A nomogram prediction model for stroke-associated pneumonia based on the Kubota water swallowing test
10.3760/cma.j.issn.1673-4165.2025.03.004
- VernacularTitle:基于洼田饮水试验的卒中相关性肺炎的列线图预测模型
- Author:
Tianle DI
1
;
Bing YAN
Author Information
1. 北京京煤集团总医院急诊科，北京 102300
- Keywords:
Stroke;
Ischemic stroke;
Cerebral hemorrhage;
Pneumonia;
Deglutition disorders;
Nomograms;
Risk factors
- From:
International Journal of Cerebrovascular Diseases
2025;33(3):180-185
- CountryChina
- Language:Chinese
-
Abstract:
Objective:To develop and verify a nomogram risk prediction model for stroke-associated pneumonia (SAP) based on the classification of the Kubota water swallowing test (WST) combined with multiple clinical parameters.Methods:Patients with acute stroke admitted to the emergency department, Beijing Jingmei Group General Hospital from August 2015 to March 2024 were retrospectively included. According to whether SAP occurred, the patients were divided into the SAP group and the non-SAP group. Multivariate logistic regression analysis was applied to screen the independent predictors of SAP, and a nomogram prediction model was developed accordingly. The predictive performance of the model was evaluated by the receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis. Results:A total of 298 patients with acute stroke were included, including 180 males (60.4%), aged (63.8±11.4) years. The time from onset to WST was (81±22) h. The WST classification assessment shows that grade Ⅰ-Ⅱ accounts for 60.4%, and grade Ⅲ-Ⅴ accounts for 39.6%. A total of 78 cases (26.2%) had SAP. Multivariate logistic regression analysis showed that age (odds ratio [ OR] 1.04, 95% confidence interval [ CI] 1.01-1.07; P=0.014), the baseline National Institutes of Health Stroke Scale score ( OR 1.11, 95% CI 1.05-1.18; P=0.002), the WST classification (Grade Ⅲ-Ⅴ vs. grade Ⅰ-Ⅱ: OR 2.05, 95% CI 1.30-3.22; P=0.001), neutrophil/lymphocyte ratio ( OR 1.26, 95% CI 1.11-1.43; P=0.005) and indwelling gastric tube ( OR 1.88, 95% CI 1.20-2.95; P=0.010) are independent predictors of SAP. ROC curve analysis showed that the area under the curve of SAP predicted by the nomogram model based on the above risk factors was 0.801 (95% CI 0.784-0.898; P<0.001). The model showed good calibration, and the decision curve analysis showed that the clinical net benefit was relatively high in the threshold range of 13% to 82%. Conclusion:The nomogram model based on WST has a relatively high predictive value for SAP.
