Risk Assessment of Cognitive Decline in Asymptomatic Carotid Artery Stenosis Patients: A Multifactor Model Based on Clinical Features, SPECT/CT Cerebral Perfusion Imaging Parameters, and Carotid Ultrasound Plaque Characteristics
WANG Mengdie, HAN Shuo, LIU Zhixiang, XIE Hai
2025, 20(7):
851-860.
DOI: 10.3969/j.issn.1673-5765.2025.07.007
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Objective To construct a multifactor prediction model integrating clinical features, SPECT/CT cerebral perfusion imaging parameters, and carotid ultrasound plaque characteristics for assessing the risk of cognitive decline in patients with asymptomatic carotid artery stenosis.
Methods A retrospective analysis was conducted on patients with asymptomatic carotid artery stenosis treated at the Affiliated Hospital of Shandong Second Medical University from January 1, 2021, to December 31, 2022. All patients underwent the following assessments: SPECT/CT cerebral perfusion imaging to assess the whole brain mean regional cerebral blood flow (rCBF), carotid ultrasound to assess the plaque area and echogenicity features, and the MoCA to assess cognitive function, with a 1-year follow-up to evaluate changes in cognitive function. A decline of 2 points or more in the MoCA score during the 1-year follow-up period was defined as cognitive decline, and patients were divided into the cognitive decline and the non-cognitive decline groups based on their scores. Univariate and multivariate logistic regression analyses were performed to identify independent risk factors for cognitive decline. A prediction model was constructed, and its performance was evaluated using the ROC curve.
Results A total of 80 patients were included, among whom 35 (43.75%) experienced cognitive decline during the 1-year follow-up. The differences in the percentage of hypertension history (62.9% vs. 35.6%, P=0.016), whole brain mean rCBF (0.82±0.09 vs. 0.93±0.08, P<0.001), asymmetry index (AI) [(8.2±2.1)% vs. (5.9±1.8)%, P<0.001], plaque area [(24.3±7.6) mm2 vs. (17.8±6.5) mm2, P<0.001], and the percentage of hypoechoic plaque (62.9% vs. 24.4%, P=0.012) between the cognitive decline group and the non-cognitive decline group were statistically significant. Multivariate logistic regression analysis revealed that the history of hypertension (OR 2.68, 95%CI 1.07-6.71, P=0.035), rCBF<0.85 (OR 2.79, 95%CI 1.08-7.21, P=0.034), AI>7% (OR 3.00, 95%CI 1.15-7.82, P=0.025), plaque area≥20 mm2 (OR 2.86, 95%CI 1.09-7.52, P=0.033), and the presence of hypoechoic plaque (OR 2.95, 95%CI 1.17-7.44, P=0.022) were independent predictors of cognitive decline. The ROC curve’s AUC for the risk prediction model of cognitive decline constructed based on the above five factors was 0.836 (95%CI 0.752-0.920, P<0.001), with a sensitivity of 0.725 and a specificity of 0.850. Risk factor combination analysis showed that the predictive efficacy of the four-factor model (history of hypertension, rCBF<0.85, AI>7%, and plaque area≥20 mm2) was similar to that of the five-factor model (history of hypertension, rCBF<0.85, AI>7%, plaque area≥20 mm2, and presence of hypoechoic plaque), with AUCs of 0.821 and 0.836, respectively.
Conclusions The multifactor prediction model developed in this study, integrating clinical features, SPECT/CT cerebral perfusion imaging parameters, and carotid ultrasound plaque characteristics, effectively predicts the risk of cognitive decline in patients with asymptomatic carotid artery stenosis, providing a reference for clinical risk assessment and early intervention.
