小脳小血管病変患者における嚥下障害のリスク因子と予測モデル
Yizhen Weng1, Jie Li1,2, Wenxiu Li1
1Department of Neurology, First Affiliated Hospital of Soochow University, No. 899, Pinghai Road, Suzhou, 215006, Jiangsu, China.
Background:
The study aims to determine the association of risk factors and other major imaging markers of cerebral small vessel disease (CSVD) with post-stroke dysphagia (PSD) in patients with recent small subcortical infarct (RSSI), establish a predictive model and evaluate its predictive effectiveness.
Methods:
A total of 394 patients with RSSI were enrolled in this study, with 79 (20.05%) of them diagnosed with PSD. Swallowing function assessments, including the water-swallowing test(WST) and volume-viscosity swallow test (V-VST), were conducted within the first 24 h following admission for oral feeding. Demographic and clinical data were collected from our stroke database. Major imaging markers of CSVD were evaluated through MRI scans. Multivariate logistic regression analysis was employed to identify independent risk factors for PSD in RSSI patients. Subsequently, a nomogram involving all these independent risk factors was developed and validated by Bootstrap. Receiver Operating Characteristic (ROC) curve analysis, calibration curve analysis, and decision curve analysis (DCA) were used to assess the predictive performance of the model.
Results:
Imaging markers of cerebral small vessel disease, including cerebral microbleeds (CMBs) (OR = 3.939, 95% CI: 1.613-9.616), and moderate to severe enlargement of perivascular spaces (EPVS) (OR = 2.276, 95% CI: 1.160-4.466), were found to be significantly associated with PSD in patients with RSSI. The risk factors related to dysphagia in patients with RSSI included the following: High-sensitivity C-reactive protein (hs-CRP) (OR = 1.076, 95% CI: 1.005-1.153),baseline National Institutes of Health Stroke Scale (NIHSS) score (OR = 1.230, 95% CI: 1.132-1.336), total CSVD burden (OR = 1.613, 95% CI: 1.195-2.177) and lesion region(OR = 4.462, 95%CI: 2.333-8.532). The nomogram based on the four independent risk factors was developed and validated by Bootstrap. The model demonstrated an excellent predictive performance, with an area under the receiver operating characteristic curve (AUC) of 84.7%. The calibration curve indicated that the model's predictions closely align with actual outcomes, and DCA confirmed the model's clinical utility.
Conclusion:
CSVD imaging markers, such as CMBs and moderate to severe EPVS, are associated with PSD in RSSI patients. Key risk factors were identified, and a predictive model was developed, which could serve as an effective tool for assessing individual risk and optimizing clinical decision-making in RSSI patients.
