Prediction of the Need to Morcellate in Laparoscopic Hysterectomy

Laparoscopic hysterectomy is considered the gold standard approach for benign gynecologic diseases even in women with large uterine masses. While performing a laparoscopic hysterectomy specimen extraction is an issue of major concern, especially for larger uteri. Morcellation is one of the methods used to extract uterine corpus after hysterectomy which has its pros and cons.Objective: To determine preoperative factors which could predict the need for uterine morcellation in laparoscopic hysterectomies, and also find the best cut-off values leading to optimum sensitivity and specificity in Iranian population.Design: Prospective CohortPatients: 162 women were recruited in this study from January 2018 till March 2019 who were planned to undergo total laparoscopic hysterectomy (TLH), with or without salpingo-oophorectomy in Arash women’s hospital, TUMS, Tehran, Iran.Measurements: Preoperative measurements including uterine ultrasonographic dimensions, uterine size in bimanual examination and myoma size in myomatous uteri were recorded. Receiver operator curves (ROC) were used to establish cut-offs that maximized sensitivity and specificity for each factor. Univariate Poisson regression analysis was used to calculate relative risks of each cut-off. Results: ROC curves demonstrated maximized sensitivities and specificities with a cross-sectional area of 36.5 cm2, largest leiomyoma dimension of 5.5 cm, bimanual exam of 13 weeks. Univariate Poisson regression 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 largest leiomyoma diameter (RR: 1.79; 95% CI:, 1.27–2.51).Conclusion: Uterine cross-sectional area, largest leiomyoma dimension, and uterine size on bimanual exam can all be used to predict the need for morcellation at the time of TLH.

کلیدواژه ها:

laparoscopic hysterectomy ، tissue extraction ، morcellation ، predictive factors.