Age-Adjusted and Clinical Probability Adapted D-Dimer Cutoffs to Rule Out Pulmonary Embolism: A Narrative Review of Clinical Trials
Bibliographic record
Abstract
Diagnosis of pulmonary embolism remains a challenge for clinicians as its differential diagnosis is wide. The use of sequential diagnostic strategies based on the assessment of clinical probability, D-dimer measurement, and computed tomography pulmonary angiography have been validated in large prospective outcome studies. D-dimer measurement at a standard cutoff of 500 μg/L has gained wide acceptance to rule out pulmonary embolism in around 20 to 30% of patients with a clinically suspected pulmonary embolism. To improve the efficiency of D-dimer measurement, different ways of selecting a higher, albeit safe cutoff were explored: the age-adjusted D-dimer cutoff and the clinical adapted D-dimer cutoff. While both have been prospectively validated in large studies, some differences do exist. In particular, the prevalence of pulmonary embolism in these different validation studies was very different. Overall, the age-adjusted cutoff seems to be safer and less efficient, while the clinical probability adapted cutoff seems more efficient and less safe. Here, we report the available data regarding these two different ways to increase the diagnostic yield of D-dimer. Also, well beyond the accuracy of these adjusted/adapted cutoffs, some external factors, such as the prevalence of pulmonary embolism in the tested population and the clinical setting, have an important impact of the negative predictive value and on the overall efficiency of these cutoffs. Therefore, we also discuss which cutoff should be used according to the expected prevalence of the disease and according to the clinical setting.
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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 | 0.008 | 0.033 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.005 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".