Development of A Clinical Prediction Rule for Risk Stratification of Recurrent Venous Thromboembolism In Patients with Cancer-Associated Venous Thromboembolism
Bibliographic record
Abstract
Abstract Abstract 475 Background: Current guidelines suggest that all cancer patients with venous thromboembolism be treated with long-term low molecular weight heparin (LMWH). However, whether treatment strategies should vary according to patient and malignancy characteristics, in particular whether patients with low risk of VTE recurrence can be identified, remains unknown. Methods: We performed a single centre retrospective cohort study conducted at the Thrombosis Unit of the Ottawa Hospital. The charts of patients with cancer and VTE followed from 2002 to 2004 and from 2007 to 2008 were reviewed to assess the feasibility of derivation of a clinical prediction rule that stratifies VTE recurrence risk in patients with cancer—associated venous thrombosis through identification and evaluation of characteristics of malignancy and other clinical characteristics. We analysed only the patients who had a recurrent VTE within the first 6 months of anticoagulation. A univariate analysis determined the strength of association between each potential predictor and VTE recurrence. All potential predictor variables (p<0.25) were evaluated in a logistic regression model. Result: Of 543 patients 55 (10.1%) presented with a VTE recurrence during the first 6 months of anticoagulation. At VTE recurrence 19 (9.5%) patients were using VKA and 36 (10.5%) patients were using LMWH. The relative risk for VTE recurrence was not significantly different between patients who used VKA or LMWH [RR= 1. 13 (95%CI, 0.743 – 1.711; p= 0.565)]. A multivariate analysis suggested that gender, primary tumour site, tumour stage and history of prior VTE were significant variables to include in the clinical prediction rule. The final model included female gender, lung cancer and prior history of VTE as increasing risk and breast cancer and stage I disease as lowering risk. Patients with a score equal or less than 0 have low risk (4.5%) for VTE recurrence and this represented 48% of our patients. Patients with a score equal or above 1 have high risk (> 19%) for VTE recurrence (Tables 1 and 2). Conclusion: We were able to derive a simple and easy scoring system that stratifies patients with cancer-associated thrombosis into low or high risk of recurrent VTE. Future prospective validation of the model is warranted and may be very relevant to better tailor anticoagulation treatment in this heterogeneous population. Disclosure: No relevant conflicts of interest to declare.
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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".