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<p>Investigate prevalence and determinants of Post-Stroke Cognitive Impairment (PSCI) to guide clinical management. Retrospective study of 792 first-time stroke patients (2020–2022) categorized into PSCI (<i>n</i> = 437) and non-PSCI (<i>n</i> = 355) groups via Mini-Mental State Examination (MMSE) scores. Variables including demographics, medical history, lesion characteristics, and clinical assessments were analyzed. LASSO regression identified significant predictors, followed by multivariate logistic regression. A nomogram model was validated using ROC, calibration, and decision curve analysis (DCA). PSCI incidence was 55.18%. Risk factors included age ≥ 60 (OR = 6.806), BMI ≥ 24 (OR = 2.69–6.53), basal ganglia/frontotemporal-parietal lesions (OR = 6.04–6.20), white matter changes (OR = 3.18), smoking (OR = 2.05), diabetes (OR = 3.42), high diastolic blood pressure variability (OR = 1.39), elevated NIHSS (OR = 1.19), and depression (OR = 1.09). Protective factors were high school (OR = 0.48) or college education (OR = 0.17) and preserved lower limb muscle strength (OR = 0.83). The nomogram achieved AUC = 0.925 (sensitivity = 88.80%, specificity = 81.70%), with robust calibration and clinical utility per DCA. Over half of stroke patients developed PSCI. Key modifiable risks (BMI, blood pressure control, diabetes management) and non-modifiable factors (lesion location, age) were identified. Education and rehabilitation targeting muscle strength may mitigate risk. The predictive model aids early high-risk patient identification, supporting tailored interventions to improve outcomes.</p>

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Predictors and clinical implications of post-stroke cognitive impairment: a retrospective study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2025年7月7日

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  • Huimin Zhou,
  • Yan Han,
  • Dan Xie,
  • Kai Zheng,
  • Zhenhe Zhou,
  • Haohao Zhu,
  • Yingying Ji

摘要

Investigate prevalence and determinants of Post-Stroke Cognitive Impairment (PSCI) to guide clinical management. Retrospective study of 792 first-time stroke patients (2020–2022) categorized into PSCI (n = 437) and non-PSCI (n = 355) groups via Mini-Mental State Examination (MMSE) scores. Variables including demographics, medical history, lesion characteristics, and clinical assessments were analyzed. LASSO regression identified significant predictors, followed by multivariate logistic regression. A nomogram model was validated using ROC, calibration, and decision curve analysis (DCA). PSCI incidence was 55.18%. Risk factors included age ≥ 60 (OR = 6.806), BMI ≥ 24 (OR = 2.69–6.53), basal ganglia/frontotemporal-parietal lesions (OR = 6.04–6.20), white matter changes (OR = 3.18), smoking (OR = 2.05), diabetes (OR = 3.42), high diastolic blood pressure variability (OR = 1.39), elevated NIHSS (OR = 1.19), and depression (OR = 1.09). Protective factors were high school (OR = 0.48) or college education (OR = 0.17) and preserved lower limb muscle strength (OR = 0.83). The nomogram achieved AUC = 0.925 (sensitivity = 88.80%, specificity = 81.70%), with robust calibration and clinical utility per DCA. Over half of stroke patients developed PSCI. Key modifiable risks (BMI, blood pressure control, diabetes management) and non-modifiable factors (lesion location, age) were identified. Education and rehabilitation targeting muscle strength may mitigate risk. The predictive model aids early high-risk patient identification, supporting tailored interventions to improve outcomes.