## 3 Results  
Figure 3 demonstrates the time chart of the Sputnik LVAD electric current measured at different \(CO\) levels with similar heart rates and LVAD speed levels. Different HRs and LVAD speed levels are given to highlight the change in pulsatility of electric current. LVAD speed levels correspond to the minimum, mean, and maximum values of the investigated speed range. Represented \(CO\) levels correspond to simulated states of initial LVAD support and significant myocardial recovery. A significant increase of the peak-to-peak amplitude of electric current with increasing \(CO\) can clearly be observed. At the same time, the mean electric current changes less significantly. This can be expressed as an overall rise in the electric current pulsatility. Therefore, possible prediction of recovery using the proposed \(PI\) of the electric current of the Sputnik LVAD can be supported by a visible correlation of \(CO\) to pulsatility. Figure 2: LVAD biotechnical system. \(H\), pressure head across the pump; \(Q_{1}\), aortic valve flow rate; \(Q_{2}\), pump flow rate; \(Q_{3}\), aortic flow rate to systemic circulation; \(P_{1}\), pump inlet pressure; \(P_{2}\), pump outlet pressure; \(\omega\), the rotation speed of the pump impeller; \(I\), pump electric current measured in the motor windings; \(A\), external work of the left ventricle. Red arrows indicate parameters under the study; black arrows indicate other parameters of circulation supported with LVAD. Statistical analysis of the measured data was performed to comprehensively evaluate this correlation. Tables 1 and 2 represent the statistical data of correlation and regression analyses for different regression models of \(CO\) and \(CPO\), respectively, versus pulsatility index of the Sputnik LVAD electric current, for three different heart rates. As one can see from the statistical data, regression analysis was performed for simple linear regression models and two types of quadratic models, which include and exclude the linear term. Statistical significance of the models was tested using F-test versus constant models for measured data sets. Moreover, Pearson's correlation coefficient and Spearman's rank correlation coefficient were calculated for each data set. Statistically significant and similar values of these coefficients suggest that investigated parameters are strongly correlated and can be either linear or nonlinear monotonic relationships. Analysis of regression residuals was performed for all proposed regression models. Statistical hypotheses of normality, zero autocorrelation, and homoscedasticity for regression residuals were tested to approve or disapprove adequacy of the models. In fact, residuals were normally distributed and homoscedastic, but autocorrelated for all tested models. Therefore, the only concern was to obtain models with nonautocorrelated residuals implementing correction of data sets given that normality and homoscedasticity of residuals persists. Unfortunately, this concern was not satisfiable for any linear model with reasonable correction of a data set, implying that these models are inadequate for given data sets. Thus, all linear models represented were obtained for initial data sets, which are shown in Figure 4. On the contrary, all quadratic models represented were obtained for corrected data sets with evident outliers excluded from the fit. It should be noted that corrected data sets for quadratic models of \(CPO\) were reduced more significantly (3-8 outliers excluding) than for \(CO\) models (1-3 outliers excluding). However, residuals of all shown quadratic models were found to be normally distributed, nonautocorrelated and homoscedastic implying that these models are adequate for corrected data sets at least. Figure 3: Time chart of the Sputnik LVAD electric current at similar HRs and speed levels for the two different \(CO\) levels corresponding to the simulated states of initial LVAD support and significant myocardial recovery.