Establishment and validation of early prediction model for post-stroke dysphagia

Stroke is a leading cause of death and disability worldwide, with dysphagia being a common complication that worsens patient outcomes.

Data from 200 stroke patients (development cohort) and 50 stroke patients (validation cohort) were analyzed to develop a nomogram for predicting post-stroke dysphagia (PSD). Risk factors were identified through univariate analysis, Least Absolute Shrinkage and Selection Operator (LASSO) regression, and multivariate logistic regression.

Univariate analysis revealed substantial differences in age, body mass index (BMI), diabetes, atrial fibrillation, National Institute of Health Stroke Scale (NIHSS) score, Activities of Daily Living (ADL) score, lesion site, stroke type, and several laboratory indicators across the groups. Further analysis of individual NIHSS items showed significant differences in consciousness level, best gaze, facial palsy, motor arm, motor leg, dysarthria, etc. LASSO regression identified three predictors: ADL score, motor leg, and dysarthria. Multivariable logistic regression revealed that ADL score [0.96 (0.94-0.97)], motor leg [5.70 (2.14-15.22)], and dysarthria [5.26 (2.00-13.82)] were independent risk factors for PSD. The prediction model's AUC was 0.915 (0.874-0.955), with a sensitivity of 0.920 (0.867-0.973), specificity of 0.800 (0.722-0.878), positive predictive value (PPV) of 0.821 (0.750-0.892), negative predictive value (NPV) of 0.909 (0.849-0.969), and F1 score of 0.859. External validation yielded an AUC of 0.995 (0.984-1.000).

ADL score, motor leg, and dysarthria are independent predictors of PSD. The prediction model based on these factors shows high accuracy, sensitivity, balance, consistency, and clinical applicability. This nomogram can support decision-making for ultra-early rehabilitation care, ultimately improving patient prognosis.