Prediction of the need to morcellate in laparoscopic hysterectomy

Background and Aim : Laparoscopic hysterectomy is considered the gold standard approach for benigngynecologic diseases even in women with large uterine masses. While performing a laparoscopichysterectomy specimen extraction is an issue of major concern, especially for larger uteri. Morcellation isone of the methods used to extract uterine corpus after hysterectomy which has its pros and cons. Aim: Todetermine preoperative factors which could predict the need for uterine morcellation in laparoscopichysterectomies, and also find the best cut-off values leading to optimum sensitivity and specificity in Iranianpopulation.Methods : Design: Prospective Cohort Patients: 162 women were recruited in this study from January 2018till March 2019 who were planned to undergo total laparoscopic hysterectomy (TLH), with or withoutsalpingo-oophorectomy in Arash women’s hospital, TUMS, Tehran, Iran. Measurements: Preoperativemeasurements including uterine ultrasonographic dimensions, uterine size in bimanual examination andmyoma size in myomatous uteri were recorded. Receiver operator curves (ROC) were used to establish cutoffsthat maximized sensitivity and specificity for each factor. Univariate Poisson regression analysis wasused to calculate relative risks of each cut-off.Results : Results: ROC curves demonstrated maximized sensitivities and specificities with a cross-sectionalarea of 36.5 cm2, largest leiomyoma dimension of 5.5 cm, bimanual exam of 13 weeks. Univariate Poissonregression analysis revealed that the strongest predictors of morcellation were bimanual exam ( RR: 1.49;95% Confidence Interval (CI): 1.21-1.83), cross-sectional area (RR:1.43; 95% CI: 1.21–1.69), and largestleiomyoma diameter (RR: 1.79; 95% CI:, 1.27–2.51).Conclusion : Conclusion: Uterine cross-sectional area, largest leiomyoma dimension, and uterine size onbimanual exam can all be used to predict the need for morcellation at the time of TLH.

Keywords:

laparoscopic hysterectomy , tissue extraction , morcellation , predictive factors