Clinical Utility of SPECT in the Heart Transplant Population: Analysis From a Single Large-volume Center
Bibliographic record
Abstract
Background. Survival after heart transplant has greatly improved, with median survival now over 12 years. Cardiac allograft vasculopathy (CAV) has become a major source of long-term morbidity and mortality. Single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI) is used for CAV surveillance, but there is limited data on its prognostic utility. Methods. We retrospectively identified patients undergoing SPECT MPI for CAV surveillance at a single, large-volume center. Images were assessed with semiquantitative visual scoring (summed stress score [SSS] and summed rest score) and quantitatively with total perfusion defect (TPD). Results. We studied 503 patients (mean age 62.5, 69.3% male) at a median of 9.0 years post-transplant. During mean follow-up of 5.1 ± 2.5 years, 114 (22.6%) patients died. The diagnostic accuracy for significant CAV (ISHLT grade 2 or 3) was highest for SSS with an area under the curve of 0.650 and stress TPD (area under the curve, 0.648), with no significant difference between SSS and stress TPD (P = 0.061). Stress TPD (adjusted hazard ratio, 1.07; P = 0.018) was independently associated with all-cause mortality, while SSS was not (P = 0.064). The prognostic accuracy of quantitative assessment of perfusion tended to be higher compared with semiquantitative assessment, with the highest accuracy for stress TPD (area under the receiver operating curve 0.584). Conclusions. While SPECT MPI identified a cohort of higher risk patients, with quantitative analysis of perfusion demonstrating higher prognostic accuracy. However, the overall prognostic accuracy was modest and alternative noninvasive modalities may be more suitable for CAV surveillance.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".