Diagnostic Accuracy and Impact of Computed Tomographic Coronary Angiography on Utilization of Invasive Coronary Angiography
Bibliographic record
Abstract
BACKGROUND: Computed tomographic coronary angiography (CTA), given its high negative predictive value, is a potential gatekeeper for invasive coronary angiography (ICA). Before CTA can be further accepted into clinical practice, its impact on healthcare resources needs to be better understood. We sought to determine the clinical impact of CTA on ICA referrals, CTA accuracy, and normalcy rate. METHODS AND RESULTS: To determine the impact of CTA, consecutive patients (n=7017) undergoing ICA before and after implementing a dedicated cardiac CT program were reviewed and compared with 3 other centers (n=11 508). To determine CTA accuracy, we evaluated consecutive CTA patients who underwent ICA. For normalcy rate, we identified patients with a low pretest probability for obstructive coronary artery disease. With the implementation of a cardiac CT program, the frequency of normal ICA decreased from 31.5% (1114 of 3538 patients) to 26.8% (932 of 3479 patients) (P<0.001). These findings were significantly different (P=0.003) from the 3 centers, in which normal ICAs were unchanged (30.0% [1870 of 6224 patients] to 31.0% [1642 of 5284 patients]). CTA had excellent per-patient sensitivity (99% [CI, 95% to 100%]), positive predictive value (92% [CI, 86% to 96%]) and negative predictive value (95% [CI, 72% to 100%]). Because of referral bias, specificity (64% [CI, 44% to 81%]) was low; however, the normalcy rate of CTA was 94% (CI, 90% to 97%). After adjusting for referral bias, the adjusted sensitivity was 90% (CI, 89% to 91%), and the adjusted specificity was 95% (CI, 94% to 96%), with positive and negative predictive values of 92% (CI, 91% to 93%) and 93% (CI, 92% to 94%), respectively. CONCLUSIONS: The clinical implementation of CTA appears to positively impact ICA by reducing the frequency of normal ICA. The operating characteristics of CTA support its potential role as a tool useful in ruling out obstructive coronary artery disease.
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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 | 0.055 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".