# Analysis of risk factors for PICC-related venous thrombosis in patients with hematological malignancies and establishment of a nomogram prediction model

## TL;DR

This study identifies risk factors for PICC-related venous thrombosis in cancer patients and builds a model to predict and manage the risk.

## Contribution

A novel nomogram prediction model for PICC-related venous thrombosis in hematologic malignancy patients is developed and validated.

## Key findings

- Seven independent risk factors for CRT were identified, including hemoglobin, platelet count, and D-dimer.
- The nomogram achieved high AUC values (0.965 in training, 0.977 in validation) and demonstrated strong predictive accuracy.
- The model supports individualized risk assessment and intervention strategies to improve chemotherapy safety.

## Abstract

To analyze the independent risk factors for PICC-related venous thrombosis in patients with hematologic malignancies, and to construct and validate a risk prediction model.

This retrospective study analyzed data from 264 hematologic malignancy patients who received PICC chemotherapy at the Affiliated Hospital of Qingdao University between January 2022 and December 2024. Patients were randomly divided into training and validation sets (7:3), and the incidence of CRT was calculated. In the training set, LASSO regression and multivariable logistic regression identified independent CRT risk factors, which were used to construct a predictive nomogram. The model’s discrimination, calibration, and clinical utility were evaluated using AUC, calibration curves, and DCA.

The prevalence of PICC-related venous thrombosis was 6.1%, with 16 out of 264 patients diagnosed with CRT. Multivariable logistic regression analysis identified seven independent risk factors for CRT: hemoglobin, platelet count, prothrombin time, D-dimer, globulin, punctured vein, and catheter insertion depth. The area under the receiver operating characteristic curve for the training and validation sets was 0.965 and 0.977, respectively. Calibration and decision curve analyses demonstrated that the nomogram had good predictive accuracy and clinical utility in estimating CRT risk in patients with hematologic malignancies.

In this study, we identified independent risk factors for CRT following PICC placement in patients with hematologic malignancies and developed a predictive model to assess CRT risk. The model demonstrated good discrimination, calibration, and clinical utility, enabling individualized risk assessment and intervention strategies to improve chemotherapy safety and extend PICC use duration.