective vaginal procedure was developed. Medical records of 315 women undergoing a set of routine gynecological surgeries via the vaginal, laparoscopic, and abdominal routes were obtained during January 2010 and November 2011. These surgeries included hysterectomy, myomectomy, bilateral or unilateral salpingo-oophorectomy and adnexal cystectomy. After that, each patient was scored according to the model. Sensitivity and specificity of the model were analyzed in one data set (cohort I) by receiver operating characteristic (ROC) curve and independently validated in a second data set (cohort II).Results175 patients were included in cohort I while the other 140 patients formed cohort II. The intra- and post-operative complication rates were 0.6% and 0%, respectively. A vaginal procedure was predicted with good accuracy (AUC=0.852). The sensitivity was 86.0% and specificity was 72.0% at an optimal cut-off point of score=3. The predication accuracy of this model was further validated in cohort II and reached as high as 85.7%. Furthermore, the score was significantly associated with the volume of estimated blood loss and the duration of operation time (P<0.05).ConclusionOur quantitative risk assessment model predicts safe vaginal surgery with good accuracy. Predictive tools based on such a model could help surgeons to optimize patient selection and thus contribute to reducing costs while enhancing patients' satisfaction. We invite other researchers to modify and validate the model in other populations.

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