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.

Controlled donation after circulatory death (cDCD) with thoracoabdominal normothermic regional perfusion (TA-NRP) has emerged worldwide as a strategy to expand the heart donor pool. In Italy, however, its implementation is constrained by a particularly restrictive setting, shaped by the mandatory 20-min asystolic no-touch period and by the absence of cardiac surgery units in most donor hospitals. Against this backdrop, the Emilia-Romagna Regional Transplant Network developed a mobile DCD Heart Team capable of performing TA-NRP in peripheral hospitals. We report a case series of 19 cDCD donors managed with TA-NRP within a regional program based at a high-volume tertiary university hospital, all with prolonged functional warm ischemic time (FWIT). Seventeen hearts (89.5%) were successfully procured and transplanted, with a median FWIT of 41 (37.5-52) minutes, and serial transesophageal echocardiography and hemodynamic assessment supported real-time monitoring of myocardial recovery. No recipient required postcardiotomy veno-arterial extracorporeal membrane oxygenation (VA-ECMO), and no primary graft dysfunction or 30-day mortality were observed. These findings support TA-NRP heart procurement as a feasible and clinically effective procedure-even in centers without onsite cardiac surgery-when a dedicated mobile heart team is available, thereby encouraging the broad adoption of cDCD heart procurement within the Italian legislative and organizational context.