Precision Nursing Device Management and Artificial Intelligence-Integrated Early Warning Systems for Central Nervous...

Central nervous system (CNS) infections and clinically overlapping neuroinflammatory conditions in the neurocritical care unit (neuro-ICU) are associated with profound mortality and prolonged clinical burdens. Invasive neuromonitoring devices, particularly external ventricular drains (EVDs), significantly increase the risk of infection, while delayed clinical manifestations often hinder early recognition and intervention. This scoping review aimed to systematically map the current landscape of precision nursing device management and artificial intelligence (AI)-integrated early warning systems (EWS) for CNS infections in the neuro-ICU, rather than to definitively evaluate their clinical effectiveness. Guided by the Preferred Reporting Items for Systematic Reviews and Meta-Analyses extension for Scoping Reviews (PRISMA-ScR) framework, a systematic search of four major databases was conducted. Eligibility criteria included studies focusing on adult patients in the neuro-ICU. A rigorous multi-round filtration process was applied. A total of 24 eligible studies, encompassing longitudinal nursing management cohorts, quality improvement protocols, and machine learning predictive models, were meticulously selected for data extraction and narrative synthesis. The systematic optimization of EVD care bundles, specifically the reduction of routine cerebrospinal fluid sampling and the implementation of closed needleless systems, significantly decreased device-related infection rates, occasionally achieving zero infections under optimal interdisciplinary rounding conditions. Furthermore, AI-driven EWS algorithms (e.g., Random Forest, XGBoost {Seattle, WA: University of Washington}) and non-invasive intracranial pressure (ICP) waveform clustering demonstrated exceptional prognostic accuracy. These advanced models successfully predicted ventriculitis up to 24 h prior to positive bacterial cultures. The management of severe complications, such as paroxysmal sympathetic hyperactivity, remains heavily reliant on continuous evidence-based nursing vigilance. The integration of AI-driven predictive modeling with standardized, precision nursing bundles represents a paradigm shift from reactive treatment to proactive prevention in neurocritical care. Translating these technologies into bedside clinical decision support systems will be pivotal in optimizing individualized patient outcomes and redefining neuro-ICU nursing standards.