Nomogram for Predicting Emergent Conversion to General Anaesthesia in Stroke Patients During Thrombectomy

Area of Science:

• Neurology
• Medical Imaging
• Biostatistics

Background:

• Stroke patients undergoing thrombectomy may require emergent conversion to general anaesthesia (GA).
• Predicting this conversion is crucial for optimizing patient management and resource allocation.
• Current prediction methods lack sufficient accuracy and validation.

Purpose of the Study:

• To develop and validate a nomogram for predicting emergent conversion to general anaesthesia (GA) in stroke patients during thrombectomy.
• To identify key risk factors associated with the need for GA during thrombectomy.

Main Methods:

• Retrospective analysis of 458 stroke patients undergoing thrombectomy.
• Univariable and multivariable logistic regression to identify risk factors.
• Nomogram construction and validation using ROC curve analysis, Hosmer-Lemeshow test, and decision curve analysis (DCA).

Main Results:

• Emergent conversion to GA occurred in 12.2% of patients.
• Four independent predictors identified: large infarct volume (>70 mL), severe aphasia, severe vessel tortuosity, and vertebrobasilar occlusion.
• The nomogram demonstrated high predictive accuracy (AUC 0.931 training, 0.893 validation) and clinical utility.

Conclusions:

• The developed nomogram shows promising predictive accuracy for emergent GA conversion in stroke thrombectomy patients.
• This tool can assist clinicians in decision-making regarding anaesthesia management during endovascular procedures.