Incremental Prognostic Value of Cardiac Computed Tomography in Coronary Artery Disease Using CONFIRM
Bibliographic record
Abstract
BACKGROUND: Large multicenter studies validating the prognostic value of coronary computed tomographic angiography (CCTA) and left ventricular ejection fraction (LVEF) are lacking. We sought to confirm the independent and incremental prognostic value of coronary artery disease (CAD) severity measured using 64-slice CCTA over LVEF and clinical variables. METHODS AND RESULTS: A large international multicenter registry (CONFIRM Registry) was queried, and CCTA patients with LVEF data on CCTA were screened. Patients with a history of myocardial infarction, coronary revascularization, or cardiac transplantation were excluded. The National Cholesterol Education Program-Adult Treatment Panel III risk was calculated for each patient, and CCTA was evaluated for CAD severity (normal, nonobstructive, non-high-risk, or high-risk CAD) and LVEF <50%. Patients were followed for an end point of all-cause mortality; 27 125 patients underwent CCTA at 12 participating centers, with a total of 14 064 patients meeting the analysis criteria. Follow-up was available for 13 966 (99.3%) patients (mean follow-up of 22.5 months; 95% confidence interval, 22.3 to 22.7 months). All-cause mortality (271 deaths) occurred in 0.65% of patients without coronary atherosclerosis, 1.99% of patients with nonobstructive CAD, 2.90% of patients with non-high-risk CAD, and 4.95% for patients with high-risk CAD. Multivariable analysis confirmed that LVEF <50% (hazard ratio, 2.74; 95% confidence interval, 2.12 to 3.51) and CAD severity (hazard ratio,1.58; 95% confidence interval, 1.42 to 1.76) were predictors of all-cause mortality, and CAD severity had incremental value over LVEF and clinical variables. CONCLUSIONS: Our results demonstrate that CCTA measures of CAD severity and LVEF have independent prognostic value. Incorporation of CAD severity provides incremental value for predicting all-cause death over routine clinical predictors and LVEF in patients with suspected obstructive CAD.
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 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 | 0.004 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| 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 | 0.001 |
| 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".