Artificial Ventricle

A schematic of DTE is shown in Figure 1, illustrating application to a ventricular assist device (VAD). The optimization process is performed first in silico – in the modeling domain – followed by experimental emulation of the device specific stress loading histories (waveforms) in a Hemodynamic Shearing Device (HSD; Figure 1 – top-right) [9] (link)) where device specific effects on platelet activation are measured with a modified prothrombinase assay (Figure 1, top-left) [10] (link). The features and advantages of this assay are described in the Experimental Flow Loop and Platelet Activation Measurement section. Flow past the VAD is modeled with a high fidelity two-phase FSI (fluid-structure interaction) simulation resolving all components of the stress tensor that are relevant to flow-induced thrombogenicity and subsequently tracking down and capturing the loading history of platelets in the flow field along trajectories that may drive them beyond the activation threshold. An example of platelet trajectories in the flow field of a VAD is shown in Figure 1 (bottom-left) and stress-loading histories of these four platelet trajectories (computed with the combined effect of shear stress and exposure time [3] (link), [5] (link), [11] (link)) is shown in Figure 1 (bottom-right). Typically, several thousand of such loading histories are collapsed into a quantitative probability density function (PDF) – a thrombogenic footprint of the device [12] (link), [13] (link) that maps where the cumulative stress of each trajectory will end up in the distribution of the stress accumulation range of the device. To validate this in silico approach experimentally, extreme cases of platelet stress loading trajectories termed as hot-spot trajectories are extracted and tested in the HSD and platelet activity is quantified for these trajectories with a modified prothrombinase assay [10] (link) (Figure 1; top-left). Virtual design modifications to the original device geometry or its components can then be performed and above in silico and experimental process iterated in the virtual domain to map the least thrombogenic footprint of the redesigned device. The prototype device design is then frozen and prototypes are manufactured according to their optimized specifications. Comparative thrombogenicity experiments of the original and optimized prototypes (whole devices) are then performed in vitro to confirm that the reduction in thrombogenicity with DTE methodology was achieved in the optimized device prototype.

Details on on-site ICU physician coverage, the Tele-ICU staffing, and daily tasks of the Tele-ICU team are showed in Fig. 1. The Tele-ICU system (eCareManager® 4.1, Philips, U.S.A) used in the study supports the decision-making process by patient information centralization and real-time physiological severity evaluation based on automatic analysis (Fig. 2). The Tele-ICU staff consists of a board-certified intensivist, specially trained nurses, and a clerical assistant to the doctor. One nurse is responsible for up to 50 patients. A support center nurse is stationed 24/7. Daily Tele-ICU team tasks involve communication with on-site staff and patients using a secured audio–video system on demand and proactive survey of high risk or physiologically worsening patients to prevent unfavorable events. Venous thrombosis prophylaxis, stress ulcer prophylaxis, medication dosing appropriateness such as catecholamines, vasopressor, analgesics and sedatives, recommendation of early mobilization, early enteral feeding, and sepsis management were included in the tasks. Because the role of Tele-ICU is severity evaluation and advice in this study, the Tele-ICU physicians do not order instead of the on-site physician and only record the contents of the consultation. In addition, as the Tele-ICU physicians expertise in respiratory care and lung protective ventilation, they performed scheduled and/or on demand respiratory round. Tele-ICU physicians are given full authority of bed placement and transfer in university hospital ICU.Fig. 1

Details on on-site ICU physician coverage, the Tele-ICU staffing, and daily tasks of the Tele-ICU of Showa University Hospital

Fig. 2

Outlines of the Tele-ICU system used in the study. BGA blood gas analysis, GCS Glasgow Coma Scale, RASS Richmond agitation–sedation scale, ICDSC Intensive Care Delirium Screening Checklist, CAM–ICU Confusion Assessment Method for the ICU, NMBA neuromuscular blocking agent, ECMO extracorporeal membrane oxygenation, IABP intra-aortic balloon pumping, VAD ventricular assist device, RRT renal replacement therapy

The study was approved by the Ethics Committee of the Medical Faculty, RWTH Aachen University approval date: January 8th 2021, approval number: EK 509/20. Written patient informed consent was waived by Ethics Committee of the Medical Faculty, RWTH Aachen University because retrospective data were analyzed entirely anonymously. All research procedures were conducted according to the ethical standards of the institutional research committee and the Declaration of Helsinki. This article was created according to the Strengthening the Reporting of Observational Studies in Epidemiology (STROBE) guidelines^{23 (link)}.
To evaluate the impact of standardization of pharmacological treatment of postoperative delirium, a pre-post comparison was performed between May 2018 and June 2020. Therefore, the control group was studied from May 2018 to April 2019. In May 2019 through June 2019, the SPMD was implemented into clinical routine, and ICU staff were trained in its safe use. After this introductory period, the SPMD group was studied from July 2019 to June 2020.
This study focuses on cardiac surgery patients undergoing CPB. Thus, we included cardiac surgery patients undergoing on-pump coronary artery bypass graft (CABG) surgery, patients with on-pump valve replacement, patients with left ventricular assist device (LAVD) implantation, and patients with supracoronary aortic replacement or a combination of the before mentioned. Exclusion criteria were: patients with isolated thoracic surgery and cardiac surgery without CPB (e.g., off-pump CABG).

The occurrence of DSB is considered as an important clinical endpoint in cardiac surgery. Two definitions were used to stratify the severity in weaning from CPB and were exclusively based on the type of support used from the end of CPB until the end of the surgery^{1 (link)}. Easy separation from bypass was defined as either no support needed or only one vasoactive (norepinephrine, phenylephrine, vasopressin) or inotropic (dobutamine, milrinone, epinephrine) agent being used. Difficult separation from bypass (DSB) was defined as the requirement for at least both vasoactive and inotropic agents or also defined as ≥ 1 failure of the first weaning attempt or the requirement for an intra-aortic balloon pump or a ventricular assist device to leave the operating room. As a secondary exploratory endpoint, we explored a plausible relationship between response to inhaled milrinone (selected single point PD drivers) and DSB. Because PH was identified as one of the most important hemodynamic predictor and risk factor for DSB^{3 (link),4 (link)}, a positive response to inhaled milrinone in attempt to control PH was considered a potential predictor of DSB. Since the exploratory objective was to identify potential prognostic variables for DSB, variable selection was also based on clinical relevance that is prior knowledge of the pathophysiology related to CPB and factors susceptible to impact on its outcome. Logistic regression was carried out to identify factors independently associated with DSB. Several potential predictors were explored (EuroSCORE II, R0, Rmax, ∆Rmax-R0 and CPB duration). Simple and multiple logistic regressions were performed with stepwise selection (SigmaPlot™ Version 11.2, Systat Software Inc., San Jose, CA, USA) were used to develop a multivariate predictor of DSB.