Abstract
Objective
To 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 design
A 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).
Results
175 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).
Conclusion
Our 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.
Keywords
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Article info
Publication history
Published online: October 03, 2013
Accepted:
September 17,
2013
Received in revised form:
August 17,
2013
Received:
May 8,
2013
Identification
Copyright
© 2013 Published by Elsevier Inc.
