Predicting Late Adverse Events in Uncomplicated Stanford Type B Aortic Dissection: Results From the ROADMAP Validation Study
Bibliographic record
Abstract
BACKGROUND: Risk stratification is highly desirable in patients with uncomplicated Stanford type B aortic dissection but inadequately supported by evidence. We sought to validate externally a published prediction model for late adverse events (LAEs), consisting of 1 clinical (connective tissue disease) and 4 imaging variables: maximum aortic diameter, false lumen circumferential angle, false lumen outflow, and number of identifiable intercostal arteries. METHODS: We assembled a retrospective multicenter cohort (ROADMAP [Registry of Aortic Diseases to Model Adverse Events and Progression]) of 401 patients with uncomplicated Stanford type B aortic dissection presenting to 1 of 8 aortic centers between 2001 and 2013, followed until 2020. LAEs were defined as fatal or nonfatal aortic rupture, new refractory hypertension or pain, organ or limb ischemia, aortic aneurysm formation (≥6 cm), or rapid growth (≥1 cm per year). We applied the original model parameters to the validation cohort and examined the effect on risk categorization using LAE end points. RESULTS: One hundred and seventy-six patients (44%) with incomplete imaging or clinical data were excluded. Of 225 patients in the final cohort, 90 (40%) developed LAEs, predominantly driven by aneurysm formation. Baseline maximum aortic diameter was significantly larger in patients with (42.6 [95% CI, 39.1–45.8] mm) compared with patients without LAEs (39.9 [95% CI, 36.3–44.2] mm; P =0.001). A multivariable Cox regression model indicated that only maximum diameter was associated with LAEs (hazard ratio, 1.07 [95% CI, 1.03–1.11] per mm; P <0.001), while the other parameters were not ( P >0.05). Applying the original prediction model to the validation cohort resulted in a poor 5-year sensitivity (38%) and specificity (69%). CONCLUSIONS: A clinical and imaging-based prediction model performed poorly in the ROADMAP cohort. Maximum aortic diameter remains the strongest predictor of LAEs in uncomplicated Stanford type B aortic dissection.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| 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".