Comparing prognostic significance of dual bolus and dual sequence quantitative stress perfusion cardiac magnetic resonance
Bibliographic record
Abstract
AIMS: Quantitative stress perfusion (QP) cardiac magnetic resonance (CMR) can be performed using the dual sequence (DS) or dual bolus (DB) technique. DS does not require additional contrast and image acquisition but needs a research sequence. DB can be performed on all magnetic resonance imaging (MRI) scanners with standard perfusion sequences but requires additional contrast injection and image acquisition. Our aim was to compare the prognostic significance of DB and DS. METHODS AND RESULTS: DB and DS were performed on the same patient and the same examination. Analysts were blinded to clinical outcomes. Stress myocardial blood flow (MBF) and myocardial perfusion reserve (MPR) were quantified. The primary outcome was a composite of major adverse cardiovascular events (MACE) comprising acute coronary syndrome, stroke, heart failure (HF) hospitalization, late revascularization, and all-cause death. 570 patients (mean age: 63.2 ± 12.3 years; 61.2% male) were recruited. Median follow-up was 743 days; 54 events were documented. All QP CMR variables demonstrated significance in univariate Cox regression [DB stress MBF [HR = 0.53 (95%CI:0.35-0.78)], DB MPR [HR = 0.38 (95%CI:0.22-0.66)], DS stress MBF [HR = 0.27 (95%CI:0.18-0.40)] and DS MPR [HR = 0.19 (95%CI:0.13-0.29)]]. On multivariable Cox regression models, only DB MPR, DS MBF, and DS MPR remained significant for MACE (HR = 0.50 (95%CI:0.28-0.89), HR = 0.35 (95%CI:0.23-0.53); HR = 0.23 (95%CI 0.15-0.36), respectively). Harrell's C-index of DS MPR and DS stress MBF showed significantly better prognostication than their DB counterparts (P < 0.001 and P = 0.012, respectively). CONCLUSION: In this blinded comparison, DS stress MBF and MPR demonstrated better prognostication than DB stress MBF and MPR. Our findings support DS as the preferred approach where available.
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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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".