Abstract

Background and Objectives:

Thromboembolism (TE) is a common and serious complication in patients with malignancies, including those with lymphoma, who are at high risk of TE. The incidence of TE in lymphoma patients ranges from 5% to 59.5%. This study aims to investigate the clinical characteristics and high-risk factors of thrombosis in lymphoma patients and to develop a nomogram model for early warning of thromboembolism events.

Materials and Methods:

Clinical data from 790 newly diagnosed lymphoma patients (January 2019 to December 2021) were analyzed, dividing them into thrombus and no thrombus groups. Key clinical features, lab indicators, and lymphoma characteristics were compared. TE risk factors were assessed using multifactor analysis. A nomogram model for predicting lymphoma-related TE was developed using R version 4.3.0, rms, and ggplot packages, and validated with Bootstrap resampling. Model performance was evaluated with ROC and calibration curves.

Results:

  1. Clinical Features: TE occurred in 9.75% of patients(77/790), with common sites being upper limb deep veins(58.44%), superficial veins(22.08%), and lower limb deep veins(20.78%).Median age of TE patients was 58 years, with TE occurring a median of 4 months post-diagnosis.

  2. Survival Outcomes: TE patients had lower survival rates compared to non-TE patients at 6 months, 1 year, and 2 years.TE complications led to death in 6.50% of TE patients, while disease progression caused 16.88% of deaths.Additionally, 2 patients (2.60%) experienced lower limb functional impairment due to TE, and 1 patient (1.30%) with intracranial TE had cognitive dysfunction.

  3. Risk Factor Analysis: Significant risk factors included high ECOG scores, previous venous thromboembolism, coronary artery disease, and central venous catheterization.

  4. Prediction Model: The nomogram model showed good predictive accuracy, with ROC AUC values exceeding 0.7 for 0.5, 1, and 2-year predictions.Validation showed consistent predictive performance with an average AUC of 0.705.

Conclusion:

The study identifies key risk factors for TE in lymphoma patients and presents a validated nomogram model for early prediction of TE, which can aid in clinical decision-making and improve patient outcomes.

Disclosures

No relevant conflicts of interest to declare.

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2024
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