Different cut-off values of quantitative D-dimer methods to predict the risk of venous thromboembolism recurrence: a post-hoc analysis of the PROLONG study
Bibliographic record
Abstract
BACKGROUND: The PROLONG study showed that patients with venous thromboembolism who had qualitatively abnormal results in a D-dimer assay (Clearview Simplify D-dimer) after discontinuation of vitamin K antagonism benefit from resumption of treatment with vitamin K antagonism. The objective of this study was to evaluate the possible advantage of using quantitative D-dimer assays. DESIGN AND METHODS: VIDAS D-dimer Exclusion (bioMerieux), Innovance D-DIMER (Dade Behring), HemosIL D-dimer HS (Instrumentation Laboratory) and STA Liatest D-dimer (Diagnostica Stago) assays were performed in plasma aliquots sampled 30+/-10 days after cessation of vitamin K antagonism in 321 patients enrolled in the PROLONG study. RESULTS: During the follow-up without vitamin K antagonism, 25 patients had recurrent venous thromboembolism. The cut-off levels of the quantitative assays giving results most comparable with those of the qualitative test were: VIDAS = 800 ng/mL; Innovance = 800 ng/mL; HemosIL HS = 300 ng/mL; STA Liatest = 700 ng/mL. When the effect of the patients' age (< or = 70 vs. >70 years) was analyzed, it was found that only in younger patients was the rate of recurrence of venous thromboembolism significantly higher in patients with abnormal D-dimer levels. However, using the quantitative assays and age-specific cut-off levels it was possible to determine statistically significant hazard ratios also in elderly patients (VIDAS = 600 and 1200 ng/mL, Innovance = 500 and 900 ng/mL, HemosIL HS = 250 and 450 ng/mL, STA Liatest = 700 and 1000 ng/mL, in patients aged < or = 70 and >70 years, respectively). CONCLUSIONS: Quantitative D-dimer assays may provide information useful for evaluating the individual risk of recurrent venous thromboembolism. They seem particularly advantageous since they allow the selection of different cut-off levels according to the age or other characteristics of the patients.
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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 | 0.015 | 0.020 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".