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Background <p>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.</p> Methods <p>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.</p> Results <p>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 [p<sub>adj</sub>] &lt;0.001; OR = 0.39, 95% CI [0.34–0.44], p<sub>adj</sub> &lt;0.001) and the test dataset (OR = 0.47, 95% CI [0.28–0.87, p<sub>adj</sub> = 0.024; OR = 0.41, 95% CI [0.28–0.61], p<sub>adj</sub> &lt;0.001).</p> Conclusions <p>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.</p>

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Machine learning survival model for personalised prevention of catheter-related thrombosis in tumour patients

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年3月30日

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  • Hewei Ge,
  • Qiao Liu,
  • Junying Xie,
  • Jianan Pang,
  • Bin Li,
  • Jie Xue,
  • Lina Xu,
  • Nana Yang,
  • Haifeng Cai,
  • Jian Wang,
  • Yalong Qi,
  • Yuhan Wei,
  • Hongnan Mo,
  • Sidan Li,
  • Lili Zhang,
  • Ziming Liu,
  • Hongyi Wang,
  • Zehao Li,
  • Xinqiao Chen,
  • Xiaoxue Gao,
  • Fangqi Li,
  • Weiwei Xing,
  • Xiaoying Sun,
  • Yufeng Li,
  • Haili Qian,
  • Jiuwei Cui,
  • Fei Ma

摘要

Background

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.

Methods

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.

Results

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).

Conclusions

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.