ArticleAging clinical and experimental research2025
Establishment and validation of early prediction model for post-stroke dysphagia.
Article in Aging clinical and experimental research, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
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Who cites it
7 citing papers in PubMed.
- Machine learning for dysphagia risk prediction in older adults after acute ischemic stroke.BMC medical informatics and decision making · 2026Article
- Development and validation of a stroke risk prediction model using regional healthcare big data and machine learning.International journal of nursing sciences · 2025Article
- Clinical and Economic Impact in Dysphagia Management: A Preliminary Economic Evaluation for the WeanCare-Dysphameal Approach.Nutrients · 2025Article
- Predicting Early Dysphagia in Acute Ischemic Stroke Using an Explainable Machine Learning Model.International journal of general medicine · 2025Article
- Clinical efficacy study on the efficacy and safety of acupuncture for post-stroke dysphagia based on the regulation of the swallowing neural circuit: protocol for a multicenter, open-label, randomized controlled trial.Frontiers in medicine · 2025Article
- Dysphagia in patients with severe stroke: influencing factors and coping strategies.Frontiers in neurologyArticle
- Application of intelligent technologies for dysphagia risk prediction: A scoping review.Digital healthReview
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5 authors.
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Abstract
backgroundStroke is a leading cause of death and disability worldwide, with dysphagia being a common complication that worsens patient outcomes.
methodsData 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.
resultsUnivariate 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). CONCLUSIONS AND IMPLICATIONS: 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.
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