Characteristics and key differences between patient populations receiving imaging modalities for coronary artery disease diagnosis in the US
Bibliographic record
Abstract
BACKGROUND: There are limited data on the impact of imaging modality selection for the assessment of coronary artery disease (CAD) risk on downstream resource utilisation. This study sought to identify differences between patient populations in the US undergoing stress echocardiography, single-photon emission computed tomography (SPECT) myocardial perfusion imaging (MPI), positron emission tomography (PET) MPI, and coronary computed tomography angiography (cCTA) for the assessment of CAD risk, and associated physician referral patterns. METHODS: Claims and electronic health records data for 2.5 million US patients who received stress echocardiography, cCTA, SPECT MPI or PET MPI between January 2016 and March 2018, from the Decision Resources Group Real-World Evidence US Data Repository, were analysed. Patients were stratified into suspected and existing CAD cohorts, and further stratified by pre-test risk and presence and recency of interventions or acute cardiac events (within 1-2 years pre-index test). Linear and logistic regression were used to compare numeric and categorical variables. RESULTS: Physicians were more likely to refer patients to standalone SPECT MPI (77%) and stress echocardiography (18%) than PET MPI (3%) and cCTA (2%). Overall, 43% of physicians referred more than 90% of their patients to standalone SPECT MPI. Just 3%, 1% and 1% of physicians referred more than 90% of their patients to stress echocardiography, PET MPI or cCTA. At the aggregated imaging level, patients who underwent stress echocardiography or cCTA had similar comorbidity profiles. Comorbidity profiles were also similar for patients who underwent SPECT MPI and PET MPI. CONCLUSION: Most patients underwent SPECT MPI at the index date, with very few undergoing PET MPI or cCTA. Patients who underwent cCTA at the index date were more likely to undergo additional imaging tests compared with those who underwent other imaging modalities. Further evidence is needed to understand factors influencing imaging test selection across patient populations.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".