To analyse the key factors influencing the recovery of lower limb motor function in stroke patients and construct a nomogram prediction model.

A retrospective analysis was conducted on the clinical data of 289 patients with limb motor dysfunction after stroke, who received rehabilitation treatment in the Department of Neurorehabilitation and Geriatric Rehabilitation of China Rehabilitation Research Center from August 2018 to August 2023. All patients were divided into the improved group (pre-post score difference>0) and the non-improved group (pre-post score difference≤0) according to the changes in the National Institutes of Health stroke scale (NIHSS) scores. Univariate analysis and multivariate Logistic regression analysis were adopted to explore the influencing factors for the recovery of limb motor function in stroke patients. Based on the results of multivariate Logistic regression analysis, a nomogram prediction model was constructed. The Hosmer-Lemeshow goodness-of-fit test was used to evaluate the model fitness. The receiver operating characteristic (ROC) curve, calibration curve and decision curve analysis (DCA) were plotted to assess the predictive performance of the model.

Of the 289 patients, 242 were enrolled in the improved group and 47 in the non-improved group. There were statistically significant differences between the two groups in age, disease course, rehabilitation frequency, as well as the proportions of smoking history, hypertension history and hyperhomocysteinemia (P<0.05). Multivariate Logistic regression analysis indicated that age (OR=8.348), disease course (OR=9.161), smoking (OR=7.192), hypertension (OR=8.314), hyperhomocysteinemia (OR=8.508), and rehabilitation frequency (OR=0.142) were independent influencing factors for the recovery of limb motor function in stroke patients (P<0.05). ROC curve analysis demonstrated that the nomogram model constructed based on the above six risk factors exhibited good predictive efficiency [AUC=0.863 (95%CI: 0.821-0.905)]. The calibration curve suggested favorable calibration of the prediction model. DCA results showed that the model yielded a high positive net clinical benefit within the threshold probability range of 10%-50%.

Age, disease course, smoking, hypertension, hyperhomocysinaemia and the rehabilitation frequency were independent influencing factors for the rehabilitation efficacy of motor dysfunction in stroke patients. The predictive model established on this basis presents good predictive performance and favorable clinical application value.