Abstract 9727: Predictive Value of Machine Learning on Adenosine Stress Single Photon Emission Computed Tomography for Multivessel Coronary Artery Stenosis
| Autor: | Masato Shimizu, Hiroko Miyazaki, shummo cho, Yoshiki Misu, Ryo Tateishi, Masao Yamaguchi, Yosuke Yamakami, Hiroshi Shimada, Tomoko Manno, Ami Isshiki, Shigeki Kimura, Hiroyuki Fujii, Makoto Suzuki, Mitsuhiro Nishizaki, Tetsuo Sasano |
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| Rok vydání: | 2021 |
| Předmět: | |
| Zdroj: | Circulation. 144 |
| ISSN: | 1524-4539 0009-7322 |
| Popis: | Introduction: Adenosine stress single photon emission computed tomography (SPECT) is utilized to detect coronary artery stenosis, but the diagnostic performance is recognized not to be enough in patients with multivessel disease (MVD). Estimation of left ventricular (LV) dysfunction on electrocardiography gated SPECT including LV dissynchrony was reported to be valuable to predict MVD, but prognostic value of machine learning of those data was not studied. Hypothesis: Machine learning of gated SPECT data has high diagnostic performance for MVD. Methods: We enrolled consecutive 256 patients (78±11 years, 188 male) who underwent adenosine stress gated SPECT (99mTechnesium) and coronary angiography in one month. LV function and myocardial perfusion score were evaluated by Heart Risk View (Nihon Medi-Physics Co.,Ltd.). First, multivariate logistic regression analysis underwent with minimum Akaike’s information critetion by stepwise regression. Propensity score (PS) was calculated with the selected predictors, and receiver operation characteristics (ROC) curve analysis was performed to evaluate the predictive value. Next, we constructed prediction model for MVD by machine learning (random forest method and deep learning). Results: Thirty-five cases (14%) showed MVD. Stepwise regression with AIC showed 7 predictors as the Figure. Their accuracy and area under ROC were in 0.527-0.809, 0.599-0.689, respectively. PS of the 7 predictors showed the accuracy 0.691 and AUROC 0.758. Random forest method and deep learning built prediction models, and their accuracy and AUROC were 0.840 and 0.788, 0.846 and 0.760, respectively. The top two feature importance of random forest were SD od TES on stress and Phase SD on stress, which indicate the degree of LV dyssynchrony. Conclusions: Machine learning of adenosine stress gated STECT had powerful predictive value for MVD, and the degree of LV dyssynchrony on stress played essential role in the prediction. |
| Databáze: | OpenAIRE |
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