Department of Obstetrics and Gynaecology, Norfolk and Norwich University Hospital NHS Foundation Trust, Norwich, United Kingdom.
Int J Gynecol Cancer. 2011 Apr;21(3):500-6. doi: 10.1097/IGC.0b013e31820c4cd6.
The aim of this study was to develop a multivariable model to predict the risk of endometrial carcinoma in postmenopausal women with vaginal bleeding using individuals' clinical characteristics.
This prospective study of consecutive postmenopausal women presenting with vaginal bleeding was conducted at a gynecological oncology center in the United Kingdom for a 46-month period. All women underwent transvaginal ultrasound scanning as the initial investigation tool to evaluate the endometrium. Women found to have an endometrial thickness 5 mm or more had endometrial sampling performed.
Of a total of 3548 women presenting with vaginal bleeding during the study period, 201 (6%) women had a diagnosis of endometrial carcinoma. An investigator-led best model selection approach used to select the best predictors of cancer in the multiple logistic regression model showed that patient's age (odds ratio [OR], 1.06), body mass index (OR, 1.07), recurrent episodes of bleeding (OR, 3.64), and a history of diabetes (OR, 1.48) increased the risk of endometrial malignancy when corrected for other characteristics. The mentioned clinical variables satisfied the criteria for inclusion in our predictive model called FAD 31 (F for the frequency of bleeding episodes, A for the age of the patient, D for diabetes, and the number 31 represents the BMI cut-off value). The total score for the model varies from 0 to 8. The area under the receiver operating characteristics curve for the developed model was 0.73 (95% confidence interval, 0.70-0.77).
We have developed a simple model based on patients' clinical characteristics in estimating the risk of endometrial cancer for postmenopausal women presenting with vaginal bleeding. The model shows reasonable discriminatory ability for women with cancer and without, with an area under the receiver operating characteristics curve of 0.73. This will allow clinicians to individualize the diagnostic pathway for women with postmenopausal vaginal bleeding.
本研究旨在开发一种多变量模型,通过个体的临床特征预测绝经后阴道出血妇女患子宫内膜癌的风险。
本前瞻性研究连续纳入在英国妇科肿瘤中心就诊的绝经后阴道出血患者,研究时间为 46 个月。所有女性均接受经阴道超声检查作为评估子宫内膜的初始检查工具。发现子宫内膜厚度≥5mm 的女性进行子宫内膜取样。
在研究期间共 3548 例出现阴道出血的女性中,201 例(6%)女性被诊断为子宫内膜癌。采用研究者主导的最佳模型选择方法,在多元逻辑回归模型中选择癌症的最佳预测因子,结果显示患者年龄(比值比[OR],1.06)、体重指数(OR,1.07)、反复出血(OR,3.64)和糖尿病史(OR,1.48)在其他特征校正后增加了子宫内膜恶性肿瘤的风险。上述临床变量符合纳入我们称为 FAD31 的预测模型的标准(F 代表出血发作频率,A 代表患者年龄,D 代表糖尿病,31 代表 BMI 截止值)。该模型的总分范围为 0 至 8 分。开发模型的受试者工作特征曲线下面积为 0.73(95%置信区间,0.70-0.77)。
我们基于绝经后阴道出血患者的临床特征开发了一种简单的模型来估计子宫内膜癌的风险。该模型对患有和不患有癌症的女性具有合理的鉴别能力,受试者工作特征曲线下面积为 0.73。这将使临床医生能够针对绝经后阴道出血的女性个体化诊断途径。