UTILITY OF A GENERAL PROGNOSTIC SCORE IN IDENTIFYING PATIENTS WITH POOR OUTCOMES AFTER AORTIC VALVE REPLACEMENT
Bibliographic record
Abstract
The 2017 American College of Cardiology guidelines recommend consideration of life expectancy during aortic valve replacement (AVR) evaluation; however, how to estimate prognosis remains uncertain. We evaluated whether a popular general prognostic score, the Lee index (JAMA 2006), could aid in identifying patients with limited life expectancy who may not benefit from AVR. We prospectively enrolled 246 older patients undergoing surgical or transcatheter AVR at an academic center and assessed their ability to perform activities of daily activity and physical tasks over 12 months. The Lee index (range: 0–41) was calculated before surgery. Poor outcome was defined as death, or New York Heart Association Class III or IV with functional decline over 12 months. Of 91 surgical and 137 transcatheter patients with available outcome data, the mean Lee index score was 9.2 in surgical patients (range: 3–17) and 13.4 in transcatheter patients (range: 7–23). In the combined cohort, the risk of poor outcome increased with higher risk score quartiles (6.8%, 17.9%, 20.0%, 34.0%; p-for-trend<0.001). A similar trend was observed in the surgical patients (2.1%, 4.0%, 15.4%, 20.0%; p-for-trend=0.05). In comparison, no such trend existed in transcatheter patients (27.3%, 29.0%, 31.3%, 35.4%; p-for-trend=0.42). Our results suggest that while a prognostic model developed from the general population may be useful for surgical AVR patients, it has limited utility in identifying patients undergoing transcatheter AVR, with a higher burden of comorbidity, frailty, and functional limitations. Thus, better prognostic tools are needed to guide decisions for this specific population.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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".