Impact of age, sex, and cardiac size on the diagnostic performance of myocardial perfusion single-photon emission computed tomography: insights from the REFINE SPECT registry
Bibliographic record
Abstract
Abstract Background Single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI) is a well-validated non-invasive method for detecting coronary artery disease (CAD). Variations in diagnostic performance due to age and sex have been thoroughly investigated in the literature yet have demonstrated conflicting results. Several studies have associated female sex with reduced accuracy, although others have discovered no significant difference (1). Similarly, while SPECT MPI in the elderly has shown prognostic utility, cardiac event rates are elevated compared to younger patients despite a normal study (2). Additional analyses have suggested that cardiac chamber size may contribute to these observed differences due to its relationship with spatial resolution; however, the interaction of age, sex, and cardiac size remains unknown. Purpose We aimed to leverage a large, multicenter, international registry to assess the impact of age, sex, and left ventricular size on the diagnostic accuracy of contemporary SPECT MPI. Methods In 9 centers, 2067 patients (67% male, 64.7±11.2 years) in the REFINE SPECT database (REgistry of Fast Myocardial Perfusion Imaging with NExt Generation SPECT) underwent MPI with new generation solid-state scanners followed by invasive coronary angiography within 6 months (3). Stress total perfusion deficit was quantified automatically, and obstructive CAD was defined as >70% stenosis or >50% for left main. Receiver-operating characteristic curves and corresponding areas under the curve (AUC) were computed to compare diagnostic performance between cohorts created based on age (<75 vs. ≥75 years), sex, and end-diastolic volume (EDV; ≥20th vs. <20th sex-specific percentile). Results Female and elderly patients had a significantly lower EDV than male and younger patients respectively (p<0.001, Figure 1). Diagnostic accuracy of SPECT was similar by sex (p=0.63). Elderly patients (AUC 0.72 vs. 0.78, p=0.025) and patients with reduced volumes (AUC 0.72 vs. 0.79, p=0.009) exhibited significantly worse performance. When isolating male patients with reduced volumes, a significant difference in accuracy was observed (AUC 0.69 vs. 0.79, p=0.001; Figure 2A), while female patients trended towards significance (p=0.32). Likewise, SPECT performed poorly for elderly patients with reduced volumes (AUC 0.64 vs. 0.78, p=0.01; Figure 2B). If patients possessed any two characteristics of male sex, age ≥75, or low EDV, prediction of CAD with SPECT was significantly decreased (p=0.002; Figure 2C). Conclusions Our findings suggest that men with reduced cardiac volumes display worse diagnostic SPECT performance, although it is uncertain whether a pathophysiologic reason exists or further investigation is required for female patients. Patients age ≥75 tended to have lower cardiac volumes as well as lower diagnostic performance. Given these results, alternative diagnostic modalities may better diagnose CAD in patients with these characteristics. Funding Acknowledgement Type of funding sources: None. Figure 1Figure 2
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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 | 0.004 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".