What Is the Specificity of the Aortic Dissection Detection Risk Score in a Low‐prevalence Population?
Bibliographic record
Abstract
BACKGROUND: Acute aortic syndrome (AAS) is a time-sensitive and difficult-to-diagnose aortic emergency. The American Heart Association (AHA) proposed the acute aortic dissection detection risk score (ADD-RS) as a means to reduce miss rate and improve time to diagnosis. Previous validation studies were performed in a high prevalence population of patients. We do not know how the rule will perform in a lower-prevalence population. This is important because application of a rule with low specificity would increase imaging rates and complications. Our goal was to assess if the diagnostic accuracy of the score would be maintained in a low-prevalence population that we are attempting to risk stratify in the emergency department (ED). METHODS: Retrospective cohort of patients age 18 years old and older who presented to two tertiary care EDs from January 1, 2015, to December 31, 2015, and underwent a computed tomographic angiography to rule out AAS. Two trained reviewers extracted data using a standardized data collection form. AAS was defined according to accepted radiologic standards. The components of the AHA risk score were defined a priori. Agreement was measured using kappa statistic. Sensitivity, specificity, and positive and negative likelihood ratios with 95% confidence intervals (CIs) were calculated. Analysis was performed using SAS 9.4 University Edition. RESULTS: A total 370 patients underwent computed tomography for suspected AAS. Chief presenting symptoms were chest pain (207, 58%), back pain (26, 7%), abdominal pain (32, 8.6%), syncope (7, 2.6%), and symptoms of stroke (6, 1.6%). AAS was finally diagnosed in 12 (3.2%) patients: five (1.4%) type A aortic dissection, four (1%) type B aortic dissection, two (0.5%) an aortic intramural hematoma, no penetrating aortic ulcer, and one a ruptured abdominal aortic aneurysm. The presence of one or more ADD risk markers (ADD-RS ≥ 1) was associated with a sensitivity of 100% (95% CI = 73.5%-100%) and a specificity of 12.3% (95% CI = 9.1%-16.2%) for the diagnosis of AAS. The negative likelihood ratio was 0 and the positive likelihood ratio was 1.14 (95% CI = 1.1-1.2). CONCLUSIONS: Our study confirms that in North America the prevalence of AAS in those undergoing advanced imaging is low. The ADD-RS in this population has a low specificity. A lack of defined inclusion criteria and a low specificity limits the application of this rule in practice.
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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 | 0.080 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".