Duration of adenosine-induced myocardial hyperaemia: insights from quantitative 13N-ammonia positron emission tomography myocardial perfusion imaging
Bibliographic record
Abstract
AIMS: This study aimed to assess the impact of adenosine on quantitative myocardial blood flow (MBF) in a rapid stress-rest protocol compared with a rest-stress protocol using 13N-ammonia positron emission tomography (PET) myocardial perfusion imaging (MPI) and to gain insights into the time dependency of such effects. METHODS AND RESULTS: Quantitative MBF at rest (rMBF) and during adenosine-induced stress (sMBF) and myocardial flow reserve (MFR) were obtained from 331 retrospectively identified patients who underwent 13N-ammonia PET MPI for suspected chronic coronary syndrome and who all exhibited no perfusion defects. Of these, 146 (44.1%) underwent a rapid stress-rest protocol with a time interval (Δtstress-rest) of 20 ± 4 min between adenosine infusion offset and rest imaging, as per clinical routine. The remaining 185 (55.9%) patients underwent a rest-stress protocol and served as the reference. Groups did not differ regarding demographics, risk factors, medication, left ventricular function, and calcium scores. rMBF was significantly higher in the stress-rest vs. the rest-stress group [0.80 (interquartile range 0.66-1.00) vs. 0.70 (0.58-0.83) mL·min-1·g-1, P < 0.001], and, as sMBF was identical between groups [2.52 (2.20-2.96) vs. 2.50 (1.96-3.11), P = 0.347], MFR was significantly lower in the stress-rest group [3.07 (2.43-3.88) vs. 3.50 (2.63-4.10), P = 0.007]. There was a weak correlation between Δtstress-rest and rMBF (r = -0.259, P = 0.002) and between Δtstress-rest and MFR (r = 0.163, P = 0.049), and the proportion of patients with abnormally high rMBF was significantly decreasing with increasing Δtstress-rest. CONCLUSION: Intravenously applied adenosine induces a long-lasting hyperaemic effect on the myocardium. Consequently, rapid stress-rest protocols could lead to an overestimation of rMBF and an underestimation of MFR.
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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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".