Delirium in Focus: Advances in Prediction, Prevention, Diagnosis, and Management

Delirium, an acute and fluctuating disturbance of attention and cognition, affects millions of patients worldwide annually. It is particularly prevalent among critically ill patients, the elderly, and postoperative populations and is associated with significant morbidity, prolonged hospitalization, increased healthcare costs, and long-term sequelae. Despite its prevalence and substantial clinical impact, delirium remains underrecognized and undertreated, partly due to its heterogeneous presentation and multifactorial aetiology. Recent advances in neuroimaging, biomarker research, AI-driven approaches, and digital monitoring tools, alongside the development of validated assessment scales, offer new opportunities for earlier detection and more effective prevention and treatment strategies. Concurrently, updated clinical guidelines, emerging management strategies, and multidisciplinary care models have improved awareness and standardized delirium management practices.

This Research Topic aims to offer a detailed exploration of recent progress in the prediction, prevention, diagnosis, and management of delirium. It seeks to bridge the divide between nascent scientific knowledge and practical clinical application through original research, expert insights, and broad-ranging literature reviews. A special focus will be on innovations in risk prediction models, the application of artificial intelligence and machine learning for early detection, and new approaches that encompass pharmacological, non-pharmacological, and system-level preventive interventions. The exploration will extend to advancements in diagnostic techniques, covering bedside assessment instruments, neuroimaging, and biomarker-based strategies, along with developments in treatment and post-delirium care plans. A pivotal objective is to foster multidisciplinary collaboration among healthcare professionals, reinforcing the understanding that delirium is a preventable and treatable condition when proactively managed.

To gather further insights in the multifaceted domain of delirium, we welcome articles addressing, but not limited to, the following themes:

• Innovations in risk prediction and early detection through AI and machine learning
• Comprehensive review of pharmacological, non-pharmacological, and system-level prevention
• Advancements in neuroimaging and biomarker research for delirium
• Effective models for multidisciplinary collaboration in delirium care
• Translation strategies for integrating clinical guidelines into practice

This Research Topic will accept Original Research, Review, Perspective, and Commentary articles.