Frontiers in neurology · 2026
Development and validation of a disability risk prediction model in readmitted recurrent stroke patients.
СтатьяВалидационное исследованиеНазвание и аннотация: Перевод в очереди
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Kong Linghui, Jiang Peng, Wang Jing, Qiao Huan, Feng Tao
Аннотация
OBJECTIVE: To analyze the risk factors of disability in readmitted patients with recurrent stroke and construct a nomogram model, so as to provide references for clinical medical staff to formulate targeted intervention measures for stroke patients.
METHODS: From September 2023 to February 2025, a total of 415 readmitted patients with recurrent stroke were enrolled from the neurology departments of tertiary hospitals in Liaoning and Shanxi Provinces by convenience sampling method. Chi-square test and t-test were used for univariate analysis, and binary logistic regression analysis was performed to establish the risk prediction model. A nomogram was plotted to visualize the risk, and the model was validated via the area under the ROC curve and calibration curve.
RESULTS: The detection rate of disability among readmitted patients with recurrent stroke was 60%. Complicated hypertension, depressive symptoms, cognitive impairment (CI), occasional or no physical exercise were independent risk factors for disability (p < 0.05), while social support and self-efficacy were protective factors (p < 0.05). The AUC value was 0.993. The calibration curve was well fitted with the ideal curve, and the clinical decision curve confirmed favorable clinical application value of the model.
CONCLUSION: The constructed nomogram model presents satisfactory predictive efficiency. It can assist clinical staff in screening disability risks, and carry out targeted intervention and rehabilitation management according to relevant risk factors, so as to reduce the incidence of disability in readmitted patients with recurrent stroke.
METHODS: From September 2023 to February 2025, a total of 415 readmitted patients with recurrent stroke were enrolled from the neurology departments of tertiary hospitals in Liaoning and Shanxi Provinces by convenience sampling method. Chi-square test and t-test were used for univariate analysis, and binary logistic regression analysis was performed to establish the risk prediction model. A nomogram was plotted to visualize the risk, and the model was validated via the area under the ROC curve and calibration curve.
RESULTS: The detection rate of disability among readmitted patients with recurrent stroke was 60%. Complicated hypertension, depressive symptoms, cognitive impairment (CI), occasional or no physical exercise were independent risk factors for disability (p < 0.05), while social support and self-efficacy were protective factors (p < 0.05). The AUC value was 0.993. The calibration curve was well fitted with the ideal curve, and the clinical decision curve confirmed favorable clinical application value of the model.
CONCLUSION: The constructed nomogram model presents satisfactory predictive efficiency. It can assist clinical staff in screening disability risks, and carry out targeted intervention and rehabilitation management according to relevant risk factors, so as to reduce the incidence of disability in readmitted patients with recurrent stroke.