Is There an Age When Myocardial Perfusion Imaging May No Longer Be Prognostically Useful?
Bibliographic record
Abstract
BACKGROUND: Heart disease continues to be the leading cause of death, and the prevalence of coronary artery disease is expected to increase as the population ages. It is important to understand the clinical utility of medical tests, or its lack thereof, in the aging population. The objective of this study was to understand the incremental prognostic value of positron emission tomographic (PET) myocardial perfusion imaging in the elderly (≥85 years of age). METHODS AND RESULTS: A total of 3343 patients enrolled in a multicenter observational PET registry were analyzed. Participants were initially divided into 3 age categories: 65 to 74.9, 75 to 84.9, and ≥85 years of age and followed for all-cause death. Median follow-up time was 3 years. Of the total patient population, 248 patients (49% men) were ≥85 years old. When compared with younger patients, individuals ≥85 years had a higher prevalence of hypertension (79%) and a lower incidence of dyslipidemia (54%) and diabetes mellitus (24%). On multivariable analysis, %left ventricular stress defect and %left ventricular ischemia were predictors of patient outcome for those <85 years of age but was not statistically significant in those ≥85 years of age. The prognostic value of PET (%left ventricular stress defect and %left ventricular ischemia) appeared to decrease with advancing age. CONCLUSIONS: The elderly is a high-risk population irrespective of PET myocardial perfusion imaging results, and incremental prognostic value of PET myocardial perfusion imaging appears to wane in those ≥85 years of age. Although PET myocardial perfusion imaging may be diagnostically useful in the elderly, its prognostic value in this population requires further evaluation.
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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.000 | 0.000 |
| 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".