Patient Preferences for Coronary CT Angiography with Stress Perfusion, SPECT, or Invasive Coronary Angiography
Bibliographic record
Abstract
Background Patient preference is pivotal for widespread adoption of tests in clinical practice. Patient preferences for invasive versus other noninvasive tests for coronary artery disease are not known. Purpose To compare patient acceptance and preferences for noninvasive and invasive cardiac imaging in North and South America, Asia, and Europe. Materials and Methods This was a prospective 16-center trial in 381 study participants undergoing coronary CT angiography with stress perfusion, SPECT, and invasive coronary angiography (ICA). Patient preferences were collected by using a previously validated questionnaire translated into eight languages. Responses were converted to ordinal scales and were modeled with generalized linear mixed models. Results In patients in whom at least one test was associated with pain, CT and SPECT showed reduced median pain levels, reported on 0–100 visual analog scales, from 20 for ICA (interquartile range [IQR], 4–50) to 6 for CT (IQR, 0–27.5) and 5 for SPECT (IQR, 0–25) (P < .001). Patients from Asia reported significantly more pain than patients from other continents for ICA (median, 25; IQR, 10–50; P = .01), CT (median, 10; IQR, 0–30; P = .02), and SPECT (median, 7; IQR, 0–28; P = .03). Satisfaction with preparation differed by continent and test (P = .01), with patients from Asia reporting generally lower ratings. Patients from North America had greater percentages of “very high” or “high” satisfaction than patients from other continents for ICA (96% vs 82%, respectively; P < .001) and SPECT (95% vs 79%, respectively; P = .04) but not for CT (89% vs 86%, respectively; P = .70). Among all patients, CT was preferred by 54% of patients, compared with 18% for SPECT and 28% for ICA (P < .001). Conclusion For cardiac imaging, patients generally favored CT angiography with stress perfusion, while study participants from Asia generally reported lowest satisfaction. © RSNA, 2019 Online supplemental material is available for this article. See also the editorial by Woodard and Nguyen in this issue.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".