Citations

ABSTRACT The contemporary intensive care unit generates an overwhelming magnitude of high‐frequency, granular physiological data. However, conventional decision‐making for patients requiring temporary mechanical circulatory support (tMCS) remains tethered to reactive, threshold‐driven manual adjustments, underutilizing continuous data streams and failing to optimize the complex patient–machine interaction. This review examines the integration of deep neural networks (DNNs) as a candidate strategy for advanced clinical decision support. By identifying a continuous “digital phenotype,” hierarchical deep learning architectures signal a shift toward real‐time trajectory recognition in tMCS. We establish a four‐tier functional taxonomy—prognosticative, descriptive, predictive, and prescriptive—to categorize translational milestones from automated pump‐speed optimization to closed‐loop homeostatic regulation. Operational challenges are analyzed through the TEIP (Transparency, Explainability, Interpretability, and Performance) Core‐Lens Framework, navigating the dynamic equilibrium between computational density and clinical explainability. We further evaluate the evolving global regulatory landscape, advocating for a foundation on verified empirical safety and rigorous clinical trial validation over absolute mathematical transparency in high‐density architectures. Implementing a strategic roadmap including context‐specific validation, multimodal integration, and human‐centered design is quintessential to safely transform passive hardware into intelligent systems. As the field evolves, DNN technology offers a consequential opportunity to advance early cardiopulmonary failure recognition into a clinical reality.

Abstract Background: Extracorporeal membrane oxygenation (ECMO) therapy is proven to be efficient in a selected critically ill patient population and is showing promising results in refractory hypoxemia in the novel coronavirus disease (COVID-19). However, it necessitates specialized clinicians and advanced technological resources. While COVID-19 is an ongoing global emergency, there is no evidence-based practice in preparedness. This article proposes an innovative and optimized nursing care protocol, the Standard Operating Procedure (SOP), that increase safety, and prevents overuse of personal protective equipment (PPE) without interfering with therapy during ECMO in COVID-19 patients. Methods: A high-fidelity simulation scenario was developed. It included practicing safe and proper PPE donning and doffing during work organization, ECMO cannulation and ECMO-related procedures, and routine daily nursing care and management of patients on ECMO over nine hours. Results: Results were collected in three parts – proposal of nursing standardized operating procedures. They provide including work organization, work load references, competences and infrastructural conditions. Additionally, the cannulation equipment checklist proposal and daily routine nursing algorithm with other procedures during extracorporeal support were created.Conclusions: High-fidelity medical simulation can play an important role in staff training, improvement in previously gained proficiency, and development of optimal SOP for nursing care and management during ECMO in patients with COVID-19. Optimal SOPs may further guide multidisciplinary teams including intensive care units and interventional departments.