Construction and Verification of Risk Predicting Models to Evaluate the Possibility of Venous Thromboembolism After Robot-Assisted Radical Prostatectomy

Abstract

Background 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). Methods 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. Results 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. Conclusions 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.

Predictive accuracy of boosted regression model in estimating risk of venous thromboembolism following laparoscopic radical surgery in pharmacological prophylaxis-naïve men with prostate cancer

“…Authors have, however, included one point for Gleason scoring above 6 given its evidence in predicting VTE in the literature. (11,12). The average score for our VTE patients following the above risk-strati ed scoring system was 10.6 while the average score for non-VTE patients was 7.5.…”

Machine learning predicts cancer-associated venous thromboembolism using clinically available variables in gastric cancer patients

“…So these tools are not a good representation of the Chinese population [ 12 , 13 ]. Xu Cheng et al used multivariate logistic regression and stepwise logistic regression to establish models to predict the occurrence of VTE after robot-assisted radical prostatectomy [ 14 ]. However, the sample size of this study was small, and only 351 samples were included.…”