Stepwise clinical and diagnostic strategy for coma of unknown origin.
Authors
Affiliations (9)
Affiliations (9)
- University of Toulouse, INSERM, ToNIC, TRACER team, Toulouse, France. [email protected].
- Department of Anesthesiology, Duke University Medical Center, Durham, USA.
- School of Medicine and Surgery, University Milano- Bicocca, Milano, Italy.
- Department of Anesthesiology and Critical Care, John Hopkins University School of Medicine, Baltimore, USA.
- Brain-Body and Consciousness Laboratory, Department of Clinical Neurosciences, Lausanne University Hospital, University of Lausanne, Lausanne, Switzerland.
- Department of Medicine, University of Cambridge, Cambridge, UK.
- GIGA Neuroscience, GIGA Institute, University of Liège, Liège, Belgium.
- Neurological Intensive Care Unit, Neurology Department, Pitié-Salpêtrière Hospital, Sorbonne University, Paris, France.
- Department of Intensive Care Medicine, Université Paris Cité, INSERM U1137, IAME, APHP.Nord, Bichat-Claude Bernard University Hospital, Paris, France.
Abstract
Coma represents a critical failure of brain systems regulating arousal and awareness, posing significant diagnostic challenges when its origin is unknown. Accurate and timely diagnosis is essential to identify reversible causes and guide treatment. Here, we propose a comprehensive stepwise diagnostic algorithm integrating clinical examination, electroencephalography, neuroimaging, and laboratory investigations, emphasizing iterative reassessment to inform early decision-making. This approach, grounded in the pathophysiology of coma and current consciousness frameworks, facilitates localization of brain dysfunction and prioritizes detection of treatable etiologies. Emerging neurotechnologies, including advanced MRI and multimodal AI, hold promise for enhancing diagnosis and personalized management. Our framework aims to improve clinical outcomes by promoting systematic, physiology-based evaluation of coma of unknown origin in acute-care settings.