Venous Thromboembolism Following Major Surgery and Risk of Recurrence: A Population-Based Cohort Study
Bibliographic record
Abstract
Abstract Abstract 5243 Background: Venous thromboembolism (VTE) which includes deep vein thrombosis (DVT) and pulmonary embolism (PE) is a common complication of major surgery. Little is known of the risk of recurrent VTE following postoperative VTE. Objectives: We used the administrative health claims (RAMQ) and hospital discharge (MED ECHO) databases of Quebec, Canada to conduct a population-based retrospective cohort study to describe the time to VTE following general surgery and to assess the risk of VTE recurrence among patients with surgery-provoked VTE. Methods: Using a previously defined cohort of individuals who had a first-time discharge diagnosis of VTE between January 1996 and December 2004 in MED ECHO, which systematically records information on all hospital admissions in the province of Quebec, we identified patients who had undergone major surgery in the 3 months prior to the VTE. Subjects were followed from the time of incident VTE until first VTE recurrence, death, or end of study (December 31, 2005). Results: Our study population consisted of 9629 patients with new VTE up to 3 months following major surgery. The mean age was 63.9 years (SD=15.2) and 53% were female. General surgery (32%) was the most common procedure. The mean time to VTE after surgery was 29.5 days (SD=62.6) with 30% of events diagnosed after discharge. The 5-year adjusted cumulative risk of recurrence was 7.9%. Patients with VTE diagnosed after discharge had an adjusted relative risk of recurrence of 1.43 (95% confidence interval (1.21, 1.69)) compared to patients with VTE diagnosed before discharge. Conclusions: Our results suggest that surgery-provoked VTE occurs frequently after discharge and is associated with an elevated risk of recurrence, and that patients with post-discharge VTE are at a higher risk of recurrence than patients with VTE diagnosed before discharge. Thromboprophylaxis strategies post-discharge should be considered in this population. Disclosures: Tagalakis: Sanofi Aventis: Research Funding; Pfizer: Research Funding.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".