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  • Title: Diastolic filling parameters derived from myocardial perfusion imaging can predict left ventricular end-diastolic pressure at subsequent cardiac catheterization.
    Author: Patel D, Robinson VJ, Arteaga RB, Thornton JW.
    Journal: J Nucl Med; 2008 May; 49(5):746-51. PubMed ID: 18413386.
    Abstract:
    UNLABELLED: Morbidity and mortality increase when diastolic dysfunction accompanies coronary artery disease (CAD). An elevated stress (201)Tl lung-to-heart ratio (LHR) is a traditional marker of elevated left ventricular end-diastolic pressure (LVEDP), which adds prognostic value in CAD. Since the introduction of (99m)Tc-labeled agents, this valuable marker has been lost. Hence, there is only a limited ability to assess diastolic dysfunction by myocardial perfusion imaging (MPI). METHODS: Fifty-two consecutive patients with an ejection fraction of >or=45% underwent MPI and cardiac catheterization within 15 d. Peak filling rate (PFR), time to PFR (TPFR), and filling rate during the first third of diastole (1/3FR) were obtained from MPI with SPECT software. Resting (201)Tl LHR was calculated manually, and LVEDP was obtained at catheterization. RESULTS: PFR, TPFR, and 1/3FR correlated significantly with LVEDP (r= -0.53, 0.45, and -0.45, respectively; P=0.00005, 0.0009, and 0.0009, respectively), whereas resting (201)Tl LHR did not (r=0.10, P=0.49). Receiver-operating-characteristic curve analysis of PFR, TPFR, and 1/3FR for detecting LVEDPs of >or=18 mm Hg showed areas under the curve of 0.83, 0.75, and 0.80, respectively. The combination of PFR and 1/3FR showed a negative predictive value of 84%, a positive predictive value of 86%, and a specificity of 94%. CONCLUSION: Diastolic filling variables obtained with the SPECT software showed a significant correlation with LVEDP. PFR, TPFR, and 1/3FR were superior to resting (201)Tl LHR and showed good sensitivity, specificity, and predictive power for detecting LVEDPs of >or=18 mm Hg. Hence, combining data on the presence of perfusion defects with data on diastolic impairments can be achieved by adding these variables to MPI results.
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