Centralized Triage of Suspected Coronary Artery Disease Using Coronary Computed Tomographic Angiography to Optimize the Diagnostic Yield of Invasive Angiography
Bibliographic record
Abstract
Background: Coronary computed tomographic angiography (CCTA) is preferable to invasive coronary angiography (ICA) for coronary artery disease (CAD) diagnosis in elective patients without known CAD. Methods: We conducted a nonrandomized interventional study involving 2 tertiary care centres in Ontario. From July 2018 to February 2020, outpatients referred for elective ICA were identified through a centralized triage process and were recommended to undergo CCTA first instead of ICA. Patients with borderline or obstructive CAD on CCTA were recommended to undergo subsequent ICA. Intervention acceptability, fidelity, and effectiveness were assessed. Results: A total of 226 patients were screened, with 186 confirmed to be eligible, of whom 166 had patient and physician approval to proceed with CCTA (89% acceptability). Among consenting patients, 156 (94%) underwent CCTA first; 43 (28%) had borderline/obstructive CAD on CCTA, and only 1 with normal/nonobstructive CAD on CCTA was referred for subsequent ICA against protocol (99% fidelity). Overall, 119 of 156 CCTA-first patients did not have ICA within the following 90 days (i.e., 76% potentially avoided ICA, due to the intervention). Among the 36 who underwent ICA post-CCTA per protocol, 24 had obstructive CAD (66.7% diagnostic yield). If all patients who were referred for and underwent ICA at either centre between July 2016 and February 2020 (n = 694 pre-implementation; n = 333 post-implementation) had had CCTA first, an additional 42 patients per 100 would have had an obstructive CAD finding on their ICA (95% confidence interval = 26-59). Conclusion: A centralized triage process, in which elective outpatients referred for ICA are instead referred for CCTA first, appears to be acceptable and effective in diagnosing obstructive CAD and improving efficiencies in our healthcare system.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| 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".