Department of Urology, The Second Xiangya Hospital, Central South University, Changsha, 410011, Hunan, China.
Ann Surg Oncol. 2022 Aug;29(8):5297-5306. doi: 10.1245/s10434-022-11574-5. Epub 2022 Mar 22.
Venous thromboembolism (VTE) is the second leading cause for death of radical prostatectomy. We aimed to establish new nomogram to predict the VTE risk after robot-assisted radical prostatectomy (RARP).
Patients receiving RARP in our center from November 2015 to June 2021, were enrolled in study. They were randomly divided into training and testing cohorts by 8:2. Univariate and multivariate logistic regression (model A) and stepwise logistic regression (model B) were used to fit two models. The net reclassification improvement (NRI), integrated discrimination improvement (IDI), and receiver operating characteristic (ROC) curve were used to compare predictive abilities of two new models with widely used Caprini risk assessment (CRA) model. Then, two nomograms were constructed and received internal validation.
Totally, 351 patients were included. The area under ROC of model A and model B were 0.967 (95% confidence interval: 0.945-0.990) and 0.978 (95% confidence interval: 0.960-0.996), which also were assayed in the testing cohorts. Both the prediction and classification abilities of the two new models were superior to CRA model (NRI > 0, IDI > 0, p < 0.05). The C-index of Model A and Model B were 0.968 and 0.978, respectively. For clinical usefulness, the two new models offered a net benefit with threshold probability between 0.08 and 1 in decision curve analysis, suggesting the two new models predict VTE events more accurately.
Both two new models have good prediction accuracy and are superior to CRA model. Model A has an advantage of less variable. This easy-to-use model enables rapid clinical decision-making and early intervention in high-risk groups, which ultimately benefit patients.
静脉血栓栓塞症（VTE）是前列腺根治术后死亡的第二大主要原因。我们旨在建立新的列线图来预测机器人辅助前列腺根治术后（RARP）的 VTE 风险。
本研究纳入 2015 年 11 月至 2021 年 6 月期间在我院接受 RARP 的患者。他们通过 8:2 的比例随机分为训练队列和测试队列。使用单变量和多变量逻辑回归（模型 A）和逐步逻辑回归（模型 B）拟合两个模型。净重新分类改善（NRI）、综合判别改善（IDI）和受试者工作特征（ROC）曲线用于比较两个新模型与广泛使用的 Caprini 风险评估（CRA）模型的预测能力。然后，构建并进行了内部验证两个列线图。
共纳入 351 例患者。模型 A 和模型 B 的 ROC 曲线下面积分别为 0.967（95%置信区间：0.945-0.990）和 0.978（95%置信区间：0.960-0.996），并在测试队列中进行了验证。两个新模型的预测和分类能力均优于 CRA 模型（NRI>0，IDI>0，p<0.05）。模型 A 和模型 B 的 C 指数分别为 0.968 和 0.978。对于临床实用性，两个新模型在决策曲线分析中，在阈值概率为 0.08 至 1 之间具有净收益，这表明两个新模型能更准确地预测 VTE 事件。
两个新模型均具有良好的预测准确性，优于 CRA 模型。模型 A 具有变量较少的优势。这种易于使用的模型可以快速进行临床决策，并对高危人群进行早期干预，最终使患者受益。
