Reassessing Cardiovascular Risk in Patients With Peripheral Artery Disease Undergoing Myocardial Perfusion Imaging
Bibliographic record
Abstract
BACKGROUND: Coronary artery disease (CAD) and peripheral artery disease (PAD) are often regarded as analogous risk factors for major adverse cardiovascular events (MACE), given their shared pathophysiology. We aimed to investigate whether the elevated MACE risk in PAD is driven by myocardial perfusion abnormalities or through other PAD-specific mediators. METHODS: We analyzed 45,252 patients from an international, multicentre registry who underwent SPECT myocardial perfusion imaging, excluding those with early coronary revascularization (< 90 days). Myocardial perfusion abnormalities were quantified using total perfusion deficit (TPD). MACE was defined as all-cause mortality, unstable angina admission, myocardial infarction, or late coronary revascularization. PAD was defined using questionnaires or review of electronic medical records. Propensity-score matching was used to select balanced groups of patients with and without PAD. RESULTS: During a median follow-up of 3.6 years (interquartile range [IQR]: 2.6-4.8 years), 5932 patients (13.7%) experienced at least 1 MACE. Compared with patients with neither disease, isolated history of CAD (adjusted hazard ratio [aHR], 1.92; 95% confidence interval [CI], 1.80-2.05) conferred a similar MACE risk as concomitant history of CAD and PAD (aHR, 1.57; 95% CI, 1.44-1.71) and greater risk than isolated history of PAD (aHR, 1.20; 95% CI, 1.09-1.32; P < 0.001). After propensity-score matching, history of PAD alone was not independently associated with increased MACE risk (P = 0.064). CONCLUSIONS: Although patients with PAD often have concomitant CAD and greater myocardial perfusion abnormalities, PAD itself was not linked to higher risk of MACE after adjusting for these factors. These findings highlight the importance of assessing myocardial ischemic burden in PAD for risk stratification and prompt initiation of disease-modifying therapies.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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".