Optimizing diagnostic resource use in suspected chronic coronary syndrome: pre-test probability and acoustic CAD
Louise Hougesen Bjerking1, Kim Wadt Skak-Hansen1, Merete Heitmann1
1Department of Cardiology, Copenhagen University Hospital Bispebjerg and Frederiksberg, Bispebjerg Bakke 23, Copenhagen NV 2400, Denmark.
Aims:
The FILTER-SCAD trial combined pre-test-probability (PTP) and the FDA-cleared coronary artery disease (CAD)-Score System to guide and improve deferred testing in suspected chronic coronary syndrome (CCS). However, adherence to the recommended strategy was low, with fewer than one-fourth of patients deferred. This study evaluates the potential of a PTP- and CAD-score-based deferral strategy on diagnostic resource consumption and clinical outcomes.
Methods And Results:
This observational post-hoc analysis included 1002 patients from the FILTER-SCAD intervention arm. Deferred testing was recommended for PTP ≤5% and/or CAD-score ≤20. We calculated the number of potentially deferred tests and clinical outcomes, including significant CAD and major adverse cardiac events (MACE). A deferred testing strategy could nearly double the number of safely deferred tests, reducing overall diagnostic test (n = 1043) volume by 275 tests (26%). Deferral based on PTP ≤5% alone would avoid unnecessary testing in 13.5% of patients, with an additional 31.3% deferrable based on CAD-score ≤20 (using the 2024 European Society of Cardiology risk factor-weighted likelihood increased deferral from 38.8% to 51.4%). Among the 135 low-likelihood patients (PTP ≤5%), 43.7% underwent testing, with only one (0.7%) positive test result, no invasive coronary angiographies, and no MACE. Among patients with CAD-score ≤20 tested (n = 209), 2.1% had positive test results, 1.9% were revascularized and there were two MACE. Conversely, 11.4% of the 596 higher likelihood patients (PTP >5%) were revascularized, and 20 patients experienced a MACE.
Conclusion:
A strategy incorporating PTP and CAD-score can safely reduce unnecessary testing in low-likelihood CCS patients, optimizing resource use without compromising outcomes.
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