Combined supine and prone quantitative myocardial perfusion SPECT: method development and clinical validation in patients with no known coronary artery disease.

Acquisition in the prone position has been demonstrated to improve the specificity of visually analyzed myocardial perfusion SPECT (MPS) for detecting coronary artery disease (CAD). However, the diagnostic value of prone imaging alone or combined acquisition has not been previously described using quantitative analysis. METHODS: A total of 649 patients referred for MPS comprised the study population. Separate supine and prone normal limits were derived from 40 males and 40 females with a low likelihood (LLk) of CAD using a 3 average-deviation cutoff for all pixels on the polar map. These limits were applied to the test population of 369 consecutive patients (65% males; age, 65 +/- 13 y; 49% exercise stress) without known CAD who had diagnostic coronary angiography within 3 mo of MPS. Total perfusion deficit (TPD), defined as a product of defect extent and severity scores, was obtained for supine (S-TPD), prone (P-TPD), and combined supine-prone datasets (C-TPD). The angiographic group was randomly divided into 2 groups for deriving and validating optimal diagnostic cutoffs. Normalcy rates were validated in 2 additional groups of consecutive LLk patients: unselected patients (n = 100) and patients with body mass index >30 (n = 100). RESULTS: C-TPD had a larger area under the receiver-operating-characteristic (ROC) curve than S-TPD or P-TPD for identification of stenosis >or=70% (0.86, 0.88, and 0.90 for S-TPD, P-TPD, and C-TPD, respectively; P < 0.05). In the validation group, sensitivity for P-TPD was lower than for S- or C-TPD (P < 0.05). C-TPD yielded higher specificity than S-TPD and a trend toward higher specificity than P-TPD (65%, 83%, and 86% for S-, P-, and C-TPD, respectively, P < 0.001; vs. S-TPD and P = 0.06 vs. P-TPD). Normalcy rates for C-TPD were higher than for S-TPD in obese LLk patients (78% vs. 95%, P < 0.001). CONCLUSION: Combined supine-prone quantification significantly improves the area under the ROC curve and specificity of MPS in the identification of obstructive CAD compared with quantification of supine MPS alone.