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Record W4389233413 · doi:10.1182/blood-2023-187817

Management of Catheter-Related Upper Extremity Deep Vein Thrombosis in Patients with Cancer: A Systematic Review and Meta-Analysis

2023· review· en· W4389233413 on OpenAlexaff
Tzu‐Fei Wang, Roger Kou, Marc Carrier, Aurélien Delluc

Bibliographic record

VenueBlood · 2023
Typereview
Languageen
FieldHealth Professions
TopicCentral Venous Catheters and Hemodialysis
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsMedicineCancerProspective cohort studySurgeryMeta-analysisRetrospective cohort studyRandomized controlled trialThrombosisCohort studyDeep veinInternal medicine

Abstract

fetched live from OpenAlex

Background: Patients with cancer commonly require a central venous catheter (CVC) for delivery of cancer therapies and/or supportive care. The presence of a CVC is associated with an increased risk of venous thromboembolism (VTE). Despite the frequent occurrence, the optimal anticoagulation management and outcomes for patients with cancer and catheter-related upper extremity deep vein thrombosis (DVT) are unclear. Objective: We performed a systematic review and meta-analysis to evaluate the rates of recurrent VTE and bleeding complications in patients with cancer and catheter-related upper extremity DVT. Methods: We searched MEDLINE, Embase, Scopus, and CENTRAL (Cochrane) from inception to June 2, 2023. We aimed to include randomized controlled trials (RCTs) (if any), prospective and retrospective cohort studies, and case-control studies (including ≥ 10 patients) evaluating adult patients with cancer and catheter-related upper extremity DVT. The primary efficacy outcome was recurrent VTE, and the primary safety outcome was major bleeding as defined by individual studies. Secondary outcomes included clinically relevant non-major bleeding, total bleeding events, and all-cause mortality. By using R software (version 4.0.3), the incidence rates (with 95% confidence interval [CI]) of recurrent VTE and bleeding outcomes at different time points were pooled using random effects model. Results: After screening 4,264 records, we included 29 studies (N=2,848 patients) in the systematic review. There were no RCTs, 5 prospective cohort studies (including 260 patients), and 24 retrospective cohort studies. Most studies had modest sample size (N<100). Overall from 24 studies with reported tumor types, the most common malignancy was hematological malignancy (50.4%), followed by gastrointestinal (13.6%), breast (10.2%), and lung (5.5%) cancers. The median or mean follow-up duration varied widely from 1 to 20 months. Duration of anticoagulation also varied considerably with median or mean duration of 20 days to 6 months. The main long-term anticoagulant used was low-molecular-weight heparin (58.1%), followed by direct oral anticoagulants (24.3%). Only two studies (N=87 patients) specifically reported the management and associated outcomes after catheter removal. Thirteen studies (N=1105 patients) reported a 3-month recurrent VTE rate, with the pooled rate of 1% (95% CI 0-3%, I 2 = 0%) (Figure 1). Nine studies (N=811 patients) reported a 3-month major bleeding rate, with the pooled rate of 2% (95% CI 1-5%, I 2 = 0%) (Figure 2). We were unable to pool event rates beyond 3 months given high degree of heterogeneity in the follow-up duration, bleeding definition, and duration of anticoagulation. Conclusions: Our systematic review and meta-analysis demonstrated relatively low rates of recurrent VTE and major bleeding events within the first 3 months in patients with cancer and catheter-related upper extremity DVT. However, there was significant heterogeneity in the management and reporting after 3 months, and available data are poor to guide treatment strategies and duration of anticoagulation. The optimal management after catheter removal was also unclear. Future large prospective studies are needed.

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)0.009
metaresearch head score (Gemma)0.024
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: Review · Consensus signal: Review
Teacher disagreement score0.016
Threshold uncertainty score0.047

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.024
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0160.027
Bibliometrics0.0060.008
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0020.001
Research integrity0.0020.001
Insufficient payload (model declined to judge)0.0030.000

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 head0.093
GPT teacher head0.386
Teacher spread0.293 · 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
GenreReview

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".

Quick stats

Citations0
Published2023
Admission routes1
Has abstractyes

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