ArticleCommunications medicine2026
Machine learning survival model for personalised prevention of catheter-related thrombosis in tumour patients.
Article in Communications medicine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundCentral 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.
methodsWe 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.
resultsHere 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 [p
conclusionsThe 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.
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