An Atraumatic Mock Loop for Realistic Hemocompatibility Assessment of Blood Pumps.

OBJECTIVE: Conventional mock circulatory loops (MCLs) cannot replicate realistic hemodynamic conditions without inducing blood trauma. This constrains in-vitro hemocompatibility examinations of blood pumps to static test loops that do not mimic clinical scenarios. This study aimed at developing an atraumatic MCL based on a hardware-in-the-loop concept (H-MCL) for realistic hemocompatibility assessment. METHODS: The H-MCL was designed for 450 ± 50 ml of blood with the polycarbonate reservoirs, the silicone/polyvinyl-chloride tubing, and the blood pump under investigation as the sole blood-contacting components. To account for inherent coupling effects a decoupling pressure control was derived by feedback linearization, whereas the level control was addressed by an optimization task to overcome periodic loss of controllability. The HeartMate 3 was showcased to evaluate the H-MCL's accuracy at typical hemodynamic conditions. To verify the atraumatic properties of the H-MCL, hemolysis (bovine blood, n = 6) was evaluated using the H-MCL in both inactive (static) and active (minor pulsatility) mode, and compared to results achieved in conventional loops. RESULTS: Typical hemodynamic scenarios were replicated with marginal coupling effects and root mean square error (RMSE) below 1.74 ± 1.37 mmHg while the fluid level remained within ±4% of its target value. The normalized indices of hemolysis (NIH) for the inactive H-MCL showed no significant differences to conventional loops ( ∆NIH = -1.6 mg/100 L). Further, no significant difference was evident between the active and inactive mode in the H-MCL ( ∆NIH = +0.3 mg/100 L). CONCLUSION AND SIGNIFICANCE: Collectively, these findings indicated the H-MCL's potential for in-vitro hemocompatibility assessment of blood pumps within realistic hemodynamic conditions, eliminating inherent setup-related risks for blood trauma.

Intensive care unit occupancy predictions in the COVID-19 pandemic based on age-structured modelling and differential flatness.

The COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. In many countries, hospitalization and in particular ICU occupancy is the primary measure for policy makers to decide on possible non-pharmaceutical interventions. In this paper a combined methodology for the prediction of COVID-19 case numbers, case-specific hospitalization and ICU admission rates as well as hospital and ICU occupancies is proposed. To this end, we employ differential flatness to provide estimates of the states of an epidemiological compartmental model and estimates of the unknown exogenous inputs driving its nonlinear dynamics. A main advantage of this method is that it requires the reported infection cases as the only data source. As vaccination rates and case-specific ICU rates are both strongly age-dependent, specifically an age-structured compartmental model is proposed to estimate and predict the spread of the epidemic across different age groups. By utilizing these predictions, case-specific hospitalization and case-specific ICU rates are subsequently estimated using deconvolution techniques. In an analysis of various countries we demonstrate how the methodology is able to produce real-time state estimates and hospital/ICU occupancy predictions for several weeks thus providing a sound basis for policy makers.

Estimation of exogenous drivers to predict COVID-19 pandemic using a method from nonlinear control theory.

The currently ongoing COVID-19 pandemic confronts governments and their health systems with great challenges for disease management. Epidemiological models play a crucial role, thereby assisting policymakers to predict the future course of infections and hospitalizations. One difficulty with current models is the existence of exogenous and unmeasurable variables and their significant effect on the infection dynamics. In this paper, we show how a method from nonlinear control theory can complement common compartmental epidemiological models. As a result, one can estimate and predict these exogenous variables requiring the reported infection cases as the only data source. The method allows to investigate how the estimates of exogenous variables are influenced by non-pharmaceutical interventions and how imminent epidemic waves could already be predicted at an early stage. In this way, the concept can serve as an "epidemometer" and guide the optimal timing of interventions. Analyses of the COVID-19 epidemic in various countries demonstrate the feasibility and potential of the proposed approach. The generic character of the method allows for straightforward extension to different epidemiological models.

Nonlinear identification with local model networks using GTLS techniques and equality constraints.

Local model networks approximate a nonlinear system through multiple local models fitted within a partition space. The main advantage of this approach is that the identification of complex nonlinear processes is alleviated by the integration of structured knowledge about the process. This paper extends these concepts by the integration of quantitative process knowledge into the identification procedure. Quantitative knowledge describes explicit dependences between inputs and outputs and is integrated in the parameter estimation process by means of equality constraints. For this purpose, a constrained generalized total least squares algorithm for local parameter estimation is presented. Furthermore, the problem of proper integration of constraints in the partitioning process is treated where an expectation-maximization procedure is combined with constrained parameter estimation. The benefits and the applicability of the proposed concepts are demonstrated by means of two illustrative examples and a practical application using real measurement data.

Identification of Neurofuzzy models using GTLS parameter estimation.

In this paper, nonlinear system identification utilizing generalized total least squares (GTLS) methodologies in neurofuzzy systems is addressed. The problem involved with the estimation of the local model parameters of neurofuzzy networks is the presence of noise in measured data. When some or all input channels are subject to noise, the GTLS algorithm yields consistent parameter estimates. In addition to the estimation of the parameters, the main challenge in the design of these local model networks is the determination of the region of validity for the local models. The method presented in this paper is based on an expectation-maximization algorithm that uses a residual from the GTLS parameter estimation for proper partitioning. The performance of the resulting nonlinear model with local parameters estimated by weighted GTLS is a product both of the parameter estimation itself and the associated residual used for the partitioning process. The applicability and benefits of the proposed algorithm are demonstrated by means of illustrative examples and an automotive application.