Risk factors analysis and the establishment of nomogram prediction model for PICC-related venous thrombosis in patients with lymphoma: a double-center cohort-based case-control study

Abstract

Objective: The objective of this study is to examine the risk factors associated with the occurrence of PICC-Related Venous Thrombosis (PICC-RVTE) in individuals diagnosed with lymphoma, as well as to develop a predictive risk nomogram model.

Methods: A total of 215 patients with lymphoma treated at Yunnan Provincial Tumor Hospital from January 2017 to December 2020 were retrospectively evaluated as the training cohort; 90 patients with lymphoma treated at the Department of Oncology of the First People's Hospital of Anning, Affiliated to Kunming University of Science and Technology during the January 2021 to September 2023 were evaluated as the validation cohort. Independent influencing factors were analyzed by logistic regression, a nomogram was developed and validated, and the model was evaluated using internal and external data cohorts for validation.

Results: A total of 305 lymphoma patients were selected and 35 (11.48%) PICC-RVTE occurred, the median time was 13 days. The incidence within 1-2week was 65.71%. Multivariate analysis suggested that the activity amount, thrombosis history(within the last 12 months), ATIII, Total cholesterol and D-dimer levels were independently associated with PICC-RVTE, and a nomogram was constructed based on the multivariate analysis. ROC analysis indicated good discrimination in the training set (area under the curve [AUC] = 0.907, 95%CI:0.850-0.964) and the testing set (AUC = 0.896, 95%CI: 0.782-1.000) for the PICC-RVTE nomogram. The calibration curves showed good calibration abilities, and the decision curves indicated the clinical usefulness of the prediction nomograms.

Conclusions: Patients should be advised to undergo color Doppler ultrasound system testing within two week after the implantation of a PICC catheter to detect PICC-RVTE at an early stage. The validated nomogram can be used to predict the risk of catheter-related thrombosis (CRT) in patients with lymphoma who received at least one chemotherapy after PICC catheterization, no bleeding tendency, no recent history of anticoagulant exposure and no severe heart, lung, renal insufficiency. This model has the potential to assist clinicians in formulating individualized treatment strategies for each patient.

Keywords: catheterization; logistic models; lymphoma neoplasms; nomogram; prognosis.

Figure 1

Experimental roadmap of this study. This flow diagram indicates the workflow of the method present in this study.

Figure 2

The time distribution between peripherally inserted central catheter (PICC) insertion to the onset of thrombosis.

Figure 3

Nomogram for predicting PICC-RVTE risk in lymphoma patients after PICC catheterization. To use the nomogram, the points corresponding to each prediction variable were obtained, then the sum of the points was calculated as the total score, and the predicted risk corresponding to the total score was the probability of PICC-RVTE.

Figure 4

Receiver operating characteristic (ROC) curve analysis for PICC-RVTE risk prediction. ROC curves of PICC-RVTE risk prediction in the training set (A) and the testing set (B). AUC was calculated using bootstrapping, and its 95% CI was estimated. The P-value were two-sided. The AUC and 95% CI in the training set and the testing set were 0.907(95%CI:0.850-0.964) and 0.896(95%CI: 0.782-1.000), respectively, and Delong test P>0.05.

Figure 5

Calibration curves in training and validation sets. (A) calibration curve in the training cohort; (B) calibration curve in the validation cohort. The gray thick line represents a perfect prediction by an ideal model, the black dashed line indicates the target parameter and the solid black line shows the performance of the model. Using bootstrap resampling (times = 1000).

Figure 6

The DCAs curve of the nomogram was observed in both the training and validation cohorts. (A) The decision curve of the nomogram for predicting PICC-RVTE risk in the training cohort; (B) The decision curve of the nomogram for predicting PICC-RVTE risk in the validation cohort. The prediction model is represented by a black dashed line, while the gray solid line represents all samples that were intervened, and the black solid horizontal line represents all samples that were not intervened. The graph illustrates the expected net benefit of each patient in relation to the nomogram’s ability to predict PICC-RVTE formation risk. The net benefit increases as the model curve extends.