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Record W4417018511 · doi:10.1182/blood-2025-4904

Arterial thrombotic events in lymphoma patients: Systematic review and meta-analysis

2025· article· en· W4417018511 on OpenAlexaboutno aff
Tamara Bibic, Vojin Vuković, Jelena Ivanović, Sofija Kozarac, Zoran Bukumirić, Darko Antić, Jawed Fareed

Bibliographic record

VenueBlood · 2025
Typearticle
Languageen
FieldMedicine
TopicCNS Lymphoma Diagnosis and Treatment
Canadian institutionsnot available
Fundersnot available
KeywordsFunnel plotStudy heterogeneityMeta-analysisPublication biasConfidence intervalUnivariate analysisUnivariateLymphoma

Abstract

fetched live from OpenAlex

Abstract Introduction: Arterial thrombotic events (ATE) represent a serious but underreported complication in lymphoma patients, potentially impacting morbidity and mortality. The prevalence of ATE among lymphoma patients shows considerable variation across studies, influenced by multiple factors and consequently dynamic during the course of the disease. The reason lies in marked heterogeneity of lymphoma, with unique characteristics depending on the specific subtype, status of the disease, patient characteristics and treatment modalities. The systematic review and meta-analysis aimed to provide the pooled prevalence of ATE in lymphoma patients and identify associated risk factors. Methods: A systematic review and meta-analysis were performed following PRISMA guideline. The search for studies included several electronic databases: PubMed, Web of Science, Cochrane Library, Scopus - all up to June 2024. The inclusion criteria were adult study population, with any type of newly diagnosed, relapsed or previous diagnosis of Hodgkin or non-Hodgkin's lymphoma. Extracted data implied: basic study characteristics, participant characteristics, intervention details and outcomes. The Newcastle-Ottawa scale was used to analyse the quality of included studies. For the pooling of single proportions, we used the inverse variance methods with logit transformation. Confidence intervals for individual studies were estimated using the Clopper-Pearson method. The heterogeneity between studies was explored using Cochran's Q test and r2 statistics, the Baujat plot, and quantified with the l2 statistic. Univariate meta-regression analyses were used to identify potential predictors. Publication bias was assessed using the funnel plot and Egger's test. Sensitivity analysis was performed by excluding studies that might influence the results of the meta-analysis. A significance level of 0.05 was applied. Results: Final analysis included six studies comprising 9293 patients. Across the studies, 63 ATE were reported. The pooled estimation of events was 1.23% (95% CI: 0.47% to 3.20%) under the random-effects model, with the prediction interval ranged from 0.04% to 28.61%, demonstrating significant heterogeneity (I² = 92.4%). According to Baujat plot, only one study had a disproportionate influence on heterogeneity. Sensitivity analysis excluding the influential study revealed a slight increase in the pooled rate (1.82%), with reduced heterogeneity (I² = 81.2%). Disproportion of the overall heterogeneity has arisen probably due to methodological or population differences. However, the value of the pooled effect remained consistent. A symmetrical funnel plot (Egger's test p = 0.809) showed no significant publication bias. Univariate meta-regression analysis revealed that increasing mean age was significantly associated with a higher prevalence of arterial thrombosis. Variables as follow-up duration and female gender proportion didn't show a significant association with event rates. Conclusions: ATE are less common than venous thrombotic events in lymphoma but remain clinically relevant. This meta-analysis identifies a low pooled event rate of ATE and increasing mean age as a significant associated variable, emphasizing the need for proper risk-adapted thromboprophylaxis strategies in lymphoma patients.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.014
metaresearch head score (Gemma)0.035
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Meta-analysis · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.020
Threshold uncertainty score0.072

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0140.035
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0200.034
Bibliometrics0.0080.009
Science and technology studies0.0010.001
Scholarly communication0.0040.002
Open science0.0020.002
Research integrity0.0020.002
Insufficient payload (model declined to judge)0.0050.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.019
GPT teacher head0.279
Teacher spread0.260 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designMeta-analysis
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2025
Admission routes1
Has abstractyes

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