Development and validation of a risk prediction model for PICC-related venous thrombosis in patients with cancer: a prospective cohort study
Abstract To develop and validate a risk prediction model for predicting the risk of Peripherally Inserted Central Catheter-Related venous thrombosis (PICC-RVT) in cancer patients with PICCs. A prospective cohort study of 281 cancer patients with PICCs was conducted from April 2023 to January 2024. D...
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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Nature Portfolio
2025-02-01
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| Series: | Scientific Reports |
| Subjects: | |
| Online Access: | https://doi.org/10.1038/s41598-025-89260-1 |
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| Summary: | Abstract To develop and validate a risk prediction model for predicting the risk of Peripherally Inserted Central Catheter-Related venous thrombosis (PICC-RVT) in cancer patients with PICCs. A prospective cohort study of 281 cancer patients with PICCs was conducted from April 2023 to January 2024. Data on patient-, laboratory- and catheter-related risk factors were collected on the day of catheterization. Patients were investigated for PICC-RVT by Doppler sonography in the presence of PICC-RVT signs and symptoms. Univariate and multivariate regression analyses were used to identify independently associated risk factors of PICC-RVT and develop a risk prediction model. 275 patients were finally included for data analysis, and 18 (6.5%) developed PICC-RVT. Four risk factors were identified as key predictors of PICC-RVT, including “diabetes requiring insulin (OR: 8.016; 95% CI 1.157–55.536), major surgery (within 1 month and operation time > 45 minutes) (OR: 0.023; 95% CI 1.296–30.77), reduced limb activities of the PICC arm (OR: 6.687; 95% CI 2.024–22.09)” and “catheter material (OR: 3.319; 95% CI 0.940–11.723)”. The nomogram model was developed and internally validated with an area under the receiver operating characteristics curve (AUC) of 0.796 (95% CI 0.707–0.885). The Hosmer–Lemeshow goodness-of-ft was 1.685 (p = 0.194). The nomogram prediction model had good predictive performance. This model could help identify patients at the highest risk for PICC-RVT to guide effective prophylaxis. Further external validation studies of this nomogram model on a large sample are required. |
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| ISSN: | 2045-2322 |