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Updated: Sep 21, 2025
In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography
Published on: February 16, 2016
Prediction of multivessel coronary artery disease and candidates for stress-only imaging using multivariable models
Yuji Kunita1, Kenichi Nakajima2, Tomoaki Nakata3
1Department of Nuclear Medicine, Kanazawa University Hospital, Kanazawa, Japan.
Insights
A new model combining myocardial perfusion imaging (MPI) and patient data predicts coronary multivessel disease (MVD). This approach also identifies patients suitable for stress-only imaging, improving diagnostic efficiency.
Area of Science:
- Cardiovascular Imaging
- Nuclear Cardiology
- Medical Diagnostics
Background:
- Accurate selection of patients with coronary multivessel disease (MVD) using myocardial perfusion imaging (MPI) remains a clinical challenge.
- Distinguishing between MVD and no significant coronary stenosis requires refined diagnostic strategies.
- Identifying patients eligible for stress-only imaging can optimize resource utilization and patient experience.
Purpose of the Study:
- To develop a predictive model for MVD utilizing quantitative MPI values and patient background factors.
- To assess the feasibility of selecting patients for stress-only imaging within the same patient cohort.
Main Methods:
- Analysis of stress MPI data from 1001 patients across 12 centers and 463 non-revascularized patients in Japan.
- Quantitative MPI analysis using cardioREPO software, including perfusion defect scores and ventricular function parameters.
- Univariate and multivariate analyses to identify predictors of MVD and candidates for stress-only imaging.
Main Results:
- A logistic regression model incorporating summed stress score (SSS), rest end-diastolic volume, and hypertension achieved an AUC of 0.825 for MVD prediction.
- Predicting three-vessel disease specifically yielded an AUC of 0.847 with SSS, diabetes, and hypertension.
- A model for stress-only imaging selection, using SSS, stress end-systolic volume, and risk factors, achieved an AUC of 0.78.
Conclusions:
- A combined analysis of myocardial SPECT and clinical data effectively predicts MVD.
- The developed models can accurately identify patients suitable for stress-only diagnostic tests.
- These predictive models offer significant potential for clinical application in cardiovascular diagnostics.
Purpose:
Selecting patients with coronary multivessel disease (MVD) or no stenosis using myocardial perfusion imaging (MPI) is challenging. We aimed to create a model to predict MVD using a combination of quantitative MPI values and background factors of patients. We also assessed whether patients in the same database could be selected who do not require rest studies (stress-only imaging).
Methods:
We analyzed data from 1001 patients who had been assessed by stress MPI at 12 centers and 463 patients who had not undergone revascularization in Japan. Quantitative values based on MPI were obtained using cardioREPO software, which included myocardial perfusion defect scores, left ventricular ejection fractions and volumes. Factors in MPI and clinical backgrounds that could predict MVD were investigated using univariate and multivariate analyses. We also investigated whether stress data alone could predict patients without coronary stenosis to identify candidates for stress-only imaging.
Results:
We selected summed stress score (SSS), rest end-diastolic volume, and hypertension to create a predictive model for MVD. A logistic regression model was created with an area under the receiver operating characteristics curve (AUC) of 0.825. To more specifically predict coronary three-vessel disease, the AUC was 0.847 when SSS, diabetes, and hypertension were selected. The mean probabilities of abnormality based on the MVD prediction model were 12%, 24%, 40%, and 51% for no-, one-, two-, and three-vessel disease, respectively (p < 0.0001). For the model to select patients with stress-only imaging, the AUC was 0.78 when the model was created using SSS, stress end-systolic volume and the number of risk factors (diabetes, hypertension, chronic kidney disease, and a history of smoking).
Conclusion:
A model analysis combining myocardial SPECT and clinical data can predict MVD, and can select patients for stress-only tests. Our models should prove useful for clinical applications.
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