Development and Validation of a Nomogram Model for Predicting the Severity of Post-Stroke Dysphagia Based on...

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.

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).

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.

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.