The use of variable D-dimer thresholds for the improved diagnosis of pulmonary embolism in the inpatient population: A retrospective cohort study
Bibliographic record
Abstract
Introduction: Diagnosis of pulmonary embolism (PE) among inpatients can be challenging, with the use of variable D-dimer thresholds validated only in outpatients. We sought to determine the negative predictive value (NPV) of such thresholds in inpatients for PE diagnosis. Methods: We performed a retrospective cohort study of inpatients with a D-dimer measured within 72 hours of a computed tomography pulmonary angiography (CTPA) or ventilation perfusion scan (VQ) for suspected PE at a tertiary care center in Montreal, Canada, between January 2012 and December 2019. D-dimers were assessed using the 500 μg FEU/L and age-adjusted thresholds and clinical pretest probability of PE using the Wells score and the PEGeD algorithm. The primary outcome of PE incidence was reported, and the negative predictive values of various D-dimer based approaches were determined. Using institutional and provincial cost information, we explored the cost-effectiveness of a D-dimer-based approach. Results: Our cohort included 290 patients, 68% female with a median age of 72 (52, 84) years old. Thirty-six patients were found to have a PE, for an incidence of 12%. Fourteen (5%) patients had a D-dimer value <500 μg FEU/L, yielding a NPV of 100%, 95% CI (76.8% to 100%). Twenty-seven (9%) inpatients had a negative age-adjusted D-dimer, yielding a NPV of 100%, 95% CI (87.2% to 100%). Discussion: This exploratory study suggests that D-dimer testing may have a role in inpatients to improve appropriate diagnostic testing and cost-effectiveness. The use of variable D-dimer thresholds in the inpatient population for PE diagnosis is an important area for further research.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".