MétaCan
Menu
← Back to cohort
Record W2583428454 · doi:10.1182/blood.v104.11.709.709

Clinical Outcomes with Unfractionated Heparin or Low-Molecular-Weight Heparin as Bridging Therapy in Patients on Long-Term Oral Anticoagulants: Results from the REGIMEN Registry.

2004· article· en· W2583428454 on OpenAlexaffabout
Alex C. Spyropoulos, Alexander G.G. Turpie, Andrew Dunn, John Spandorfer, James D. Douketis, A. Jacobson, Floyd J. Frost

Bibliographic record

VenueBlood · 2004
Typearticle
Languageen
FieldMedicine
TopicAtrial Fibrillation Management and Outcomes
Canadian institutionsSt. Joseph's HospitalHamilton General Hospital
Fundersnot available
KeywordsMedicinePerioperativeRegimenHeparinLow molecular weight heparinAdverse effectSurgeryTolerabilityProspective cohort studyAnticoagulantAnesthesiaInternal medicine

Abstract

fetched live from OpenAlex

Abstract Introduction: The optimal perioperative management of patients on long-term oral anticoagulant (OAC) therapy is currently unclear. There have been no large prospective clinical studies comparing unfractionated heparin (UFH) and low-molecular-weight heparin (LMWH) as bridge therapy in this setting. This registry compared the efficacy and safety of in-hospital UFH with mostly outpatient-based LMWH for perioperative bridging in patients on long-term OAC. Methods: REGIMEN is a large, prospective, multi-center registry in the USA and Canada of patients receiving chronic anticoagulation and requiring a perioperative bridge for an elective procedure or surgery. Patients who were ≥18 years, on OAC ≥3 months prior to the elective procedure, and who had heparin as a bridge to OAC for ≥2 days in the pre- and/or post-operative period, were included in the registry. Data on patient characteristics and primary clinical adverse events up to 30 days post-procedure were collected and compared between UFH and LMWH. Results: Of 1077 patients enrolled in this registry, 180 received UFH alone and 721 received LMWH (treatment or prophylactic dose) alone. Patients not receiving the same heparin pre- and post-procedure (n=123) and those undergoing multiple procedures (n=53) were excluded from the analysis. Patients on LMWH spent less time in hospital and were less likely to undergo major surgery or receive general anesthesia. Adverse events occurred at similar rates in both groups (table 1), and were primarily minor bleeds. Univariate analysis for procedure variables showed that UFH versus LMWH, intra-procedural anticoagulants/thrombolytics, procedure duration ≥45 minutes and general anesthesia were associated with an increased risk of a major adverse event. Logistic regression analysis for major bleeding can be seen in table 2. Conclusions: REGIMEN, a large, prospective, multi-center registry of patients in the US and Canada requiring bridging therapy with heparin for elective surgery, reveals that bridging therapy with LMWH in selected outpatients is at least as safe and effective as in-hospital UFH. Table 1. Adverse events in bridged patients UFH (n=164) LMWH (n=668) p-value Adverse event 28 (17.1%) 108 (16.2%) 0.81 Arterial/venous complication, major bleed, or death 13 (7.9%) 28 (4.2%) 0.07 Adverse Events: Arterial complications: Cardiac valvular or mural thrombus 1 (0.6%) 0 (0%) Intracranial event 1 (0.6%) 2 (0.3%) TIA 1 (0.6%) 2 (0.3%) Peripheral arterial event 1 (0.6%) 0 (0%) Venous complications: DVT 0 (0%) 2 (0.3%) PE 0 (0%) 0 (0%) Major bleed 9 (5.5%) 22 (3.3%) 0.25 Minor bleed 15 (9.1%) 80 (12.0%) 0.34 Thrombocytopenia 2 (1.2%) 3 (0.4%) Death 2 (1.2%) 4 (0.6%) Table 2. Logistic regression analysis for major bleed Independent variable Reference OR 95% CI Vascular surgery Not vascular surgery 4.72 0.92–24.09 General surgery Not general surgery 2.25 0.78–6.53 Charlson score >1 Charlson score ≤1 2.24 1.02–4.93 Procedure duration ≥45mins Procedure duration <45mins 1.37 0.59–3.18 LMWH post-op UFH post-op 0.76 0.32–1.81

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)0.002
metaresearch head score (Gemma)0.004
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 score0.002
Threshold uncertainty score0.008

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.004
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.002
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0000.000
Research integrity0.0000.000
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 head0.051
GPT teacher head0.351
Teacher spread0.301 · 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".

Quick stats

Citations12
Published2004
Admission routes2
Has abstractyes

Explore more

Same venueBlood→Same topicAtrial Fibrillation Management and Outcomes→French-language works237,207→