Key result
An optimal myocardial perfusion reserve index cutoff of ≤1.68 on CMR predicted cardiac allograft vasculopathy with 100% sensitivity, 63% specificity, and 100% negative predictive value.
Why the study?
Does perfusion cardiac magnetic resonance imaging accurately detect or rule out cardiac allograft vasculopathy compared to intravascular ultrasound in heart transplant patients?
Cross-Sectional (n=29)
Does perfusion cardiac magnetic resonance imaging accurately detect or rule out cardiac allograft vasculopathy compared to intravascular ultrasound in heart transplant patients?
Effect estimate: Sensitivity 100%, Specificity 63%, NPV 100%
Perfusion CMR with an MPR cutoff ≤1.68 provides a 100% negative predictive value for detecting cardiac allograft vasculopathy, suggesting its potential utility as a noninvasive rule-out test.
May support perfusion CMR MPR ≤1.68 as noninvasive CAV rule-out test; hypothesis-generating, requires prospective validation.
Cardiac allograft vasculopathy (CAV) is a leading cause of mortality after heart transplantation. Noninvasive imaging techniques used in CAV evaluation have important limitations. In a cross-sectional study, we investigated perfusion cardiac magnetic resonance (CMR) imaging to determine an optimal myocardial perfusion reserve index (MPR) cutoff for detecting CAV using receiver operating characteristic curve analysis. We evaluated CMR performance using sensitivity, specificity and likelihood ratio analysis. We included 29 patients (mean 5 ± 4 years after transplant) scheduled for coronary angiography with intravascular ultrasound (IVUS) who completed CMR. CAV was defined as maximal intimal thickness (MIT) >0.5 mm by IVUS of the left anterior descending artery. CAV was evident in 19 patients (70%) on IVUS (mean MIT 0.82 ± 0.42 mm). MPR was significantly lower in patients with MIT ≥0.50 mm (1.35 ± 0.23 vs. 1.71 ± 0.45, p = 0.013). There was moderate inverse correlation between MPR and MIT (r = −0.36, p = 0.075). The optimal MPR cutoff ≤1.68 for predicting CAV showed sensitivity of 100%, specificity of 63%, a negative predictive value of 100%, a positive predictive value of 86% and a positive likelihood ratio of 2.7. An MPR ≤1.68 has high negative predictive value, suggesting its potential as a test to rule out CAV. Cardiac allograft vasculopathy (CAV) is a leading cause of mortality after heart transplantation. Noninvasive imaging techniques used in CAV evaluation have important limitations. In a cross-sectional study, we investigated perfusion cardiac magnetic resonance (CMR) imaging to determine an optimal myocardial perfusion reserve index (MPR) cutoff for detecting CAV using receiver operating characteristic curve analysis. We evaluated CMR performance using sensitivity, specificity and likelihood ratio analysis. We included 29 patients (mean 5 ± 4 years after transplant) scheduled for coronary angiography with intravascular ultrasound (IVUS) who completed CMR. CAV was defined as maximal intimal thickness (MIT) >0.5 mm by IVUS of the left anterior descending artery. CAV was evident in 19 patients (70%) on IVUS (mean MIT 0.82 ± 0.42 mm). MPR was significantly lower in patients with MIT ≥0.50 mm (1.35 ± 0.23 vs. 1.71 ± 0.45, p = 0.013). There was moderate inverse correlation between MPR and MIT (r = −0.36, p = 0.075). The optimal MPR cutoff ≤1.68 for predicting CAV showed sensitivity of 100%, specificity of 63%, a negative predictive value of 100%, a positive predictive value of 86% and a positive likelihood ratio of 2.7. An MPR ≤1.68 has high negative predictive value, suggesting its potential as a test to rule out CAV.
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Chih et al. (2016) conducted a cross-sectional in Cardiac allograft vasculopathy (CAV) (n=29). Perfusion cardiac magnetic resonance (CMR) imaging vs. Intravascular ultrasound (IVUS) was evaluated on Detection of CAV (maximal intimal thickness >0.5 mm by IVUS) (Sensitivity 100%, Specificity 63%, NPV 100%). An optimal myocardial perfusion reserve index cutoff of ≤1.68 on CMR predicted cardiac allograft vasculopathy with 100% sensitivity, 63% specificity, and 100% negative predictive value.
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