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Updated: Jul 3, 2025

Electroencephalography Network Indices as Biomarkers of Upper Limb Impairment in Chronic Stroke
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Predicting post-stroke cognitive impairment using electronic health record data.

Jeffrey M Ashburner1,2, Yuchiao Chang1,2, Bianca Porneala1

  • 1Division of General Internal Medicine, Massachusetts General Hospital, Boston, Massachusetts, USA.

Medrxiv : the Preprint Server for Health Sciences
|February 14, 2024
PubMed
Summary
This summary is machine-generated.

Early identification of individuals at high risk for post-stroke cognitive impairment (PSCI) is crucial for secondary prevention. A predictive model using electronic health record data accurately identifies patients needing risk factor modification to reduce PSCI.

Keywords:
post-stroke cognitive impairmentrisk predictionrisk stratification

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Area of Science:

  • Neurology
  • Public Health
  • Data Science

Background:

  • Secondary prevention of post-stroke cognitive impairment (PSCI) requires early identification of at-risk individuals.
  • Electronic health records (EHRs) offer a rich source of data for developing predictive models.

Approach:

  • A predictive model was developed and validated using EHR data from primary care practices.
  • A cohort study design included patients aged 45+ with incident ischemic stroke.
  • Least absolute shrinkage and selection operator (LASSO) penalized Cox proportional hazards models were used for variable selection.

Key Points:

  • The model accurately predicted 5-year PSCI risk with a c-statistic of 0.731 (internal) and 0.724 (external validation).
  • A risk score stratified patients into low, intermediate, and high-risk groups.
  • High-risk individuals had a 6.2-fold (internal) and 6.1-fold (external) increased hazard for PSCI.

Conclusions:

  • Routinely collected EHR data can accurately predict 5-year PSCI risk.
  • The developed model enables risk stratification for targeted preventive interventions.
  • This approach facilitates early identification of individuals who would benefit from risk factor modification.