Multicenter Evaluation of Myocardial Flow Reserve as a Prognostic Marker for Mortality in ¹³N-Ammonia PET Myocardial Perfusion Imaging
Bibliographic record
Abstract
ABSTRACT Background Myocardial flow reserve (MFR), measured by PET MPI, provides valuable information on epicardial coronary disease, diffuse atherosclerosis, and microvascular function. Despite its routine use, the prognostic efficacy of 13 N-ammonia PET MFR remains unconfirmed in larger multicenter cohorts of patients with suspected or known coronary artery disease (CAD). Methods We considered patients from five sites in the REFINE PET registry who underwent 13 N-ammonia PET MPI for CAD. Clinical and imaging data were collected at the time of MPI. MFR was quantified as the ratio of stress to rest myocardial blood flow, using QPET software (Cedars-Sinai Medical Center, Los Angeles, CA). The primary outcome was all-cause mortality (ACM). Survival analyses were performed using Kaplan-Meier and Cox regression models adjusted for clinical and imaging covariates. Results In total, 6277 patients were included (mean age of 64 years, 56% male). Median follow-up time was 3.8 years. There were 1895 patients with MFR ≤2 and 4382 with MFR >2. Patients with MFR ≤2 had significantly higher mortality than those with MFR >2 (n=701 [37.0%] vs. n=537 [12.3%], respectively; p<0.001). Annualized ACM rates by MFR and SSS ranged from 1.7 to 11.6. In multivariable analysis, MFR ≤2 was independently associated with increased ACM in the overall population (HR 2.70, 95% CI 2.41-3.03, p<0.001), even among patients with no perfusion defects (HR 2.36, 95% CI 1.93-2.89; p<0.001). Mortality risk decreased across increasing MFR deciles ranging from HR 2.73 (95% CI 2.39-3.11) to HR 0.35 (95% CI 0.25-0.49). Conclusion In this large multicenter cohort, MFR derived from 13 N-ammonia PET MPI is a strong, independent predictor of ACM, even in patients with normal perfusion. An MFR of ≤2.0 identifies elevated risk, while higher values are associated with improved survival. These findings support the routine integration of MFR to enhance risk stratification in patients with suspected or known CAD.
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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 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".