Construction and verification of a nomogram model for predicting the risk of post-stroke spasticity: a retrospective study.

RESULTS: LASSO-logistic regression analysis identified seven predictors associated with PSS: C-reactive protein, albumin, creatine kinase, fasting blood glucose, hyperlipidemia, sleep disorders, and manual muscle testing (MMT) score at admission. The model had an area under the curve (AUC) of 0.844 (95% CI: 0.793-0.896) in the training set and 0.842 (95% CI: 0.765-0.920) in the validation set, which means it was good at making predictions. The calibration curves showed excellent agreement between predicted and observed probabilities in the training set. Good calibration was maintained in the validation set, indicating only minimal overestimation of risk. DCA and CIC both agreed that the nomogram model could be used in a wide range of therapeutic situations.

CONCLUSION: The nomogram based on routine clinical data in this study, after internal validation, can effectively predict the risk of PSS and provides a practical decision-making tool for clinicians. However, future multi-centre external validation is still required to confirm its broad applicability.