BACKGROUND: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD). METHODS AND RESULTS: Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1309/2079 (63%) patients. MLS had higher area under the receiver operator characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, P < .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P < .01) and high-risk CAD, defined as stenosis of the left main, proximal left anterior descending, or triple-vessel CAD (sensitivity 96% vs 89% vs 90%, P < .01). CONCLUSIONS: The MLS is highly sensitive for prediction of both obstructive and high-risk CAD from stress-only MPI and can be applied to a stress-first protocol for automatic cancellation of unnecessary rest imaging.
BACKGROUND: Stress-only myocardial perfusion imaging (MPI) markedly reduces radiation dose, scanning time, and cost. We developed an automated clinical algorithm to safely cancel unnecessary rest imaging with high sensitivity for obstructive coronary artery disease (CAD). METHODS AND RESULTS: Patients without known CAD undergoing both MPI and invasive coronary angiography from REFINE SPECT were studied. A machine learning score (MLS) for prediction of obstructive CAD was generated using stress-only MPI and pre-test clinical variables. An MLS threshold with a pre-defined sensitivity of 95% was applied to the automated patient selection algorithm. Obstructive CAD was present in 1309/2079 (63%) patients. MLS had higher area under the receiver operator characteristic curve (AUC) for prediction of CAD than reader diagnosis and TPD (0.84 vs 0.70 vs 0.78, P < .01). An MLS threshold of 0.29 had superior sensitivity than reader diagnosis and TPD for obstructive CAD (95% vs 87% vs 87%, P < .01) and high-risk CAD, defined as stenosis of the left main, proximal left anterior descending, or triple-vessel CAD (sensitivity 96% vs 89% vs 90%, P < .01). CONCLUSIONS: The MLS is highly sensitive for prediction of both obstructive and high-risk CAD from stress-only MPI and can be applied to a stress-first protocol for automatic cancellation of unnecessary rest imaging.
Authors: D S Berman; L J Shaw; J K Min; R Hachamovitch; A Abidov; G Germano; S W Hayes; J D Friedman; L E J Thomson; X Kang; P Slomka; A Rozanski Journal: Q J Nucl Med Mol Imaging Date: 2010-04 Impact factor: 2.346
Authors: Robert J H Miller; Alan Rozanski; Piotr J Slomka; Donghee Han; Heidi Gransar; Sean W Hayes; John D Friedman; Louise E J Thomson; Daniel S Berman Journal: J Nucl Cardiol Date: 2022-04-28 Impact factor: 5.952
Authors: Attila Feher; Konrad Pieszko; Robert Miller; Mark Lemley; Aakash Shanbhag; Cathleen Huang; Leonidas Miras; Yi-Hwa Liu; Albert J Sinusas; Edward J Miller; Piotr J Slomka Journal: J Nucl Cardiol Date: 2022-10-04 Impact factor: 3.872
Authors: Waseem Hijazi; Willam Leslie; Neil Filipchuk; Ryan Choo; Stephen Wilton; Matthew James; Piotr J Slomka; Robert J H Miller Journal: J Nucl Cardiol Date: 2022-04-13 Impact factor: 3.872