Long-Term Risk Factors for Occult Cancer Detection in Patients with Unprovoked Venous Thromboembolism: A Post-Hoc Analysis of the Reverse Study
Bibliographic record
Abstract
Abstract Background: The diagnosis of an unprovoked (i.e., in the absence of any risk factors) venous thromboembolism (VTE) may be a sign of an underlying cancer. Previous estimates of the incidence rate of occult cancer in patients with unprovoked VTE are as high as 5-10%. However, screening methods to detect cancer early in these patients are of limited clinical value and previous identification of risk factors focused on short-term cancer diagnosis. Therefore, this study will assess the long-term incidence and risk factors of cancer in unprovoked VTE patients. Methods: This retrospective study is a post-hoc analysis of the REVERSE (Recurrent Venous Thromboembolism Risk Stratification Evaluation) study. Patient data was collected following an initial six-month course of anticoagulation therapy and were followed for a mean time period of 5.0 years (range 1 month-8 years). We performed univariable analysis to test the strength of association between each potential predictor variable and occult cancer diagnosis. Furthermore, using forward model selection we created a multivariable model with optimal predictive value for cancer diagnosis. Results: Among 663 patients presenting with unprovoked VTE, 38 (5.7%) subsequently developed a new cancer diagnosis during the follow-up period. The most common types of cancers diagnosed were prostate (21%) and colorectal cancer (16%). Univariate analysis revealed age > 65 (OR 2.59; 95% CI 1.31-4.49, P= 0.0036), elevated D-Dimer (OR 2.71; 95% CI 1.38-5.31, P=0.026) and pulmonary vein obstruction (PVO) score (OR 4.34 95% CI 1.26-14.92, P=0.023) as the strongest predictors of occult cancer. Only D-Dimer (Adj.-OR 2.72 95% CI 1.39-5.32) remained in the multivariate model and no other factor significantly improved the model's fit. Conclusion: Patients with an unprovoked VTE remain at an elevated risk of developing cancer following anticoagulation therapy. Age > 65 years old, elevated D-Dimer, and PVO in pulmonary embolism patients are among potential risk factors for developing occult cancer. More studies are needed to validate these potentially important risk factors. Disclosures Carrier: BMS: Honoraria, Research Funding; Leo Pharma: Research Funding; Pfizer: Honoraria; Bayer: Honoraria. Rodger:Biomerieux: Research Funding. Kovacs:Daiichi Sankyo Pharma Development: Research Funding; Bayer: Research Funding.
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 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 | 0.003 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".