Circulation Journal
Online ISSN : 1347-4820
Print ISSN : 1346-9843
ISSN-L : 1346-9843
Clinical Investigation
Application of Support Vector Machine Classifiers to Preoperative Risk Stratification With Myocardial Perfusion Scintigraphy
Tomotaka KasamatsuJun HashimotoHitoshi IyatomiTadaki NakaharaJingming BaiNaoto KitamuraKoichi OgawaAtsushi Kubo
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2008 Volume 72 Issue 11 Pages 1829-1835

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Abstract

Background Myocardial perfusion single-photon emission computed tomography (SPECT) has been used for risk stratification before non-cardiac surgery. However, few authors have used mathematical models for evaluating the likelihood of perioperative cardiac events. Methods and Results This retrospective cohort study collected data of 1,351 patients referred for SPECT before non-cardiac surgery. We generated binary classifiers using support vector machine (SVM) and conventional linear models for predicting perioperative cardiac events. We used clinical and surgical risk, and SPECT findings as input data, and the occurrence of all and hard cardiac events as output data. The area under the receiver-operating characteristic curve (AUC) was calculated for assessing the prediction accuracy. The AUC values were 0.884 and 0.748 in the SVM and linear models, respectively in predicting all cardiac events with clinical and surgical risk, and SPECT variables. The values were 0.861 (SVM) and 0.677 (linear) when not using SPECT data as input. In hard events, the AUC values were 0.892 (SVM) and 0.864 (linear) with SPECT, and 0.867 (SVM) and 0.768 (linear) without SPECT. Conclusion The SVM was superior to the linear model in risk stratification. We also found an incremental prognostic value of SPECT results over information about clinical and surgical risk. (Circ J 2008; 72: 1829 - 1835)

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© 2008 THE JAPANESE CIRCULATION SOCIETY
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