Background: Generalized anxiety disorder (GAD) is the least studied among anxiety disorders. Therefore, we aimed to compare the cervical blood flow velocities using doppler ultrasonography in untreated chronic GAD patients and healthy individuals.
Material And Methods: In this study, thirty-eight GAD patients were enrolled. And thirty-eight healthy volunteers were recruited as control participants. The common carotid artery (CCA), internal carotid artery (ICA), and vertebral artery (VA) of both sides were explored. Also, we trained machine learning models based on cervical arteries characteristics to diagnose GAD patients.
Results: Patients with chronic untreated GAD showed a significant increase in peak systolic velocity (PSV) bilaterally in the CCA and the ICA (P value < 0.05). In GAD patients, the end-diastolic velocity (EDV) of bilateral CCA, VA, and left ICA was significantly decreased. The Resistive Index (RI) showed a significant increase in all patients with GAD. Moreover, the Support Vector Machine (SVM) model showed the best accuracy in identifying anxiety disorder.
Conclusion: GAD is associated with hemodynamic alterations of extracranial cervical arteries. With a larger sample size and more generalized data, it is possible to make a robust machine learning-based model for GAD diagnosis.
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