Real-world evidence study on the impact of SPECT MPI, PET MPI, cCTA and stress echocardiography on downstream healthcare utilisation in patients with coronary artery disease in the US
Bibliographic record
Abstract
BACKGROUND: Coronary artery disease (CAD) is associated with a large clinical and economic burden. However, consensus on the optimal approach to CAD diagnosis is lacking. This study sought to compare downstream healthcare resource utilisation following different cardiac imaging modalities, to inform test selection for CAD diagnosis. METHODS: Claims and electronic health records data from the Decision Resources Group Real-World Evidence US Data Repository were analysed for 2.5 million US patients who underwent single-photon emission computed tomography myocardial perfusion imaging (SPECT MPI), positron emission tomography myocardial perfusion imaging (PET MPI), coronary computed tomography angiography (cCTA), or stress echocardiography between January 2016 and March 2018. Patients were stratified into nine cohorts based on suspected or existing CAD diagnosis, pre-test risk, and prior events or interventions. Downstream healthcare utilisation, including additional diagnostic imaging, coronary angiography, and cardiac-related health system encounters, was compared by cohort and index imaging modality. RESULTS: Among patients with suspected CAD diagnosed within 3 months of the index test, PET MPI was associated with lower downstream utilisation; 25-37% of patients who underwent PET MPI required additional downstream healthcare resources compared with 40-49% of patients who received SPECT MPI, 35-41% of patients who underwent cCTA, and 44-47% of patients who received stress echocardiography. Patients who underwent PET MPI experienced fewer acute cardiac events (5.3-9.4%) and generally had lower rates of healthcare encounters (0.8-4.1%) and invasive coronary angiography (ICA, 15.4-24.2%) than those who underwent other modalities. SPECT MPI was associated with more downstream ICA (31.3-38.2%) and a higher rate of cardiac events (9.5-13.2%) compared with PET MPI (5.3-9.4%) and cCTA (6.9-9.9%). Across all cohorts, additional diagnostic imaging was 1.6 to 4.7 times more frequent with cCTA compared with PET MPI. CONCLUSION: Choice of imaging modality for CAD diagnosis impacts downstream healthcare utilisation. PET MPI was associated with lower utilisation across multiple metrics compared with other imaging modalities studied.
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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 | 0.020 | 0.153 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.004 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".