Objective:To analyze the predictive value of the prognostic nutritional index combined with inflammatory indicators for clinical outcomes in patients with ischemic stroke, providing a basis for targeted therapeutic interventions and nursing care for patients at potential risk of poor outcomes.Methods:This retrospective cohort study recruited 424 ischemic stroke patients admitted to the Stroke Center of the First Hospital of Jilin University from June 2021 to May 2024 by the convenience sampling method. Collect the general demographic data, routine laboratory examination results within 24 hours of admission, as well as the National Institutes of Health Stroke Scale (NIHSS) scores at admission and discharge of the patients. Based on 3-month functional outcomes, patients were divided into a favorable prognosis group and a poor prognosis group. Univariate analysis, least absolute shrinkage and selection operator regression, and binary Logistic regression were employed to screen predictive variables and develop a nomogram model. Internal validation was performed using the Bootstrap method. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drew to evaluate the predictive performance of the model.Results:The favorable prognosis group comprised 256 patients, including 57 females and 199 males, with an age of 61(54, 68) years. The poor prognosis group included 168 patients, with 55 females and 113 males, and an age of 66(58, 72) years. Binary Logistic regression identified age, sex, hyperlipidemia, NIHSS score at discharge, prognostic nutritional index, systemic inflammatory response index, and systemic immune-inflammation index as independent predictors of prognosis in ischemic stroke patients ( χ2 values were 4.52-56.18, all P<0.05). The prediction model demonstrated the area under the curve of ROC of 0.806 (95% CI 0.764-0.849), with an optimal cutoff value of 0.406, achieving specificity of 78% and sensitivity of 68%. The Hosmer-Lemeshow test yielded a non-significant P>0.05, and the calibration curve showed good agreement between predicted and observed outcomes, with a mean absolute error of 0.012. Decision curve analysis confirmed the clinical utility of the model across a wide range of threshold probabilities. Conclusions:The integrated prognostic model combining the prognostic nutritional index and inflammatory indicators demonstrated favorable discriminative ability, robust calibration, and substantial clinical utility. This risk stratification tool shows high applicability for predicting ischemic stroke outcomes, facilitating early identification of high-risk patients with poor prognosis and guiding personalized intervention strategies.

Objective:To analyze the predictive value of the prognostic nutritional index combined with inflammatory indicators for clinical outcomes in patients with ischemic stroke, providing a basis for targeted therapeutic interventions and nursing care for patients at potential risk of poor outcomes.Methods:This retrospective cohort study recruited 424 ischemic stroke patients admitted to the Stroke Center of the First Hospital of Jilin University from June 2021 to May 2024 by the convenience sampling method. Collect the general demographic data, routine laboratory examination results within 24 hours of admission, as well as the National Institutes of Health Stroke Scale (NIHSS) scores at admission and discharge of the patients. Based on 3-month functional outcomes, patients were divided into a favorable prognosis group and a poor prognosis group. Univariate analysis, least absolute shrinkage and selection operator regression, and binary Logistic regression were employed to screen predictive variables and develop a nomogram model. Internal validation was performed using the Bootstrap method. The receiver operating characteristic (ROC) curve, calibration curve, and decision curve were drew to evaluate the predictive performance of the model.Results:The favorable prognosis group comprised 256 patients, including 57 females and 199 males, with an age of 61(54, 68) years. The poor prognosis group included 168 patients, with 55 females and 113 males, and an age of 66(58, 72) years. Binary Logistic regression identified age, sex, hyperlipidemia, NIHSS score at discharge, prognostic nutritional index, systemic inflammatory response index, and systemic immune-inflammation index as independent predictors of prognosis in ischemic stroke patients ( χ2 values were 4.52-56.18, all P<0.05). The prediction model demonstrated the area under the curve of ROC of 0.806 (95% CI 0.764-0.849), with an optimal cutoff value of 0.406, achieving specificity of 78% and sensitivity of 68%. The Hosmer-Lemeshow test yielded a non-significant P>0.05, and the calibration curve showed good agreement between predicted and observed outcomes, with a mean absolute error of 0.012. Decision curve analysis confirmed the clinical utility of the model across a wide range of threshold probabilities. Conclusions:The integrated prognostic model combining the prognostic nutritional index and inflammatory indicators demonstrated favorable discriminative ability, robust calibration, and substantial clinical utility. This risk stratification tool shows high applicability for predicting ischemic stroke outcomes, facilitating early identification of high-risk patients with poor prognosis and guiding personalized intervention strategies.