THE INFLUENCE OF INCREASING AGE ON OUTCOMES WITH MECHANICAL CIRCULATORY SUPPORT
Bibliographic record
Abstract
Purpose: Increasing age is known to impact outcomes after various cardiac surgery procedures. Similar data for the implantation of mechanical circulatory support devices is not available. This study examined the relationship between advancing age and clinical outcomes during mechanical circulatory support. Methods: Utilizing data from the Novacor LVAS global registry, the study cohort consisted of all registry patients except those with an indication of destination therapy (70), or those with missing data (26). The resulting cohort of 1365 patients were grouped by age: <40 years (n=316), 40–49 years (n=353), 50–59 years (n=451), and 60 years of age and older (n=245). Results: Regression analysis found advanced age (≥60 year group) was a significant predictor of mortality (OR 2.36, 95% CI 1.78–3.12). Only 44% of these patients survived during mechanical circulatory support, whereas, 65% of the younger patients (<60 years) survived. There was also a two-fold decrease in the risk of death for those recipients <40 years (OR 0.50, 95 CI 0.38–0.66). Conclusion: Increasing age does adversely impact outcomes during LVAD support. While increasing age is probally also an indicator for other co-morbid conditions at the time of implant, age remains a powerful discriminator of survival. This data may be useful in developing patient selection guidelines and risk profiles for mechanical circulatory support.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".