Validation of A Clinical Prediction Rule for Risk Stratification of Recurrent Venous Thromboembolism In Patients with Cancer-Associated Venous Thromboembolism
Bibliographic record
Abstract
Abstract Abstract 4209 Background: The risk of recurrent venous thromboembolism (VTE) in patients with cancer-associated VTE, remains high even with the use of low molecular weight heparin (LMWH). However, due to the heterogeneity of the disease it is probable that recurrence risk varies widely. We have developed a prediction rule to classify risk of recurrence in the first 6 months of treatment: + 1 is scored for each of female gender, lung cancer and prior VTE and – 1 is scored for breast cancer and – 2 for TNM stage 1 disease. With a score of ≤ 0, 4.5% of patients recur (this represented 48% of the patient populations), and > 0, 19.7% recur. The rule was derived in a retrospective cohort study of patients followed at the Thrombosis unit of the Ottawa hospital and requires validation. Methods: We applied our rule in a new set of 819 consecutive patients with cancer-associated VTE from 2 multicentre randomized controlled trials comparing LMWH with vitamin K antagonists (VKA) (ClotCant group). In these studies the stage of disease was not separated by exact TNM classification, rather patients were classified as stage I, II (no metastasis) versus III, IV (metastasis). As such, we redid our derivation model with stage I and II grouped together, which gave this variable a score of – 1. This resulted in a prediction rule which gave a recurrence risk that no longer clearly dichotomized risk; rather gave a low, intermediate, and high risk groups. As in our derivation study, we evaluated patients' risk of recurrence regardless of type of anticoagulant use (VKA or LMWH). Results: Of 819 patients, 86 (10.5%) presented with a VTE recurrence during the anticoagulation period. When we applied our derivation rule in this population, we were able to demonstrate a significant difference in VTE recurrence risk dependent on gender, primary tumour site, stage and history of prior VTE. Patients with a score < 0 have low risk (5.1%) for VTE recurrence and this represented 19% of the patient population; patients with a score of 0 had a intermediate risk (9.8%) and this represented 42% of patients; a score ≥ 1 was high risk (13.9%), occurring in 38% of the population. Dichotomizing the results gave a recurrence risk of 8% in patients with a score ≤ 0 and a 15.2% recurrence risk with a score > 0. Conclusion: the validation dataset suggests reproducibility of our model. The dichotomized score is less discriminatory than our original model suggesting an advantage to classifying patients tumour stage as TNM stage I versus stage II, III and IV. Unfortunately, we could not test this hypothesis with the ClotCant dataset. Our model appears to differentiate risk for recurrence and should be utilized in treatment trials: attempting novel treatment strategies in high risk patients since LMWH alone does not seem to be enough; and using the less costly typical “LMWH followed by oral anticoagulants” in the low risk population to evaluate whether VKA can be as safe and effective as long term LMWH. Disclosures: Lee: Eisai: Research Funding; Sanofi Aventis: Consultancy, Honoraria; Leo Pharma: Consultancy; Pfizer: Consultancy, Honoraria; Bayer: Honoraria; Boehringer Ingelheim: Consultancy, Honoraria, Speakers Bureau.
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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 | 0.013 | 0.048 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".