Central venous catheters for drug delivery introduce catheter-related thrombosis (CRT) and influence the survival of cancer patients. The key unmet needs to personalise CRT prevention include identifying high-risk patients and optimising extubation time. In this study, we aimed to develop a survival model to facilitate personalised CRT prevention strategies. We prospectively collected tumour patient catheterization data across 4 centres. The SM-CRT survival model, which provides both continuous risk ranking (crank) predictions and the survival distribution (distr) predictions was constructed. Here we include a total of 30,947 patients. The SM-CRT model exhibits robust performance in identifying high-risk patients, with c-indexes of 0.714 in the prospective test dataset and 0.678 and 0.779 in 2 external test datasets based on crank predictions. Femorally inserted central catheter (FICC), peripherally inserted central catheter (PICC), tumours in the thoracic cavity, and alkylating agents are identified as high-risk factors. Patients are subsequently divided into high-risk, low-risk, and long-term period groups on the basis of their distr predictions. The predicted low-risk and long-term groups present significantly fewer CRT events per day than the high-risk group in both the training dataset (odds ratio OR = 0.54, 95% CI 0.38–0.91, adjusted p-value padj <0.001; OR = 0.39, 95% CI 0.34–0.44, padj <0.001) and the test dataset (OR = 0.47, 95% CI 0.28–0.87, padj = 0.024; OR = 0.41, 95% CI [0.28–0.61, padj <0.001). The high c-indexes based on crank predictions demonstrated the ability of the SM-CRT model to identify high-risk patients for thromboprophylaxis. Additionally, the SM-CRT model can guide extubation time by identifying high-risk periods through distr predictions. Central venous catheters are commonly used to deliver cancer treatment, but they can cause catheter-related thrombosis (CRT), a serious blood clot complication. Doctors currently lack reliable tools to identify which patients are most at risk and to determine the safest time to remove the catheter. In this study, we collected data from 30,947 cancer patients across four medical centres and developed a prediction model called SM-CRT. This model estimates both a patient’s overall risk of developing CRT and when that risk is highest. We found that the model can accurately identify high-risk patients and distinguish time periods when the risk of thrombosis is elevated. This model may help doctors personalise prevention strategies and make safer decisions about catheter removal, ultimately reducing complications for cancer patients. Ge, Liu et al. develop and validate a survival model (SM-CRT) using multicentre data from 30,947 cancer patients to predict catheter-related thrombosis (CRT) risk. The model accurately identifies high-risk patients and high-risk time periods, supporting personalised thromboprophylaxis and optimised catheter removal timing.
Ge et al. (Mon,) studied this question.