What is the clinical evidence from this study?

Study design: Cohort. Population: Suspected coronary artery disease (n=206). Intervention: Automated SPECT analysis (optimized S-TPD AC) vs. Expert visual scoring. Primary outcome: Diagnostic accuracy for detecting hemodynamically significant CAD (FFR ≤ 0.80) (p=1.000).

February 22, 2018Open Access

Automated SPECT analysis compared with expert visual scoring for the detection of FFR-defined coronary artery disease

Q: What does this research mean for the field?

Optimized automated SPECT analysis achieves a diagnostic accuracy of 73.5%, which is comparable to the 73.8% accuracy of expert visual interpretation for detecting FFR-defined significant coronary artery disease. Novelty: ClaimNovelty.INCREMENTAL. Consensus alignment: ConsensusAlignment.NEUTRAL.

Key Result

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.

Study Design

Type

Cohort (n=206)

Blinding

Single-blind

Multicenter

Structured PICO

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?

Population

206 patients suspected of stable CAD with an intermediate pre-test likelihood and a normal left ventricular function, mean age 58.2, 64% male. Exclusions: documented history of CAD, signs of prior myocardial infarction, contraindication to adenosine, atrial fibrillation, glomerular filtration rate < 45 mL/min, and pregnancy.

Intervention

Automated analysis of 99mTc-tetrofosmin SPECT images (with and without attenuation correction) using commercially available software (Cedars-Sinai QPS) to calculate segmental summed stress score (SSS), summed difference score (SDS), stress total perfusion deficit (S-TPD), and ischemic total perfusion deficit (I-TPD).

Comparator

Expert visual interpretation by a highly experienced observer (>30 years of experience in nuclear cardiology) using a 17-segment model and 5-point scoring system.

Outcome

Diagnostic performance (sensitivity, specificity, and diagnostic accuracy) for the detection of hemodynamically significant CAD defined by invasive coronary angiography with fractional flow reserve (FFR) ≤0.80.surrogate

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.

Main Result

Absolute Event Rate: 73.5% vs 73.8%

p-value: p=1.000

Limitations

Relatively small study population (n=206) and small newly derived normal database
Analyses were performed with one specific scanning protocol and study population
Compared with a single expert visual reader
Diagnostic accuracy results depend on the prevalence of disease

Abstract

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.

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