A novel, comprehensive tool for predicting 30-day mortality after surgical aortic valve replacement
Bibliographic record
Abstract
OBJECTIVES: We sought to develop and validate a novel risk assessment tool for the prediction of 30-day mortality after surgical aortic valve replacement incorporating a patient's frailty. METHODS: Overall, 4718 patients from the multicentre study OBSERVANT was divided into derivation (n = 3539) and validation (n = 1179) cohorts. A stepwise logistic regression procedure and a criterion based on Akaike information criteria index were used to select variables associated with 30-day mortality. The performance of the regression model was compared with that of European System for Cardiac Operative Risk Evaluation (EuroSCORE) II. RESULTS: At 30 days, 90 (2.54%) and 35 (2.97%) patients died in the development and validation data sets, respectively. Age, chronic obstructive pulmonary disease, concomitant coronary revascularization, frailty stratified according to the Geriatric Status Scale, urgent procedure and estimated glomerular filtration rate were independent predictors of 30-day mortality. The estimated OBS AVR score showed higher discrimination (area under curve 0.76 vs 0.70, P < 0.001) and calibration (Hosmer-Lemeshow P = 0.847 vs P = 0.130) than the EuroSCORE II. The higher performances of the OBS AVR score were confirmed by the decision curve, net reclassification index (0.46, P = 0.011) and integrated discrimination improvement (0.02, P < 0.001) analyses. Five-year mortality increased significantly along increasing deciles of the OBS AVR score (P < 0.001). CONCLUSIONS: The OBS AVR risk score showed high discrimination and calibration abilities in predicting 30-day mortality after surgical aortic valve replacement. The addition of a simplified frailty assessment into the model seems to contribute to an improved predictive ability over the EuroSCORE II. The OBS AVR risk score showed a significant association with long-term 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 | 0.002 | 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 | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".