ABSTRACT
Objective:To examine the long-term survival outcomes and identify mortality risk factors for coronary artery bypass grafting patients who received venoarterial extracorporeal membrane oxygenation(VA-ECMO) for postcardiotomy cardiogenic shock.Methods:Data from 121 consecutive venoarterial extracorporeal membrane oxygenation-treated coronary artery bypass grafting(CABG) patients at Beijing Anzhen Hospital between June 2012 and December 2016 were analyzed. There were 84 males and 24 females. The median age was 62(55, 67) years, and the median body mass index was 25(23, 27)kg/m 2. Thirty-seven patients(34%) had left main artery disease, and 56 patients(52%) underwent off-pump CABG. According to the 3 year survival outcome after ECMO, patients were divided into survival group(35 cases) and non-survival group(73 cases). The differences of clinical indicators between the two groups were compared and analyzed. Multivariable Cox regression modeling was used to identify factors independently associated with 36 month mortality. Results:Sixty-five patients(60%) could be weaned from VA-ECMO, 49 patients(45%) survived to hospital discharge, and 35 patients(32%) survived to 3 years. The median( IQR) time on VA-ECMO support was 4(3, 5) days. The median( IQR) length of ICU stay and hospital stay duration were 8(5, 12) and 20(13, 29) days, respectively. Older age( HR=1.06, 95% CI: 1.03-1.10, P<0.001), left main coronary artery disease( HR=1.62, 95% CI: 1.00-2.60, P=0.048), and vasoactive inotropic score( HR=1.09, 95% CI: 1.03-1.17, P=0.007)were independent risk factors associated with 3-year mortality. The area under the receiver operating characteristic curve for the model, which was constructed with age≥60years, left main coronary artery disease, and vasoactive inotropic score>60, was 0.88(95% CI: 0.80-0.95). Conclusion:Long-term survival of patients who survive to discharge appears favorable. Older age, left main coronary artery disease, and vasoactive inotropic score were associated with 3-year mortality in coronary artery bypass grafting patients who received VA-ECMO.
ABSTRACT
The purpose of using brain-computer interface (BCI) is to build a bridge between brain and computer for the disable persons, in order to help them to communicate with the outside world. Electroencephalography (EEG) has low signal to noise ratio (SNR), and there exist some problems in the traditional methods for the feature extraction of EEG, such as low classification accuracy, lack of spatial information and huge amounts of features. To solve these problems, we proposed a new method based on time domain, frequency domain and space domain. In this study, independent component analysis (ICA) and wavelet transform were used to extract the temporal, spectral and spatial features from the original EEG signals, and then the extracted features were classified with the method combined support vector machine (SVM) with genetic algorithm (GA). The proposed method displayed a better classification performance, and made the mean accuracy of the Graz datasets in the BCI Competitions of 2003 reach 96%. The classification results showed that the proposed method with the three domains could effectively overcome the drawbacks of the traditional methods based solely on time-frequency domain when the EEG signals were used to describe the characteristics of the brain electrical signals.