SPECT blood flow improves per-vessel sensitivity of myocardial perfusion imaging to detect ischemia
Bibliographic record
Abstract
26 Objectives: SPECT myocardial blood flow (MBF) imaging can be performed using modern solid state SPECT cameras. It is an additive technique to traditional relative myocardial perfusion imaging (MPI). The additional clinical benefit of MPI remains to be proven and its promise is largely based on prior PET MBF data (1,2,3). We sought to determine whether SPECT MBF would improve the sensitivity of ischemia detection by MPI. Methods: Consecutive patients undergoing clinical SPECT MPI had MBF performed. A comprehensive electronic medical record review was undertaken to determine 12-month cardiovascular follow-up. In patients who underwent anatomical downstream testing, absolute MBF was calculated for the global left ventricle and individual myocardial vessels (LAD, LCx and RCA) at rest and stress. SPECT findings (MBF and MPI) were evaluated on a per-vessel and per-patient analysis. Results: 195 patients were included with a mean age of 71 years old. Indications for SPECT MPI were chest pain (20%), previous percutaneous coronary intervention (PCI; 15%), and dyslipidemia (10%). 25 patients without prior CABG had downstream anatomical testing with invasive or CT coronary angiography. Global and per-vessel values for MBF were defined in 7 patients who had non-obstructed or normal coronaries. Per-patient and per-vessel analysis demonstrated improved sensitivity of MBF to detect stenosis ≥ 70% in comparison to MPI: per-patient MBF 85% versus 77% for MPI (p<0.01); and per-vessel, LAD analysis MBF 90% versus 60% for MPI (p<0.01). Low per vessel sensitivity of SPECT was attributed to multivessel disease in 40% of cases. Conclusions: In this small series of consecutive patients SPECT MBF was more sensitive that MPI at detecting myocardial ischemia on a per vessel and per patient analysis. These results are promising but will require further validation in a larger prospective cohort or through multicenter studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 0.001 |
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".