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Record W2996535411 · doi:10.1182/blood-2019-125292

Predictors of Bleeding in the Perioperative Anticoagulant Use for Surgery Evaluation (PAUSE) Study

2019· article· en· W2996535411 on OpenAlexaff
Alfonso Tafur, Na Li, Nathan P. Clark, Sam Schulman, Alex C. Spyropoulos, Joseph A. Caprini, James D. Douketis

Bibliographic record

VenueBlood · 2019
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsMcMaster University
Fundersnot available
KeywordsMedicineDabigatranRivaroxabanApixabanPerioperativeAtrial fibrillationLogistic regressionAnticoagulantWarfarinSurgeryInternal medicineAnesthesia

Abstract

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Introduction In the Perioperative Anticoagulant Use for Surgery Evaluation (PAUSE) Study a simplified, standardized, pharmacokinetic based perioperative interruption scheme was stablished for patients with non-valvular atrial fibrillation taking direct oral anticoagulants (DOACs). The rate of bleeding and thromboembolic outcomes was low, yet better awareness of bleeding predictors will aid the informed decision making for both clinicians and patients. Thus, we aimed to define the factors associated with perioperative bleeding events in the PAUSE cohorts. Methods The standardized interruption scheme is depicted in figure 1. We analyzed bleeding as the composite of major and clinically relevant non-major bleeding (MB/CRNM) according to the ISTH criteria. Putative predictors of bleeding were prospectively collected during the PAUSE recruitment. For patients taking dabigatran the dilute thrombin time was measured from a pre-operative blood sample. A DOAC-calibrated anti-factor Xa level was assessed preoperatively for patients taking factor Xa inhibitors. Continuous numeric variables were reported as means with SD; frequencies were reported when appropriate. We used stratified logistic regression models for analysis. All statistical analyses were performed in R version 3.6.0. Results There were 3007 patients requiring perioperative DOAC interruption, distributed in the apixaban (A) (N = 1,257), dabigatran (D) (N = 668), and rivaroxaban (R) (N = 1,082) cohorts. Characteristics of the patients who developed MB/CRNM are reported in table 1. More than one third of included patients underwent a high bleeding risk procedure. The rates of MB/CRNM were 3.02% in group A, 2.84% in group D, and 4.16% of group R patients from the point of DOAC interruption to 30 days after the procedure. In the univariate analysis, surgery bleeding risk was significantly associated with MB/CRNM (low vs high, OR 0.56, 95% CI 0.36-0.86; p=0.008). Multivariate analysis stratified by region found hypertension (OR 1.81, 95%CI 1.07-3.07; p=0.027), prior bleed or bleed predisposition (OR 1.62, 95% CI 1.02-2.58; p=0.043) were significantly associated with MB/CRNM (table 2); whereas surgery bleeding risk was not significant after adjusting other covariates. Female gender was associated with lower MB/CRNM risk (This result seems to be mainly driven by the rate of CRNM, 2.21% male had CRNM and 1.57% female had CRNM, and the signal disappeared in the model for MB). [Hypertension (OR 3.93, 95%CI 1.40-11.07; p=0.010), active cancer (OR 2.30, 95% CI 1.10-4.81; p=0.026) were significantly associated with MB (table 3)] Aspirin use was more prevalent among males (13.78%) than females (7.75%), but this did not fully explain the gender effect. The model for MB/CRNM had an area under the curve (AUC) of 0.65 (standard error 0.03). Drug level analysis was available for 2541 (84.5%) patients. In the stratified analysis with the DOAC level (>50ng/dL vs £50ng/dL) as a single predictor, there was no significant association with MB/CRNM observed (OR 1.07, 95% CI 0.38-2.96; p=0.902). In the multivariate model, adding DOAC level to the model did not improve the AUC. Discussion A standardized interruption scheme results in low risk of bleeding for patients with atrial fibrillation interrupting DOACs for surgery/invasive procedures. The protocol-defined surgical bleed risk in PAUSE was valid, and selected to numerically higher bleeding rates, but it was not independent predictor of bleeding when the interruption scheme and covariates are accounted for. Hypertension was the only potentially modifiable predictor of perioperative MB/CRNM bleeding. Likely selecting for a frail population, active cancer and hypertension were predictors of MB events. In the exploratory analyses, residual levels did not improve the predictive model. The multivariate model had a low performance and we do not advocate modification of the PAUSE-proposed interruption schema based on these results. The PAUSE trial is the first large scale study enabled to investigate the factors governing perioperative bleed among DOAC-Anticoagulated patients. Nonetheless, we did not have power to analyze DOAC cohorts independently due to the paucity outcomes. Overall, the results strengthen the validity of the PAUSE-proposed DOAC interruption scheme for atrial fibrillation patients who need surgery. Disclosures Tafur: Recovery Force: Consultancy; Janssen: Other: Educational Grants, Research Funding; BMS: Research Funding; Idorsia: Research Funding; Daichi Sanyo: Research Funding; Stago: Research Funding; Doasense: Research Funding. Spyropoulos:Bayer: Consultancy; Ingelheim: Consultancy; Portola: Consultancy; Janssen: Research Funding; Boehringer INgelheim: Research Funding; Janssen: Consultancy. Douketis:Pfizer: Membership on an entity's Board of Directors or advisory committees; Sanofi: Membership on an entity's Board of Directors or advisory committees; Leo Pharma: Membership on an entity's Board of Directors or advisory committees; Bristol-Myers Squibb: Membership on an entity's Board of Directors or advisory committees; Portola: Other: Consultant ; Janssen: Other: Consultant ; The Merck Manual: Patents & Royalties; Up-to-Date: Patents & Royalties.

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)0.002
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: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.006

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0000.001
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 head0.150
GPT teacher head0.365
Teacher spread0.215 · 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
Published2019
Admission routes1
Has abstractyes

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