Malperfusion in Patients With Acute Type A Aortic Dissection: A Nationwide Analysis
Bibliographic record
Abstract
BACKGROUND: This study describes in detail the clinical burden of malperfusion associated with acute type A aortic dissection (ATAAD) in a large, national cohort and the effect of treatment strategy on outcomes. METHODS: All patients undergoing repair of ATAAD between 2017 and 2020 in The Society of Thoracic Surgeons (STS) Adult Cardiac Surgery Database were studied. Malperfusion was defined using STS definitions on the basis of imaging or the surgeon's evaluation. Multivariable logistic regression was used to analyze the effect of patient and treatment factors on outcomes in patients with and without malperfusion. RESULTS: A total of 9958 patients undergoing ATAAD repair were studied. Preoperative malperfusion occurred in 27.7% (2748 of 9958) of cases and most often involved the extremity (14.9%; 1484 of 9958), renal (10.2%), or cerebral (9.8%) vascular beds. Operative mortality was much greater among patients with malperfusion (26.8% vs 13.6%; P < .001). After adjustment, coronary malperfusion was associated with the highest odds of mortality (odds ratio, 2.28; 95% CI, 1.85-2.81; P < .001) followed by mesenteric malperfusion (odds ratio, 1.82; 95% CI, 1.45-2.28; P < .001). Cerebral malperfusion was not independently associated with significantly increased odds of mortality (odds ratio, 1.14; 95% CI, 0.94-1.38; P = .18). Partial arch replacement (zone 1 or zone 2) compared with ascending aorta or hemiarch replacement only showed a similar rate of mortality in patients with malperfusion (24.8% vs 26.9%; P = .99) and without malperfusion (11.6% vs 13.6%; P = .54). CONCLUSIONS: Preoperative malperfusion in ATAAD was common and associated with significant operative mortality, which varied according to the malperfused region. Partial aortic arch replacement, compared with ascending aorta or hemiarch replacement alone, was not associated with increased mortality.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".