Purpose: This study aims to investigate the characteristics and influencing factors of cognitive impairment in patients with asymptomatic middle cerebral artery stenosis (aMCAS) and to construct a nomogram to predict the risk of cognitive impairment in patients with aMCAS.
Patients and Methods: We collected 54 patients with aMCAS and 35 healthy controls to investigate the impaired cognitive domains and pathogenesis in patients with aMCAS. All patients underwent a cranial MRI, CT perfusion, transcranial Doppler ultrasound, blood tests, and a comprehensive neuropsychological evaluation. According to the MoCA score, patients were divided into cognitively normal and cognitively impaired groups. To construct the nomogram, we conducted univariate and multivariate logistic regression analyses to identify factors that affect cognitive function. And the performance of nomogram was evaluated by ROC curves, calibration curves, decision curve analysis (DCA), and clinical impact curve (CIC).
Results: In 54 patients with aMCAS, 24 patients presented with cognitive normal, and 30 patients presented with cognitive impairment. The results of multivariate logistic regression suggested that perfusion decompensation, middle cerebral artery mean flow velocity, and LDL-cholesterol levels were independent influencing factors of cognitive impairment. In the following step, a nomogram was constructed. The AUC of the nomogram is 0.862. Calibrating curves show good agreement between nomogram predictions and actual observations, while DCA and CIC show great clinical usefulness.
Conclusion: Patients with aMCAS have cognitive impairment in multiple cognitive domains, and impaired executive function was observed during the perfusion compensation period. Furthermore, a nomogram was constructed and validated to predict the risk of cognitive impairment in patients with aMCAS, which can help clinicians to identify at an early stage and improve the management of patients.
Keywords: cognitive impairment, asymptomatic middle cerebral artery stenosis, nomogram, predictive model