ArticleBrain & spine2026
The C.O.S.T.A.S. project (Clinically Oriented Spasmolysis Treatment- A Scorescale): a pilot study.
Article in Brain & spine, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Introduction: Symptomatic cerebral vasospasm (CVS) following aneurysmal subarachnoid hemorrhage (SAH) presents a significant therapeutic dilemma. Intra-arterial spasmolysis is a potentially life-saving intervention, associated with procedural risks. Research question: The aim of C.O.S.T.A.S. (Clinically Oriented Spasmolysis Treatment - A Score Scale) project is to develop a clinically based, patient-oriented scoring system, to identify patients with SAH who may benefit from intra-arterial spasmolysis for CVS. Material and methods: Parameters and clinical data from SAH-patients were categorized and weighted according to their clinical relevance creating the scoring-system. Parameters included are age, smoking, intracranial arterial atherosclerosis, body-mass index, renal function, mechanical ventilation metrics, noradrenaline requirements, and an artificial intelligence (AI)-based vasospasm-risk prediction model. Each parameter was assigned a score ranging from 0 to 3 points, with a maximum cumulative score of 18. Results: The scale provides a comprehensive overview of each patient's clinical status. It integrates data from artificial intelligence-based model, along with other relevant factors, identifying patients at higher risk of developing CVS who may benefit from intra-arterial spasmolysis. In addition, it serves as a triage tool by efficiently assessing the patient's condition and determining their suitability for invasive intervention, balancing the potential risks and benefits of the procedure. Discussion and conclusion: The C.O.S.T.A.S. project is the first scoring-scale specifically designed to guide intra-arterial spasmolysis in SAH patients with CVS. While currently in prototype form, it offers a practical, transparent, and adaptable framework for individualized treatment decisions. A multicenter application, testing, and indexing are crucial for achieving maximal classification accuracy and parameter weighting.
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