A comparison of four risk models for the prediction of cardiovascular complications in patients with a history of atrial fibrillation undergoing non‐cardiac surgery
Bibliographic record
Abstract
Summary It is unclear how best to predict peri‐operative cardiovascular risk in patients with atrial fibrillation undergoing non‐cardiac surgery. This study examined the accuracy of the revised cardiac risk index and three atrial fibrillation thrombo‐embolic risk models for predicting 30‐day cardiovascular events after non‐cardiac surgery in patients with a pre‐operative history of atrial fibrillation. We conducted a prospective cohort study in 28 centres from 2007 to 2013 of 40,004 patients ≥ 45 years of age undergoing inpatient non‐cardiac surgery who were followed until 30 days after surgery for cardiovascular events (defined as myocardial injury, heart failure, stroke, resuscitated cardiac arrest or cardiovascular death). The 2088 patients with a pre‐operative history of atrial fibrillation were at higher risk of peri‐operative cardiovascular events compared with the 34,830 patients without a history of atrial fibrillation (29% vs. 13%, respectively, adjusted odds ratio 1.30 (95% CI 1.17–1.45). Compared with the revised cardiac risk index (c‐index 0.60), all atrial fibrillation thrombo‐embolic risk scores were significantly better at predicting peri‐operative cardiovascular events: CHADS 2 (c‐index 0.62); CHA 2 DS 2 ‐ VAS c (c‐index 0.63); and R 2 CHADS 2 (c‐index 0.65), respectively. Although the three thrombo‐embolic risk prediction models were significantly better than the revised cardiac risk index for prediction of peri‐operative cardiovascular events, none of the four models exhibited strong discrimination metrics. There remains a need to develop a better peri‐operative risk prediction model.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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".