Optimizing stress-only myocardial perfusion imaging: a clinical prediction model to improve patient selection
Bibliographic record
Abstract
BACKGROUND: Stress-only single photon emission computed tomography myocardial perfusion imaging (MPI) offers numerous advantages in terms of improved workflow, cost and radiation reduction but is currently not widely utilized due to challenges in selecting appropriate patients for this technique. METHODS: Data from 5959 individuals were used to derive (N = 4018) and validate (N = 1941) a binomial logistic regression model to predict normal stress MPI studies (stress total perfusion deficit < 4%, ejection fraction ≥ 50%). Model performance was analyzed using receiver operator characteristic curves. A simplified point-scoring system was developed and its impact on imaging workflow was assessed. RESULTS: Significant predictors of abnormal vs. normal stress MPI included male sex, age > 65 years, cardiomyopathy, congestive heart failure, myocardial infarction, angina, and pharmacological stress. The final model and simplified scoring system were associated with areas under the curve of 0.81 (95% CI 0.79-0.83) and 0.80 (95% CI 0.79-0.82) in the validation group, respectively. Use of the scoring system was estimated to result in a decrease of 56.5% in the number of non-contributory imaging studies acquired with minimal patient rescheduling. CONCLUSION: A prediction tool derived from simple clinical information can identify candidates for stress-only MPI studies with a beneficial impact on departmental workflow.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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".