Patient Characteristics and Outcomes of Cardiac Rehabilitation Following Thoracic Aortic Dissection Surgery: A Multicenter Retrospective Study
Bibliographic record
Abstract
PURPOSE: Despite the 2022 American Heart Association/American College of Cardiology guidelines recommending cardiac rehabilitation (CR) after aortic dissection repair, both patient participation in CR and the associated clinical outcomes remain poorly understood. METHODS: Adults (≥18 years of age) discharged alive following thoracic aortic dissection surgery across the Mayo Clinic Enterprise (January 2012 to November 2022), with follow-up until May 2024, were included (IRB #24-001141). Data were presented using summary statistics and logistic regression modeling. RESULTS: A total of 186 patients were referred; 37% were female, and the median age was 64 (51, 73) years. Dissections were classified as type A (43%), type B (18%), or a combination, and 55% had residual dissection. Over half (53%) did not start CR, with older age (aOR = 0.97: 95% CI, 0.94 - 0.99; P = .003), family history of aortic dissections and/or aneurysms (aOR = 0.17: 95% CI, 0.04 - 0.54; P = .005), and tobacco use (aOR = 0.35: 95% CI, 0.13 - 0.91; P = .037) being associated with lower odds of enrollment. Of the 87 who enrolled, 34 did so at a Mayo Clinic facility. There were no complications, with no difference in adverse outcomes. Among those with evaluations at graduation from CR (n = 21), quality of life (Dartmouth index 16 [14, 19] vs 25 [22, 30]; P = .002) and 6-minute walk distance (488 [375, 531] vs 302 [235, 398] m; P = .001) improved. CONCLUSIONS: Following thoracic aortic dissection repair, CR significantly improved quality of life and functional capacity without adverse events. Further work is needed to improve enrollment and establish data-driven safety margins for exercise.
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".