A clinical and biomarker model for predicting post-stroke cognitive impairment: a prospective cohort study
- 1School of Health Preservation and Rehabilitation, Chengdu University of Traditional Chinese Medicine
- 2Department of Clinical Laboratory, Shanghai Fourth People's Hospital, School of Medicine, Tongji University
- 3Department of Rehabilitation Medicine, Shanghai Fourth People's Hospital Affiliated to Tongji University
Abstract
Background and objectives: Post-stroke cognitive impairment (PSCI) is highly prevalent and substantially compromises functional recovery and quality of life; however, early identification remains challenging. This study aimed to develop and validate an early prediction model for PSCI by integrating routinely available blood biomarkers with clinical characteristics.
Methods: Patients with acute ischemic stroke were prospectively enrolled at Shanghai Fourth People's Hospital between March and November 2024. Cognitive function was assessed 3 months after stroke using the Telephone Montreal Cognitive Assessment (T-MoCA), a telephone-based cognitive screening instrument, with PSCI defined as a corrected T-MoCA score <19. Univariable logistic regression, Spearman correlation analysis, and variance inflation factor analysis were performed to characterize associations and predictor redundancy. Bootstrap least absolute shrinkage and selection operator (LASSO) logistic regression with 1,000 resampling iterations was used to assess variable-selection stability, followed by multivariable logistic regression and nomogram construction. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration analysis, decision curve analysis (DCA), and clinical impact curves (CIC), with 1,000 bootstrap iterations used for internal validation.
Results: A total of 198 patients were included, of whom 138 developed PSCI and 60 were classified as post-stroke cognitively normal (PSCN, non-PSCI group). The final prediction model incorporated age, prior stroke history, diabetes mellitus, small dense low-density lipoprotein (sdLDL), apolipoprotein A-I (ApoA-I), homocysteine, cystatin C, and D-dimer. The model achieved an apparent AUC of 0.798. Following 1,000 bootstrap resampling iterations, the mean AUC was 0.777 (95% CI, 0.733–0.802), and the optimism-corrected AUC was 0.762. Calibration analysis suggested reasonable agreement between predicted and observed risks, while DCA and CIC analyses indicated potential utility for PSCI risk stratification.
Conclusions: This study developed and internally validated a multivariable model combining clinical characteristics and circulating biomarkers to estimate PSCI risk after acute ischemic stroke. The model showed acceptable predictive performance and may support early risk stratification.
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BibTeX
@article{zhang2026clinical,
title = {{A clinical and biomarker model for predicting post-stroke cognitive impairment: a prospective cohort study}},
author = {Xinyu Zhang and Xiangwen Hao and Yanyang Li and Jingqi Chen and Xiaoyan Zheng and Anren Zhang and Qiuhong Man},
journal = {Frontiers in Neurology},
year = {2026},
month = oct,
doi = {10.3389/fneur.2026.1963734},
url = {https://www.frontiersin.org/articles/10.3389/fneur.2026.1963734/full},
note = {CC BY 4.0},
}
RIS
TY - JOUR
TI - A clinical and biomarker model for predicting post-stroke cognitive impairment: a prospective cohort study
AU - Xinyu Zhang
AU - Xiangwen Hao
AU - Yanyang Li
AU - Jingqi Chen
AU - Xiaoyan Zheng
AU - Anren Zhang
AU - Qiuhong Man
PY - 2026
DA - 2026/10/14
JO - Frontiers in Neurology
DO - 10.3389/fneur.2026.1963734
UR - https://www.frontiersin.org/articles/10.3389/fneur.2026.1963734/full
AB - Background and objectives: Post-stroke cognitive impairment (PSCI) is highly prevalent and substantially compromises functional recovery and quality of life; however, early identification remains challenging. This study aimed to develop and validate an early prediction model for PSCI by integrating routinely available blood biomarkers with clinical characteristics. Methods: Patients with acute ischemic stroke were prospectively enrolled at Shanghai Fourth People's Hospital between March and November 2024. Cognitive function was assessed 3 months after stroke using the Telephone Montreal Cognitive Assessment (T-MoCA), a telephone-based cognitive screening instrument, with PSCI defined as a corrected T-MoCA score <19. Univariable logistic regression, Spearman correlation analysis, and variance inflation factor analysis were performed to characterize associations and predictor redundancy. Bootstrap least absolute shrinkage and selection operator (LASSO) logistic regression with 1,000 resampling iterations was used to assess variable-selection stability, followed by multivariable logistic regression and nomogram construction. Model performance was evaluated using receiver operating characteristic (ROC) analysis, calibration analysis, decision curve analysis (DCA), and clinical impact curves (CIC), with 1,000 bootstrap iterations used for internal validation. Results: A total of 198 patients were included, of whom 138 developed PSCI and 60 were classified as post-stroke cognitively normal (PSCN, non-PSCI group). The final prediction model incorporated age, prior stroke history, diabetes mellitus, small dense low-density lipoprotein (sdLDL), apolipoprotein A-I (ApoA-I), homocysteine, cystatin C, and D-dimer. The model achieved an apparent AUC of 0.798. Following 1,000 bootstrap resampling iterations, the mean AUC was 0.777 (95% CI, 0.733–0.802), and the optimism-corrected AUC was 0.762. Calibration analysis suggested reasonable agreement between predicted and observed risks, while DCA and CIC analyses indicated potential utility for PSCI risk stratification. Conclusions: This study developed and internally validated a multivariable model combining clinical characteristics and circulating biomarkers to estimate PSCI risk after acute ischemic stroke. The model showed acceptable predictive performance and may support early risk stratification.
SN - 1664-2295
ER -