A Diagnostic Solution for Lupus Anticoagulant Testing in Patients Taking Direct Oral FXa Inhibitors Using DOAC Filter
Bibliographic record
Abstract
Background: Direct oral factor Xa (FXa) inhibitors interfere with lupus anticoagulant (LA) assays challenging antiphospholipid syndrome diagnosis in treated patients. We evaluated a new device, called DOAC Filter, and its usefulness in this setting. It is a single-use filtration cartridge in which FXa inhibitor compounds are trapped by non-covalent binding while plasma is filtered through a solid phase. Patient samples were analyzed before and after filtration: 38 rivaroxaban, 41 apixaban, and 68 none. Anticoagulant plasma concentrations were measured using specific anti-Xa assays and HPLC-MS/MS. LA testing was performed using dilute Russell Viper Venom Time (dRVVT) and Silica Clotting Time (SCT). Baseline median [min–max] concentrations were 64.8 [17.6; 311.4] for rivaroxaban and 92.1 ng/mL [37.1; 390.7] for apixaban (HPLC-MS/MS). They were significantly correlated with anti-Xa assay results (r = 0.98 and r = 0.94, respectively). dRVVT was positive in 92% rivaroxaban and 72% apixaban and SCT in 28 and 41% of samples, respectively. Post-filtration, median % of neutralization was 100% with rivaroxaban and apixaban concentrations of, respectively, <2 [<2–2.4] and <2 ng/mL [<2–9.6] using HPLC-MS/MS. No significant effect of DOAC Filter was observed on LA testing in controls (n = 31) and LA-positive (n = 37) non-anticoagulated samples. dRVVT and SCT remained positive in, respectively, 16 and 8% of rivaroxaban and 41 and 18% of apixaban samples. DOAC Filter would be an easy-to-use device allowing FXa inhibitor removal from plasma samples, limiting their interference with LA testing in treated 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".