Accuracy of a Rapid Diagnostic Test for the Presence of Direct Oral Factor Xa or Thrombin Inhibitors in Urine—A Multicenter Trial
Bibliographic record
Abstract
Abstract The rapid determination of the presence of direct oral anticoagulants (DOACs) in a patient remains a major challenge in emergency medicine and for rapid medical treatment decisions. All DOACs are excreted into urine. A sensitive and specific point-of-care test has been developed to determine whether they are present in patient urine samples. This prospective multicenter study aimed to demonstrate at least 95% correct positive and negative predictive results for factor Xa and thrombin inhibitors in urine samples using DOAC Dipstick pads compared with liquid chromatography-tandem mass spectrometry (LC-MS/MS) (NCT03182829). Nine hundred and fourteen subjects were included and 880 were evaluated per protocol (factor Xa inhibitors apixaban, edoxaban, and rivaroxaban: n = 451, thrombin inhibitor dabigatran: n = 429) at 18 centers. The sensitivity, specificity, accuracy, and predictive values and agreement between methods for determination of factor Xa inhibitors were at least noninferior to 95% with a 0.5% margin and of thrombin inhibitor superior to 97.5%. These results were compared with LC-MS/MS results in the intention-to-analyze cohort (all p < 0.05). The receiver operating curve showed c-values of 0.989 (factor Xa inhibitors) and 0.995 (thrombin inhibitor). Visual evaluation of the factor Xa and thrombin inhibitor pads was not different between centers. Qualitative determination of both types of DOACs was accurate using the DOAC Dipstick compared with using LC-MS/MS. The high predictive values may impact laboratory and clinical decision-making processes.
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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 | 0.016 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 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".