Machine Learning for Multi-Vessel Coronary Artery Disease Prediction on Electrocardiogram Gated Single-Photon Emission Computed Tomography.

Abstract

Background: Single-photon emission computed tomography (SPECT) encounters difficulties in diagnosing severe multi-vessel coronary artery disease (svMVD) because of balanced ischemia. We estimated the predictive value of electrocardiogram-gated SPECT for svMVD and improved it using machine learning (ML). Methods and results: We enrolled consecutive 335 patients (median age, 74 years; 255 men) who underwent adenosine stress-gated SPECT (99mTechnesium) and coronary angiography. svMVD was defined as three-vessel disease or left main tract stenosis. Predictive models were constructed using statistical and ML methods. Eighteen cases (5%) showed svMVD, and diabetes, summed stress score (SSS), and the max difference among segmental time of stroke volume per cardiac cycle (MDSV: a parameter of left ventricular [LV] end-systolic dyssynchrony) on adenosine stress were independent significant predictors. The area under the receiver operating characteristic curve (AUC) of SSS and MDSV on stress were 0.759 and 0.763, respectively. Conversely, the extra trees classifier and light gradient boosting machine had improved AUC values of 0.826 and 0.870, respectively, and the MDSV on stress and diabetes showed high feature values in the ML models. Conclusion: ML on SPECT helped to improve the diagnostic performance of svMVD and diabetes, and the parameters of LV dyssynchrony played essential roles in the ML predictive models.