Towards the identification of subjects prone to develop atrial fibrillation after coronary artery bypass graft surgery via univariate and multivariate complexity analysis of heart period variability

The assessment of cardiovascular control complexity as derived from spontaneous heart period (HP) fluctuations can be improved by exploiting a multivariate (MV) approach. This work proposes the assessment of a normalized complexity index (NCI) of HP variability according to a k-nearest-neighbor approach based on local predictability performed in a MV nonuniform embedding space. The method allows the selection of the past components of HP, systolic arterial pressure (SAP) and respiration (R) most useful for the prediction of HP fluctuations. The NCI derived from the MV approach (NCIMV) was compared to a NCI computed via the same technique applied in a univariate (UV) embedding space (NCIUV) formed exclusively by HP past samples. Indexes were computed in 130 patients undergoing coronary artery bypass graft (CABG) surgery before and after the induction of general anesthesia. Thirty-eight subjects developed atrial fibrillation (AF) after surgery, while the remaining ones did not (noAF, n=92). Both NCIUV and NCIMV could separate AF from noAF patients and revealed a larger complexity of the AF subjects. However, the statistical power of the NCIMV was superior given that the probability of type I error was smaller than that of NCIUV. The assessment of cardiac control complexity could improve risk stratification of patients at risk of developing AF after CABG surgery