Accuracy and usefulness of a clinical prediction rule and D-dimer testing in excluding deep vein thrombosis (DVT) in cancer patients
Bibliographic record
Abstract
19522 Background: Cancer patients frequently present with thrombotic complications and rapid, accurate diagnostic testing would reduce morbidity and mortality. Although the combination of a low clinical probability using clinical prediction rules (e.g. Well’s Score) and a negative D-dimer result have proven to be safe and reliable in ruling out DVT in the general population, the accuracy of such a strategy is less certain in cancer patients. Because cancer patients often have alternative reasons for leg swelling and pain, and because malignancy and chemotherapy can render the D-dimer test positive in the absence of DVT, we hypothesize that the Well’s Score and D-dimer testing are potentially less accurate and less useful in excluding DVT in patients with active cancer. Methods: We performed a retrospective analysis of 2 prospective studies to compare the diagnostic test characteristics of the Well’s Score and D-dimer testing between patients with and without cancer presenting with suspected DVT. Results: A total of 1630 patients were studied; 107 had cancer. DVT was confirmed in 39.3% of patients with and 13.7% of patients without cancer. In both patient groups, the proportions of patients with DVT were significantly different among the high-, moderate- and low-probability groups according to the Well’s score (P<0.001). However, significantly fewer cancer patients (19.6%) had a low-probability score compared to patients without cancer (47.5%) (P<0.001). Similarly, 36.4% of cancer vs. 60.4% of noncancer patients had a negative D-dimer result (P<0.001). In cancer patients, a low probability score alone had a sensitivity of 95.2% (95%CI 82.6%-99.2%) and a specificity of 29.2% (95% CI 18.9%-42.0%). In combination with D-dimer testing, the sensitivity improved to 100% (95%CI 31.0%-100%) but the specificity was reduced to 26.4% (95%CI 13.5%-44.7%). In contrast, the specificity in patients without cancer was preserved at 53.9% (95%CI 50.4%-57.3%). Conclusion: DVT can be ruled out in cancer patients with a low clinical probability of DVT and a negative D-dimer result. However, the low specificity of these tests excludes very few patients and thereby limits their clinical usefulness. No significant financial relationships to disclose.
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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 | 0.004 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".