Department of Gynecological Oncology, 56713Sun Yat-sen Memorial Hospital, Sun Yat-sen University, Guangzhou, China.
Key Laboratory of malignant tumor gene regulation and target therapy of Guangdong Higher Education Institutes, 56713Sun Yat-sen University, Guangzhou, China.
Clin Appl Thromb Hemost. 2022 Jan-Dec;28:10760296221095558. doi: 10.1177/10760296221095558.
To identify predictive factors and develop a nomogram to predict the probability of venous thromboembolism for epithelial ovarian cancer patients. Our study cohort was composed of 208 EOC patients who had received initial treatment in Sun Yat-sen Memorial Hospital from January 2016 to March 2020. Clinicopathological variables predictive of VTE were identified using univariate logistic analysis. A multivariate logistic regression model was used to select the predictive factors used for nomogram. The accuracy of nomogram was evaluated by the Concordance index (C-index), the area under the receiver-operator characteristic (ROC) curve, area under concentration-time curve (AUC) and the calibration curve. Advancing age (hazard ratio [HR], 1.042; 95% confidence interval [CI], 1.000-1.085; = .048), higher D-dimer level (HR, 1.144; 95%CI, 1.020-1.283; = .022), lower PR immunohistochemical positive rate (HR, 0.186; 95%CI, 0.034-1.065; = .059) and higher Ki67 immunohistochemical positive rate (HR, 4.502; 95%CI, 1.637-12.380; = .004) were found to be independent risk factors for VTE, and were used to construct the nomogram. The C-index for VTE prediction of the nomogram was 0.75. We constructed and validated a nomogram able to quantify the risk of VTE for EOC patients, which can be applied in recognizing EOC patients with high risk of VTE.
为了确定上皮性卵巢癌患者静脉血栓栓塞的预测因素并建立预测模型。我们的研究队列由 208 名在中山大学孙逸仙纪念医院接受初始治疗的上皮性卵巢癌患者组成，入组时间为 2016 年 1 月至 2020 年 3 月。采用单因素 logistic 分析确定预测 VTE 的临床病理变量。使用多因素 logistic 回归模型选择预测模型的预测因素。通过一致性指数（C 指数）、接受者操作特征曲线下面积（ROC 曲线下面积）、浓度-时间曲线下面积（AUC）和校准曲线评估预测模型的准确性。高龄（风险比 [HR]，1.042；95%置信区间 [CI]，1.000-1.085； = .048）、较高的 D-二聚体水平（HR，1.144；95%CI，1.020-1.283； = .022）、较低的 PR 免疫组化阳性率（HR，0.186；95%CI，0.034-1.065； = .059）和较高的 Ki67 免疫组化阳性率（HR，4.502；95%CI，1.637-12.380； = .004）是 VTE 的独立危险因素，并用于构建预测模型。该预测模型预测 VTE 的 C 指数为 0.75。我们构建并验证了一个能够量化上皮性卵巢癌患者 VTE 风险的预测模型，该模型可用于识别 VTE 风险较高的上皮性卵巢癌患者。
