Evidence map›Paper›PMID 41727336›Full record

ArticleClinical Medicine Insights. Oncology2026

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

Liang-Ying Chen, Che-An Tsai, Po-Wei Liao, Ling-Chiao Teng, Cheng-Hsien Lin, Yu-Chen Su, Chieh-Lin Jerry Teng

Abstract read
In one paragraph

Article in Clinical Medicine Insights. Oncology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
1citing papers in PubMed, 1 pooled it
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

1 citing paper in PubMed, 1 synthesis or guideline pooled it.

  1. Pooled it
4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

7 authors.

Liang-Ying ChenDivision of Hematology/Medical Oncology, Department of Medicine, Taichung Veterans General Hospital, Taichung.ORCID https://orcid.org/0000-0003-3509-8848
Che-An TsaiDivision of Infectious Disease, Department of Medicine, Taichung Veterans General Hospital, Taichung.
Po-Wei LiaoDivision of Hematology/Medical Oncology, Department of Medicine, Taichung Veterans General Hospital, Taichung.
Ling-Chiao TengDivision of Hematology/Medical Oncology, Department of Medicine, Taichung Veterans General Hospital, Taichung.
Cheng-Hsien LinDivision of Hematology/Medical Oncology, Department of Medicine, Taichung Veterans General Hospital, Taichung.
Yu-Chen SuDivision of Hematology/Medical Oncology, Department of Medicine, Taichung Veterans General Hospital, Taichung.
Chieh-Lin Jerry TengDivision of Hematology/Medical Oncology, Department of Medicine, Taichung Veterans General Hospital, Taichung.ORCID https://orcid.org/0000-0001-9744-0368

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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; 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.

Indexed as

DLBCLfevermachine learningneutropeniaR-CHOP

Identifiers

PMID41727336
PMCPMC12923925

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.