Optimized automated SPECT analysis achieved 73.5% diagnostic accuracy, comparable to the 73.8% accuracy of expert visual interpretation for detecting FFR-defined significant coronary artery disease.
Cohort (n=206)
Single-blind
No
Does automated SPECT analysis provide comparable diagnostic accuracy to expert visual scoring for detecting FFR-defined coronary artery disease in patients with suspected stable CAD?
Automated analysis of myocardial perfusion SPECT, when optimized with an institutional database, provides diagnostic accuracy comparable to expert visual interpretation for detecting FFR-defined CAD.
Absolute Event Rate: 73.5% vs 73.8%
p-value: p=1.000
PURPOSE: Traditionally, interpretation of myocardial perfusion imaging (MPI) is based on visual assessment. Computer-based automated analysis might be a simple alternative obviating the need for extensive reading experience. Therefore, the aim of the present study was to compare the diagnostic performance of automated analysis with that of expert visual reading for the detection of obstructive coronary artery disease (CAD). METHODS: Tc-tetrofosmin single-photon emission computed tomography (SPECT) and invasive coronary angiography with fractional flow reserve (FFR) measurements. Non-corrected (NC) and attenuation-corrected (AC) SPECT images were analyzed both visually as well as automatically by commercially available SPECT software. Automated analysis comprised a segmental summed stress score (SSS), summed difference score (SDS), stress total perfusion deficit (S-TPD), and ischemic total perfusion deficit (I-TPD), representing the extent and severity of hypoperfused myocardium. Subsequently, software was optimized with an institutional normal database and thresholds. Diagnostic performances of automated and visual analysis were compared taking FFR as a reference. RESULTS: Sensitivity did not differ significantly between visual reading and most automated scoring parameters, except for SDS, which was significantly higher than visual assessment (p < 0.001). Specificity, however, was significantly higher for visual reading than for any of the automated scores (p < 0.001 for all). Diagnostic accuracy was significantly higher for visual scoring (77.2%) than for all NC images scores (p < 0.05), but not compared with SSS AC and S-TPD AC (69.8% and 71.2%, p = 0.063 and p = 0.134). After optimization of the automated software, diagnostic accuracies were similar for visual (73.8%) and automated analysis. Among the automated parameters, S-TPD AC showed the highest accuracy (73.5%). CONCLUSION: Automated analysis of myocardial perfusion SPECT can be as accurate as visual interpretation by an expert reader in detecting significant CAD defined by FFR.
Driessen et al. (2018) conducted a cohort in Suspected coronary artery disease (n=206). Automated SPECT analysis (optimized S-TPD AC) vs. Expert visual scoring was evaluated on Diagnostic accuracy for detecting hemodynamically significant CAD (FFR ≤ 0.80) (p=1.000). Optimized automated SPECT analysis achieved 73.5% diagnostic accuracy, comparable to the 73.8% accuracy of expert visual interpretation for detecting FFR-defined significant coronary artery disease.
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