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Objective <p>To develop and validate a multiparameter-based nomogram for predicting early neurological deterioration (END) in patients with acute ischemic stroke (AIS), integrating serum biomarkers and clinical factors.</p> Methods <p>This retrospective study enrolled 505 AIS patients. The primaryoutcome was the occurrence of END. The least absolute shrinkage and selection operator (LASSO) regression was employed for variable selection from a comprehensive set of clinical characteristics and laboratory biomarkers. Significant predictors identified were incorporated into a multivariable logistic regression to construct the predictive nomogram. Model performance was assessed by discrimination (area under the receiver operating characteristic curve, AUC), calibration (calibration curve), and clinical utility (decision curve analysis). Internal validation was performed using the bootstrap resampling method.</p> Results <p>Among the 505 AIS patients, 89 (17.6%) experienced END. Thefinal nomogram incorporated six independent predictors: non-high-density lipoprotein cholesterol (Non-HDL-C), high-sensitivity C-reactive protein (hs-CRP), fibrinogen (FIB), history of diabetes mellitus, National Institutes of Health Stroke Scale (NIHSS) score at admission, and glycated hemoglobin (HbA1c). The model demonstrated excellent discrimination, with an AUC of 0.876 (95% CI 0.835–0.917). Calibration curve and decision curve analysis confirmed satisfactory model calibration and positive net clinical benefit, respectively. Internal validation yielded a corrected C-index of 0.859, indicating robust model performance.</p> Conclusion <p>We developed and validated a multiparameter nomogram that integrates lipid, inflammatory, coagulation, and clinical parameters for the individualized prediction of END risk in AIS patients. This practical tool may assist clinicians in early risk stratification and personalized management.</p>

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Development and validation of a multiparameter nomogram for predicting early neurological deterioration in acute ischemic stroke

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月25日

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  • Song Zhang,
  • Yue Hu,
  • Weiye Wang,
  • Hanxiao Zhou,
  • Xiao Yu,
  • Guangyu Zhang

摘要

Objective

To develop and validate a multiparameter-based nomogram for predicting early neurological deterioration (END) in patients with acute ischemic stroke (AIS), integrating serum biomarkers and clinical factors.

Methods

This retrospective study enrolled 505 AIS patients. The primaryoutcome was the occurrence of END. The least absolute shrinkage and selection operator (LASSO) regression was employed for variable selection from a comprehensive set of clinical characteristics and laboratory biomarkers. Significant predictors identified were incorporated into a multivariable logistic regression to construct the predictive nomogram. Model performance was assessed by discrimination (area under the receiver operating characteristic curve, AUC), calibration (calibration curve), and clinical utility (decision curve analysis). Internal validation was performed using the bootstrap resampling method.

Results

Among the 505 AIS patients, 89 (17.6%) experienced END. Thefinal nomogram incorporated six independent predictors: non-high-density lipoprotein cholesterol (Non-HDL-C), high-sensitivity C-reactive protein (hs-CRP), fibrinogen (FIB), history of diabetes mellitus, National Institutes of Health Stroke Scale (NIHSS) score at admission, and glycated hemoglobin (HbA1c). The model demonstrated excellent discrimination, with an AUC of 0.876 (95% CI 0.835–0.917). Calibration curve and decision curve analysis confirmed satisfactory model calibration and positive net clinical benefit, respectively. Internal validation yielded a corrected C-index of 0.859, indicating robust model performance.

Conclusion

We developed and validated a multiparameter nomogram that integrates lipid, inflammatory, coagulation, and clinical parameters for the individualized prediction of END risk in AIS patients. This practical tool may assist clinicians in early risk stratification and personalized management.