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Evaluation of cabozantinib (cabo) in combination with direct oral anticoagulants (DOAC) or low molecular weight heparin (LMWH) in renal cell carcinoma (RCC).

2021· article· en· W3135044645 on OpenAlexaff
Akram Mesleh Shayeb, Danielle Urman, Nazlı Dizman, Luís Meza, Akhilesh Sivakumar, Chun Loo Gan, Hannah Dzimitrowicz, Tian Zhang, Pedro C. Barata, Mehmet Asım Bilen, Xīn Gào, Daniel Yick Chin Heng, Sumanta K. Pal, Marina D. Kaymakcalan, Bradley A. McGregor, Toni K. Choueiri, Rana R. McKay

Bibliographic record

VenueJournal of Clinical Oncology · 2021
Typearticle
Languageen
FieldMedicine
TopicVenous Thromboembolism Diagnosis and Management
Canadian institutionsUniversity of Calgary
Fundersnot available
KeywordsMedicineLow molecular weight heparinCabozantinibRenal cell carcinomaHemostasisInternal medicineRivaroxabanCancerClinical endpointConcomitantFondaparinuxApixabanThrombosisAnticoagulantSurgeryClinical trialWarfarinVenous thromboembolism

Abstract

fetched live from OpenAlex

291 Background: Venous thromboembolism (VTE) is the second leading cause of death in patients with cancer. Despite cabo improving RCC outcomes, VTE management in these patients remains a challenge, partly due to poor understanding of cabo safety profile and drug interactions with anticoagulants. Recent anti-Xa DOAC studies demonstrated comparable efficacy and safety with LMWH for VTE treatment in patients with cancer. Thus far, cabo clinical trials have largely allowed concurrent LMWH use but not DOACs. Herein, we investigated the hemostasis safety profile of cabo with different anticoagulants in patients with RCC. Methods: We performed a retrospective multicenter study (7 sites) of patients with advanced RCC receiving treatment with cabo. Patients were allocated into three groups: cabo with concomitant use (at least 1 week) of 1) DOACs (anti-Xa inhibitors), 2) LMWH, or 3) no anticoagulant. Primary endpoint was to evaluate the rate of major bleeding events (defined per the International Society of Hemostasis and Thrombosis criteria) in the above groups. Secondary endpoint was rate of new/recurrent VTE while on anticoagulation. Overall comparison between groups was analyzed by Fisher exact test. If a difference was found, then pairwise comparison was done. Results: Between 2016-2020, 172 patients with RCC received cabo (DOAC 50, LMWH 18, and no anticoagulant 104). At initiation, cabo median dose was 60 mg but 45% had dose reduction. Median age was 63 [IQR 57-69]. Most were males (77%), had clear cell histology (81.5%), underwent nephrectomy (76.7%), and had intermediate IMDC risk disease (59%). Cabo was first, second, and subsequent line of therapy in 19.8%, 34.9%, and 45.3% of patients, respectively. The table below shows major bleeding and VTE events between groups. An overall difference of major bleeding was found between the three groups comparison ( p=0.009). There was no difference in major bleeding events between patients who received DOAC vs LMWH ( p=0.28) and DOAC vs no anticoagulant ( p=0.1) but there was a difference between LMWH vs no anticoagulant ( p=0.02). Two patients died from bleeding (one in LMWH and one in DOAC group). Conclusions: This study highlights the first reported real world experience of cabo with different anticoagulants in patients with advanced RCC. Cabo use with a DOAC had a similar bleeding risk in comparison to patients not receiving any anticoagulation. In carefully selected patients, DOACs can be considered as concurrent medications in those receiving cabo. Given the low number of patients receiving LMWH, it is difficult to draw conclusions from this group. Data are currently being updated to expand subjects receiving DOAC and LMWH in our cohort. [Table: see text]

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)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.116
GPT teacher head0.440
Teacher spread0.324 · 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 designObservational
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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Citations2
Published2021
Admission routes1
Has abstractyes

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