Laparoscopic myomectomy: predicting the risk of conversion to an open procedure

剖腹手术 医学 置信区间 子宫肌瘤切除术 肌瘤 逻辑回归 外科 腹腔镜检查 优势比 内科学 子宫
作者
Jean‐Bernard Dubuisson
出处
期刊:Human Reproduction [Oxford University Press]
卷期号:16 (8): 1726-1731 被引量:173
标识
DOI:10.1093/humrep/16.8.1726
摘要

BACKGROUND: Laparoscopic myomectomy (LM) has some advantages over laparotomy; however, it is reputed to be technically difficult, and the risk of conversion to laparotomy might be an obstacle in using this procedure. The aim of this study was to identify the pre-operative factors affecting the risk of conversion to an open procedure (either laparoscopic assisted myomectomy or laparotomy), and to develop a simple prediction model based on available pre-operative data with the use of multiple logistic regression. METHODS: A total of 426 women presenting with a subserous or intramural myoma measuring 20 mm or more underwent LM between March 1989 and October 1999. Of these patients, 378 had successful LM. Forty eight patients [11.3%, 95% confidence interval (CI) 8.3–14.3] had a conversion to an open procedure. A total of 265 women had adequate pre-operative ultrasonography (US) and were used for the analysis. RESULTS: The best prediction model included four pre-operative factors that were found to be independently related to the risk of conversion: size │50 mm at US (adjusted OR = 10.3; 95% CI = 2.8–37.9), intramural type (adjusted OR = 4.3; 95% CI = 1.3–14.5), anterior location (adjusted OR = 3.4; 95% CI = 1.3–9.0) and pre-operative use of gonadotrophin-releasing hormone (GnRH) agonists (adjusted OR = 5.4; 95% CI = 2.0–14.2). The regression coefficients were then scaled and rounded to integers to provide an estimate of the risk for conversion. For a given patient with selected characteristics the predicted risk varied from 0–73%. CONCLUSIONS: This prediction model provides a useful tool that enables multiple criteria to be taken into account simultaneously to help select cases for LM. GnRH agonists should been used only in selected cases. US evaluation is essential before performing LM.
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