Derivation and Validation of a 4-Level Clinical Pretest Probability Score for Suspected Pulmonary Embolism to Safely Decrease Imaging Testing
Bibliographic record
Abstract
Importance: In patients with suspected pulmonary embolism (PE), overuse of diagnostic imaging is an important point of concern. Objective: To derive and validate a 4-level pretest probability rule (4-Level Pulmonary Embolism Clinical Probability Score [4PEPS]) that makes it possible to rule out PE solely on clinical criteria and optimized D-dimer measurement to safely decrease imaging testing for suspected PE. Design, Setting, and Participants: This study included consecutive outpatients suspected of having PE from US and European emergency departments. Individual data from 3 merged management studies (n = 11 114; overall prevalence of PE, 11%) were used for the derivation cohort and internal validation cohort. The external validation cohorts were taken from 2 independent studies, the first with a high PE prevalence (n = 1548; prevalence, 21.5%) and the second with a moderate PE prevalence (n = 1669; prevalence, 11.7%). A prior definition of pretest probability target values to achieve a posttest probability less than 2% was used on the basis of the negative likelihood ratios of D-dimer. Data were collected from January 2003 to April 2016, and data were analyzed from June 2018 to August 2019. Main Outcomes and Measures: The rate of PE diagnosed during the initial workup or during follow-up and the rate of imaging testing. Results: Of the 5588 patients in the derivation cohort, 3441 (61.8%) were female, and the mean (SD) age was 52 (18.5) years. The 4PEPS comprises 13 clinical variables scored from -2 to 5. It results in the following strategy: (1) very low probability of PE if 4PEPS is less than 0: PE ruled out without testing; (2) low probability of PE if 4PEPS is 0 to 5: PE ruled out if D-dimer level is less than 1.0 μg/mL; (3) moderate probability of PE if 4PEPS is 6 to 12: PE ruled out if D-dimer level is less than the age-adjusted cutoff value; (4) high probability of PE if 4PEPS is greater than 12: PE ruled out by imaging without preceding D-dimer test. In the first and the second external validation cohorts, the area under the receiver operator characteristic curves were 0.79 (95% CI, 0.76 to 0.82) and 0.78 (95% CI, 0.74 to 0.81), respectively. The false-negative testing rates if the 4PEPS strategy had been applied were 0.71% (95% CI, 0.37 to 1.23) and 0.89% (95% CI, 0.53 to 1.49), respectively. The absolute reductions in imaging testing were -22% (95% CI, -26 to -19) and -19% (95% CI, -22 to -16) in the first and second external validation cohorts, respectively. The 4PEPS strategy compared favorably with all recent strategies in terms of imaging testing. Conclusions and Relevance: The 4PEPS strategy may lead to a substantial and safe reduction in imaging testing for patients with suspected PE. It should now be tested in a formal outcome study.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".