Abstract 16999: Analysis of Risk Factors for Mortality and Morbidity of Surgical Aortic Valve Replacement for Aortic Stenosis: Risk Models From a Japanese Database
Bibliographic record
Abstract
Background: Surgical aortic valve replacement (sAVR) is the standard treatment for atherosclerotic aortic stenosis; however, trans-catheter aortic valve replacement (tAVR) is being increasingly used for high risk patients. The risks associated with sAVR have usually been assessed by operative mortality. However, it may be meaningful to assess the risk for operative morbidity. Purpose: This study aimed to create risk models associated with sAVR for mortality, as well as for combined mortality and morbidity (M-M), using the Japan Adult Cardiovascular Surgery Database. Methods: A total of 14,100 patients who underwent sAVR with/without CABG between 2009 and 2012 were retrospectively evaluated. We excluded patients with contraindications to tAVR, except for chronic dialysis. Multiple logistic regression analysis was used to create a risk model for mortality (30 days postoperative and in-hospital), and for M-M (patient was hospitalized longer than 90 days or patient’s daily activities were disturbed with a modified Rankin scale of 4 or more at discharge). Results: Mortality was 3.1%, and rate of M-M was 10.9%. Significant risk factors are listed in the table. Risk factors common to both mortality and M-M were older age, chronic dialysis, atrial fibrillation, higher NYHA class, chronic lung disease, cerebrovascular disease, and congestive heart failure. Risk factors specific to M-M were history of psychoneurotic disorder, diabetes mellitus, obesity, and left ventricular dysfunction. Conclusions: Analyzing the risk factors not only for mortality but also for M-M may be useful in identifying appropriate candidates for tAVR.
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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 | 0.005 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".