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

Rationale and Objectives: The aim of this study was to develop and validate a nomogram for predicting emergent conversion to general anaesthesia (GA) in stroke patients during thrombectomy. Methods: In this retrospective study, 458 patients (320 and 138 were randomised into the training and validation cohorts) were enroled. Univariable and multivariable logistic regression analyses were employed to identify risk factors for emergent conversion to GA. Subsequently, a nomogram was constructed based on the identified risk factors. The discriminative ability, calibration, and clinical utility of the nomogram were assessed in both the training and validation cohorts using receiver operating characteristic (ROC) curve analysis, Hosmer-Lemeshow test, and decision curve analysis (DCA). Results: The emergent conversion to GA occurred in 56 cases (12.2%). In the training cohort, four independent predictors of emergent conversion to GA were identified and incorporated into the nomogram: core infarct volume > 70 mL, severe aphasia, severe cerebral vessel tortuosity, and vertebrobasilar occlusion. The ROC curves illustrated area under curve values of 0.931 (95% CI: 0.863-0.998) and 0.893 (95% CI: 0.852-0.935) for the training and validation cohorts, respectively. Hosmer-Lemeshow testing resulted in average absolute errors of 0.028 and 0.031 for the two cohorts. DCA demonstrated the nomogram's exceptional utility and accuracy across a majority of threshold probabilities. Conclusion: The constructed nomogram displayed promising predictive accuracy for emergent conversion to GA in stroke patients during thrombectomy, thereby providing potential assistance for clinical decision-making. (c) 2024 The Association of University Radiologists. Published by Elsevier Inc. All rights are reserved, including those for text and data mining, AI training, and similar technologies.