Wang Junming, Wang Pengfei, Shen Zhengyao, Liao Kehan, He Daikun, Pan Zhigang
Department of General Practice, Jinshan Hospital, Fudan University, Shanghai, China.
Center of Emergency and Critical Care Medicine, Jinshan Hospital, Fudan University, Shanghai, China.
Front Neurol. 2025 Jun 3;16:1556541. doi: 10.3389/fneur.2025.1556541. eCollection 2025.
Post-stroke dysphagia (PSD) affects up to 76% of stroke patients and increases aspiration pneumonia (AP) risk, leading to higher mortality among older survivors. Current risk assessment tools for AP in PSD patients lack precision.
We conducted a retrospective study of 7,134 stroke patients admitted to Jinshan Hospital from 2019 to 2023. We used multivariable logistic regression to identify AP predictors and constructed a nomogram model using these predictors. Model performance was evaluated using bootstrap resampling, calibration, and decision curve analysis. Internal validation was conducted on 30% of cases, and external validation was performed on 500 PSD patients from community health centers.
Among 2,663 PSD patients, 578 (21.7%) developed AP. Independent predictors included age, stroke severity, hyperlipidemia, hyperhomocysteinemia, heart failure, CRP, WBC, neutrophil ratio, Hb, FBG, prealbumin, BNP, and serum sodium. The nomogram model showed excellent discrimination (C-index: 0.885) and good agreement between predicted and observed AP probabilities. It provided net benefit across various threshold probabilities.
Our study developed the first dedicated nomogram for AP risk prediction in PSD patients, incorporating novel predictor combinations and demonstrating robust validation across multi-center cohorts. This fills an important clinical need under community conditions by enabling early identification of high-risk PSD patients using routinely available clinical variables.
卒中后吞咽困难（PSD）影响高达76%的卒中患者，并增加吸入性肺炎（AP）风险，导致老年幸存者死亡率更高。目前用于PSD患者AP风险评估的工具缺乏精准性。
我们对2019年至2023年入住金山医院的7134例卒中患者进行了一项回顾性研究。我们使用多变量逻辑回归来识别AP预测因素，并使用这些预测因素构建了一个列线图模型。使用自助重采样、校准和决策曲线分析评估模型性能。对30%的病例进行内部验证，并对来自社区卫生中心的500例PSD患者进行外部验证。
在2663例PSD患者中，578例（21.7%）发生了AP。独立预测因素包括年龄、卒中严重程度、高脂血症、高同型半胱氨酸血症、心力衰竭、CRP、白细胞、中性粒细胞比例、血红蛋白、空腹血糖、前白蛋白、脑钠肽和血清钠。列线图模型显示出优异的区分度（C指数：0.885），预测的和观察到的AP概率之间具有良好的一致性。它在各种阈值概率下都提供了净效益。
我们的研究开发了首个用于PSD患者AP风险预测的专用列线图，纳入了新的预测因素组合，并在多中心队列中展示了强大的验证。通过使用常规可用的临床变量早期识别高危PSD患者，这满足了社区条件下的一项重要临床需求。
