Scholarlink

Background <p>The long-term functional prognosis after ischemic stroke (IS) plays a crucial role in rehabilitation planning, yet it remains challenging to predict in clinical practice. Existing prognostic models primarily focus on short-term outcomes and lack integration of multidimensional determinants. Although elevated serum globulin levels have been associated with acute neuroinflammation and short-term disability, their prognostic significance for 1-year functional outcomes within a comprehensive biopsychosocial framework has not yet been established. To address these gaps, this study aimed to develop and validate a multidimensional prognostic model that integrates serum globulin as a key inflammatory biomarker into the International Classification of Functioning, Disability and Health (ICF) framework of the World Health Organization (WHO), with the objective of predicting 1-year functional outcome in patients with acute ischemic stroke (AIS).</p> Methods <p>This prospective study consecutively enrolled 1,562 AIS patients at a Grade A tertiary hospital from 2021 to 2023; after data cleaning and screening, 1,356 cases were included for analysis. Baseline data were collected within 1&#xa0;week of hospital admission. The study cohort was randomly divided into a training set (70%, <i>n</i> = 949) for model development and a validation set (30%, <i>n</i> = 407) for internal validation. The primary outcome was the patients’ functional status assessed using the modified Rankin Scale (mRS), 1-year post-admission. Predictors significant (<i>p</i> &lt; 0.05) in univariate analysis within the training set were entered into backward stepwise multivariable logistic regression. The performance of the model was comprehensively evaluated using the area under the curve (AUC), Hosmer–Lemeshow test, calibration curve, and decision curve analysis (DCA).</p> Results <p>Multivariable analysis identified six independent predictors (all <i>p</i> &lt; 0.05): age, occupational status, BI, serum globulin, number of stroke episodes and NIHSS. A nomogram incorporating these predictors demonstrated excellent discrimination in both training (AUC = 0.90, 95%&#xa0;CI 0.88–0.93) and validation sets (AUC = 0.85, 95%&#xa0;CI 0.80–0.89). Calibration was good, with predicted probabilities (training: 22.23%; validation: 21.72%) closely matching the observed incidence of 22.20%, nonsignificant Hosmer–Lemeshow test results (<i>p</i> &gt; 0.05), and well-aligned calibration curves. DCA confirmed the model’s superior net benefit over “treat-all” and “treat-none” strategies across clinically relevant high-risk thresholds (20–80%) in both training and validation cohorts.</p> Conclusions <p>This study successfully integrated serum globulin into the ICF framework and constructed a prognostic model for the 1-year prognosis after AIS. It enables early identification of high-risk individuals and personalized rehabilitation strategies to improve long-term recovery.</p>

错误：搜索内容不能为空，请输入英文关键词
错误：关键词超出字数限制，请精简
高级检索

Integrating serum globulin into the ICF framework: a novel multidimensional predictive model for 1-year mRS outcomes in acute ischemic stroke

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年2月5日

查看全文
  • Chunxun Xiao,
  • Hong Zhang,
  • Dongli Chen,
  • Yuqi Xiu,
  • Zhili Liu,
  • Yanchun Wu

摘要

Background

The long-term functional prognosis after ischemic stroke (IS) plays a crucial role in rehabilitation planning, yet it remains challenging to predict in clinical practice. Existing prognostic models primarily focus on short-term outcomes and lack integration of multidimensional determinants. Although elevated serum globulin levels have been associated with acute neuroinflammation and short-term disability, their prognostic significance for 1-year functional outcomes within a comprehensive biopsychosocial framework has not yet been established. To address these gaps, this study aimed to develop and validate a multidimensional prognostic model that integrates serum globulin as a key inflammatory biomarker into the International Classification of Functioning, Disability and Health (ICF) framework of the World Health Organization (WHO), with the objective of predicting 1-year functional outcome in patients with acute ischemic stroke (AIS).

Methods

This prospective study consecutively enrolled 1,562 AIS patients at a Grade A tertiary hospital from 2021 to 2023; after data cleaning and screening, 1,356 cases were included for analysis. Baseline data were collected within 1 week of hospital admission. The study cohort was randomly divided into a training set (70%, n = 949) for model development and a validation set (30%, n = 407) for internal validation. The primary outcome was the patients’ functional status assessed using the modified Rankin Scale (mRS), 1-year post-admission. Predictors significant (p < 0.05) in univariate analysis within the training set were entered into backward stepwise multivariable logistic regression. The performance of the model was comprehensively evaluated using the area under the curve (AUC), Hosmer–Lemeshow test, calibration curve, and decision curve analysis (DCA).

Results

Multivariable analysis identified six independent predictors (all p < 0.05): age, occupational status, BI, serum globulin, number of stroke episodes and NIHSS. A nomogram incorporating these predictors demonstrated excellent discrimination in both training (AUC = 0.90, 95% CI 0.88–0.93) and validation sets (AUC = 0.85, 95% CI 0.80–0.89). Calibration was good, with predicted probabilities (training: 22.23%; validation: 21.72%) closely matching the observed incidence of 22.20%, nonsignificant Hosmer–Lemeshow test results (p > 0.05), and well-aligned calibration curves. DCA confirmed the model’s superior net benefit over “treat-all” and “treat-none” strategies across clinically relevant high-risk thresholds (20–80%) in both training and validation cohorts.

Conclusions

This study successfully integrated serum globulin into the ICF framework and constructed a prognostic model for the 1-year prognosis after AIS. It enables early identification of high-risk individuals and personalized rehabilitation strategies to improve long-term recovery.