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 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 | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.005 |
| 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 | 0.000 |
| 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".