The frontiers of nuclear cardiology research

Factors that impact a patient's experience when undergoing single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) in the US: A survey of patients, imaging center staff, and physicians

BACKGROUND

Single-photon emission computed tomography myocardial perfusion imaging (SPECT-MPI) is commonly used for coronary artery disease diagnosis/assessment in the United States (US); however, the factors that most significantly affect patients' experience when undergoing SPECT-MPI are not well known.

METHODS

In this US-based cross-sectional study, an online questionnaire was used to identify and quantify attributes of the SPECT-MPI process that impact patients' experience, according to adults who underwent SPECT-MPI in the prior month, cardiac imaging center staff, and referring physicians. Participants were asked to rate the importance of 32 factors using an 11-point scale; congruence between groups (physicians vs patients, patients vs imaging center staff, and physicians vs imaging center staff) was assessed.

RESULTS

The survey was completed by 101 patients, 101 center staff, and 100 physicians, who gave similar ratings for the highest-rated factors (high-quality results/decreasing likelihood of having to retest, highly skilled and knowledgeable staff, and compassionate and respectful staff). Congruence was higher between patients and imaging center staff compared with physicians and patients, and was notably low between imaging center staff and physicians.

CONCLUSIONS

We identified areas for improvement in the patient SPECT-MPI experience that could translate into improved quality and value.

Quantification of myocardial blood flow by CZT-SPECT with motion correction and comparison with 15O-water PET

BACKGROUND

We compared quantification of MBF and myocardial flow reserve (MFR) with a 99mTc-sestamibi CZT-SPECT to 15O-water PET.

METHODS

SPECT MBF for thirty patients in the WATERDAY study was re-analyzed by QPET software with motion correction and optimal placement of the arterial input function. 15O-water PET MBF was re-quantified using dedicated software. Inter-operator variability was assessed using repeatability coefficients (RPC).

RESULTS

Significant correlations were observed between global (r = 0.91, P < 0.001) and regional MBF (r = 0.86, P < 0.001) with SPECT compared to PET. Global MBF (rest 0.95 vs 1.05 ml/min/g, P = 0.07; stress 2.62 vs 2.68 mL/min/g, P = 0.17) and MFR (2.65 vs 2.75, P = 0.86) were similar between SPECT and PET. Rest (0.81 vs 0.98 mL/min/g, P = 0.03) and stress MBF (1.98 vs 2.61 mL/min/g, P = 0.01) in right coronary artery (RCA) were lower with SPECT compared to PET. However, MFR in the RCA territory was similar (2.54 vs 2.77, P = 0.21). The SPECT-PET RPC for global MBFs and MFR were 0.95 mL/min/g and 0.94, with inter-observer RPC of 0.59 mL/min/g and 0.74, respectively.

CONCLUSIONS

MBF and MFR derived from CZT-SPECT with motion correction and optimal placement of the arterial input function showed good agreement with 15O-water PET, as well as low inter-operator variability.

Cardiac Phase Space Tomography: A novel method of assessing coronary artery disease utilizing machine learning

Background

Artificial intelligence (AI) techniques are increasingly applied to cardiovascular (CV) medicine in arenas ranging from genomics to cardiac imaging analysis. Cardiac Phase Space Tomography Analysis (cPSTA), employing machine-learned linear models from an elastic net method optimized by a genetic algorithm, analyzes thoracic phase signals to identify unique mathematical and tomographic features associated with the presence of flow-limiting coronary artery disease (CAD). This novel approach does not require radiation, contrast media, exercise, or pharmacological stress. The objective of this trial was to determine the diagnostic performance of cPSTA in assessing CAD in patients presenting with chest pain who had been referred by their physician for coronary angiography.

Methods

This prospective, multicenter, non-significant risk study was designed to: 1) develop machine-learned algorithms to assess the presence of CAD (defined as one or more ≥ 70% stenosis, or fractional flow reserve ≤ 0.80) and 2) test the accuracy of these algorithms prospectively in a naïve verification cohort. This report is an analysis of phase signals acquired from 606 subjects at rest just prior to angiography. From the collective phase signal data, features were extracted and paired with the known angiographic results. A development set, consisting of signals from 512 subjects, was used for machine learning to determine an algorithm that correlated with significant CAD. Verification testing of the algorithm was performed utilizing previously untested phase signals from 94 subjects.

Results

The machine-learned algorithm had a sensitivity of 92% (95% CI: 74%-100%) and specificity of 62% (95% CI: 51%-74%) on blind testing in the verification cohort. The negative predictive value (NPV) was 96% (95% CI: 85%-100%).

Conclusions

These initial multicenter results suggest that resting cPSTA may have comparable diagnostic utility to functional tests currently used to assess CAD without requiring cardiac stress (exercise or pharmacological) or exposure of the patient to radioactivity.

Correlations of SELE genetic polymorphisms with risk of coronary heart disease and myocardial infarction: a meta-analysis

This meta-analysis of case-control studies was conducted to determine whether SELE genetic polymorphisms contribute to the pathogenesis of coronary heart disease (CHD) and myocardial infarction (MI). The PubMed, CISCOM, CINAHL, Web of Science, Google Scholar, EBSCO, Cochrane Library, and CBM databases were searched for relevant articles published before November 1st, 2013 without any language restrictions. Meta-analysis was conducted using the STATA 12.0 software. Twenty case-control studies met the inclusion criteria, with a total of 2,292 CHD patients, 901 MI patients and 3,233 healthy controls. Six common polymorphisms in the SELE gene were evaluated, including 554L/F, 98G/T, 128S/R, 2692G/A, 1901C/T, and 1856A/G. The results of our meta-analysis suggest that SELE genetic polymorphisms might be strongly correlated with an increased risk of CHD (allele model: OR 2.08, 95% CI 1.67-2.58, P<0.001; dominant model: OR 2.12, 95% CI 1.68-2.68, P<0.001; respectively), especially the SELE 554L/F, 98G/T and 128S/R polymorphisms. Furthermore, our findings indicated that SELE genetic polymorphisms were closely linked to the risk of CHD in Asians but not Caucasians. However, our findings reveal no positive correlations between SELE genetic polymorphisms and MI risk (allele model: OR 1.39, 95% CI 1.00-1.94, P=0.054; dominant model: OR 1.40, 95% CI 0.96-2.04, P=0.081; respectively). The current meta-analysis suggests that SELE genetic polymorphisms may contribute to an increased risk of CHD, especially the SELE 554L/F, 98G/T and 128S/R polymorphisms in Asians. However, SELE genetic polymorphisms may not be important determinants of susceptibility to MI.