Work

Risk Factors for Febrile Neutropenia in Patients With Newly Diagnosed Diffuse Large B-Cell Lymphoma Undergoing Initial R-CHOP Treatment

Year: 2026

Type: article

Abstract: Background: Febrile neutropenia (FN) frequently occurs as a complication among patients with diffuse large B-cell lymphoma (DLBCL) receiving their initial rituximab plus cyclophosphamide, doxorubicin, vincristine, and prednisone (R-CHOP) therapy. This study aimed to identify the risk factors for FN and create a reliable predictive model for FN using machine learning methods. Methods: This retrospective study evaluated 238 patients newly diagnosed with DLBCL and treated with the R-CHOP regimen. Logistic regression was used to identify the risk factors for FN. In addition, a machine learning model was developed to predict the occurrence of FN. Results: The incidence rate of FN was 23.9%. Univariate analysis revealed significant associations between FN and bone marrow involvement (odds ratio [OR], 2.78; 95% confidence interval [CI], 1.36-5.66; P = .005), stage III and IV disease (OR, 3.39; 95% CI, 1.69-6.84; P = .001), Eastern Cooperative Oncology Group Performance Status score of ⩾ 2 (OR, 2.75; 95% CI, 1.13-6.67; P = .025), lactate dehydrogenase levels ⩾ 240 U/L (OR, 2.68; 95% CI, 1.35-5.32; P = .005), and involvement of at least 2 extranodal sites (OR, 2.69; 95% CI, 1.44-5.02; P = .002). Machine learning techniques were applied to construct predictive models for FN, achieving C-statistics of 0.751 to 0.879 in cross-validation and 0.692 to 0.861 in independent training and testing experiments. Notably, patients with FN (57.6%) had a substantially inferior 5-year overall survival (OS) rate than those without FN (77.1%) ( P = .007). Conclusions: Patients with DLBCL who develop FN after their first R-CHOP treatment have a significantly worse OS than those without FN. Tailored prophylaxis with granulocyte-colony stimulating factor and antibiotics may be essential in this population. Models with moderate to strong predictive power can be designed using various artificial intelligence techniques.

Source: Clinical Medicine Insights Oncology

DOI: 10.1177/11795549261423995