Cerebral hemodynamic characteristics of patients with auditory verbal hallucinations and the construction of nomogram models.

BACKGROUND

The characteristics of cerebral hemodynamic indexes of patients with different types of auditory verbal hallucinations (AVHs) was not clear.

AIM

To explore the characteristics of cerebral hemodynamic indexes of patients with different types of AVHs and construct the risk nomogram prediction model of patients with different types of AVHs.

METHODS

Patients with different types of verbal hallucinations who visited Wenzhou Seventh People's Hospital were retrospectively selected from March 2021 to March 2023, and these patients were classified into 117 cases of schizophrenia (SCZ) with AVHs, 108 cases of post-traumatic stress disorder (PTSD) with AVHs, and 105 cases of recurrent depressive disorder with AVHs according to type. Transcranial doppler was performed to measure the hemodynamic parameters of the anterior cerebral artery (ACA), middle cerebral artery (MCA), posterior cerebral artery (PCA), basilar artery (BA) and vertebral artery (VA). Logistic regression modelling was used to explore the factors affecting patients with different types of AVHs and odds ratio, 95% confidence interval (CI). A clinical prediction model was constructed, and the efficacy of the clinical prediction model was evaluated by using receiver operating characteristic, Hosmer-Lemeshow Goodness-of-Fit test, calibration curves and decision curve analysis.

RESULTS

The differences between the three groups of patients in mean velocity (Vm)-MCA, end-diastolic velocity (Vd)-MCA, Vm-ACA, pulsatility index (PI)-ACA, Vm-PCA, peak systolic velocity (Vs)-PCA, Vd-PCA, Vm-BA, Vs-BA, Vd-BA, PI-BA, resistance index (RI)-BA, Vm-VA, Vs-VA, Vd-VA, PI-VA, and RI-VA indexes were statistically significant. Rising Vm-ACA is an independent risk factor for SCZ with AVHs, and falling Vm-VA, Vd-MCA, and Vd-VA are independent risk factors for SCZ with AVHs. Rising Vm-ACA, Vm-PCA, Vs-PCA, Vd-PCA, Vm-BA, and Vs-BA are independent risk factors for PTSD with AVHs, and Vm-MCA, Vs-MCA, Vd-MCA, PI-PCA, and RI-BA are independent protective factors for PTSD with AVHs. Elevated Vm-MCA, Vd-MCA, RI-BA, Vm-VA, and Vd-VA were independent risk factors, and elevated Vm-ACA, Vs-ACA, Vm-PCA, Vs-PCA, and Vd-PCA were independent protective factors. The areas under the curve of the three models were 0.82 (95%CI: 0.76-0.87), 0.88 (95%CI: 0.83-0.92), and 0.81 (95%CI: 0.77-0.86), respectively; the Hosmer-Lemeshow Goodness-of-Fit test of the calibration curves of the three models suggests that > 0.05.

CONCLUSION

Monitoring the cerebral hemodynamic indexes of patients with AVHs is of practical significance in determining the type of mental disorder, which helps clinicians identify the type of AVHs and adopt more efficient treatment strategies to help patients recover.