Abstract / Summary
Objective: This study aimed to develop and validate a nomogram model based on resting-state functional near-infrared spectroscopy (fNIRS) data to predict the severity of post-stroke dysphagia (PSD), providing a basis for individualized assessment and intervention for PSD.
Methods: This multicenter retrospective study consecutively enrolled 178 PSD patients from two hospitals between March 2023 and April 2026. Patients were classified into mild (Standardized Swallowing Assessment [SSA] score < 26) and severe (SSA ≥ 26) PSD groups. Resting-state fNIRS data were collected to calculate functional connectivity strength among 18 swallowing-related brain regions, generating 513 candidate features. In the training set, least absolute shrinkage and selection operator (LASSO) regression with 10-fold cross-validation was first used for preliminary feature screening, followed by Bootstrap resampling (B = 200) for stability selection to identify final predictors. A multivariate logistic regression model was constructed based on the selected features, and a visualized nomogram was established accordingly. Model performance was comprehensively evaluated: discriminative ability via the area under the receiver operating characteristic curve (AUC), calibration via calibration curves, and clinical utility via decision curve analysis (DCA).
Results: Three stable functional connectivity features were finally included in the model: LIPG_RIPG (interhemispheric connectivity of the inferior prefrontal gyrus), RDLPFC_RVAC (connectivity between right dorsolateral prefrontal cortex and right visual association cortex), and LFEF_LFEF (intraregional connectivity of left frontal eye field). The model demonstrated excellent discriminative performance, with AUCs of 0.928 (95% CI: 0.878-0.978) in the training set, 0.857 (95% CI: 0.727-0.986) in the internal validation set, and 0.881 (95% CI: 0.769-0.994) in the external validation set. Calibration curves showed high consistency between predicted risk and actual observation, and DCA confirmed the model yielded positive net clinical benefit across a wide threshold probability range (0.05-0.9). Sensitivity analyses further verified that the predictive value of the three fNIRS features was independent of conventional clinical variables and not affected by the choice of PSD severity cutoff.
Conclusion: The nomogram model based on fNIRS functional connectivity can effectively predict PSD severity, with sound discrimination, calibration, and clinical translational potential. It provides clinicians with a non-invasive, easy-to-use quantitative tool for early identification of high-risk PSD patients and supports evidence-based formulation of personalized rehabilitation strategies, promoting the transition of PSD management from subjective scale assessment to objective neurofunctional precision evaluation.