Predictive nomogram for post-stroke motor dysfunction using fNIRS.

BACKGROUND

There is a lack of objective evaluation tools for assessing upper limb motor dysfunction in ischemic stroke patients (ULMD-IS). This study aimed to develop and validate a diagnostic nomogram for diagnosing the severity of ULMD-IS using functional near-infrared spectroscopy (fNIRS) data.

METHODS

This retrospective analysis included 275 ULMD-IS patients at Tianjin Medical University General Hospital. Patients were randomly assigned to a training group (n = 193) or a validation group (n = 82). The data were preprocessed using HOMER2. In the training group, least absolute shrinkage and selection operator (LASSO) and multivariate logistic regression were employed to identify predictive variables and construct the nomogram. The nomogram's performance was validated using the area under the receiver operating characteristic curve (AUC), the Hosmer-Lemeshow goodness-of-fit test, calibration curves, and decision curve analysis (DCA).

RESULTS

No significant differences in baseline characteristics, including sex, age, lesion hemisphere, or medical history, were observed between the training and validation groups. LASSO regression analysis identified three independent risk factors: deoxyhemoglobin (HbD) levels in the affected temporal region, total hemoglobin (HbT) levels in the total region, and HbT levels in the unaffected frontopolar region. These factors showed good differentiation ability (training group AUC: 0.766, verification group AUC: 0.861). The model's goodness-of-fit was confirmed, and it demonstrated a favorable net clinical benefit. Additionally, correlation analysis between these model variables and activities of daily living (ADL) scores revealed no significant relationships (p > 0.05 for all variables), indicating that the identified risk factors may not directly influence ADL performance.

CONCLUSION

This study identified HbD in the affected temporal region, Total HbT levels, and HbT in the unaffected frontopolar region as independent risk factors for diagnosing the severity of ULMD-IS, and a corresponding predictive model was constructed. Given the model's limited sensitivity, the nomogram should be regarded only as a supplementary reference for objectively assessing post-stroke motor dysfunction; its utility in predicting treatment outcomes and guiding therapeutic choices remains modest and warrants cautious interpretation.