Simple scores to predict 1-year mortality in atrial fibrillation
Bibliographic record
Abstract
BACKGROUND: Traditional scores as CHADS2 and CHA2DS2-Vasc are suitable for predicting stroke and systemic embolism in patients with atrial fibrillation (AF) and have shown to be also associated with mortality. Other more complex scores have been recommended for survival prediction. The purpose of our analysis was to test the performance of different clinical scores in predicting 1-year mortality in AF patients. MATERIAL AND METHODS: CHADS2 and CHA2DS2-Vasc scores were calculated for AF patients of the BLITZ-AF register and compared to R2-CHADS2, R2-CHA2DS2-Vasc and CHA2DS2VASc-RAF scores in predicting 1-year survival. Scores including renal function were calculated both with glomerular filtration rate (GFR) and creatinine clearance. RESULTS: One-year vital status (1960 alive and 199 dead) was available in 2159 patients. Receiver-operating characteristic curves displayed an association of each score to all-cause mortality, with R2(ClCrea)-CHADS2 being the best [area under the curve (AUC) 0.734]. Differences among the AUCs of the eight scores were not so evident, and a significant difference was found only between R2(ClCrea)-CHADS2 and CHADS2, CHA2DS2VASc, (ClCrea)-CHA2DS2-VASC-RAF.All the scores showed a similar performance for cardiovascular (CV) mortality, with CHA2DS2VASc-RAF being the best (AUC 0.757), with a significant difference with respect to CHADS2, CHA2DS2VASc, and (ClCrea)CHA2DS2Vasc-RAF. CONCLUSIONS: More complex scores, even if with better statistical performance, do not show a clinically relevant higher capability to discriminate alive or dead patients at 12 months. The classical and well known CHA2DS2VASc score, which is routinely used all around the world, has a high sensitivity in predicting all-cause mortality (AUC 0.695; Sensit. 80.4%) and CV mortality (AUC 0.691; Sensit. 80.0%).
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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".