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.

Fig. 1

ROC curves of two new models and Caprini scores in the training cohort (A) and in the testing cohort (B). Model A, univariate and multivariate logistic regression model; Model B, stepwise logistic regression

Fig. 2

Calibration curve of model A and B. X-axis: risk prediction of VTE in patients. Y-axis: actual diagnosed VTE. The more solid bias corrected line was closed to ideal line, the better prediction capacity. Model A, univariate and multivariate logistic regression model; Model B, stepwise logistic regression

Fig. 3

The nomogram obtained from model A (A) and model B (B). Model A, univariate and multivariate logistic regression model; Model B, stepwise logistic regression

Fig. 4

Decision Curve Analysis curve of model A, model B, and CRA model in training cohort (A) and testing cohort (B). Model A, univariate and multivariate logistic regression model; Model B, stepwise logistic regression

Fig. 5

Clinical impact curve of CRA model (A), model A (B), and model B (C). Model A, univariate and multivariate logistic regression model; Model B, stepwise logistic regression