Predicting Catheter-Related Thrombosis in Colorectal Cancer Patients Using Clinical Features in 730 Patients
Abstract:
ObjectiveCatheter-related thrombosis (CRT) is a common complication in colorectal cancer patients undergoing chemotherapy, significantly impacting patient outcomes. However, effective predictive tools for identifying high-risk patients are currently lacking. This study aimed to develop and validate a predictive model, the TD score, to identify patients at high risk of CRT based on clinical parameters.MethodsThis single-institutional retrospective study included 730 colorectal cancer patients with intravenous catheters, divided into training (n = 624) and test sets (n = 106). The primary endpoint was CRT, diagnosed via imaging. Multivariable logistic regression analysis was used to identify independent predictors of CRT, and a predictive model (TD score) was developed based on T stage and duration of intravenous catheter use. The model's performance was evaluated using receiver operating characteristic (ROC) curve analysis.ResultsThe TD score demonstrated good diagnostic performance, with areas under the ROC curve of 0.732 in the training set and 0.749 in the test set. Survival analysis revealed that patients with lower T stages had longer durations of non-CRT status. The study identified T stage and duration of intravenous catheter use as independent predictors of CRT.ConclusionsThe TD score is a promising tool for identifying high-risk patients for CRT. It may improve patient risk stratification and guide targeted prophylactic interventions. Future validation studies involving larger and more diverse patient cohorts are needed to confirm the clinical utility of the TD score and evaluate its performance across different TNM stages.
More Related Videos
06:46Competing-Risk Nomogram for Predicting Cancer-Specific Survival in Multiple Primary Colorectal Cancer Patients after Surgery
Published on: September 27, 2024
07:13Comparison of Predictive Performance of Three Lymph Node Staging Systems in Colorectal Signet Ring Cell Carcinoma Based on Machine Learning Model
Published on: April 18, 2025
