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Updated: Jun 28, 2026

A Preclinical Model to Assess Brain Recovery After Acute Stroke in Rats

A Preclinical Model to Assess Brain Recovery After Acute Stroke in Rats

Published on: November 6, 2019

Development of a Risk Prediction Model for Post-Stroke Functional Recovery Based on Clinical and Nursing Factors.

Ruilin Sun¹, Zhaojun Liu¹

¹Stroke Center, Affiliated Second Clinical Hospital, Harbin Medical University, Harbin, Heilongjiang, People's Republic of China.

Risk Management and Healthcare Policy

|June 2, 2026

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This study developed a stroke recovery prediction model using clinical and nursing factors. The model accurately identifies patients at high risk for poor functional recovery, enabling targeted interventions.

Area of Science:

Neurology
Nursing Science
Public Health

Background:

Stroke is a leading cause of long-term disability.
Effective early risk stratification is crucial for optimizing patient outcomes.
Current prediction models often lack comprehensive clinical and nursing-related factors.

Purpose of the Study:

To develop and validate a risk prediction model for unfavorable functional recovery at 6 months post-stroke.
To incorporate both clinical and modifiable nursing-related predictors.
To facilitate early risk stratification and individualized nursing interventions.

Main Methods:

Retrospective cohort study of 1340 stroke patients.
Data included demographics, clinical factors, imaging, nursing, and psychosocial variables.

Keywords:

early risk identification functional recovery nursing-related predictors rehabilitation management stroke

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Last Updated: Jun 28, 2026

A Preclinical Model to Assess Brain Recovery After Acute Stroke in Rats

A Preclinical Model to Assess Brain Recovery After Acute Stroke in Rats

Published on: November 6, 2019

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Determining the Functional Status of the Corticospinal Tract Within One Week of Stroke

Published on: February 22, 2020

Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System

Cognitive Function and Upper Limb Rehabilitation Training Post-Stroke Using a Digital Occupational Training System

Published on: December 29, 2023

A nomogram-based risk prediction model was developed and validated using logistic regression and stratified sampling.

Main Results:

Older age, prior stroke, comorbidity, delayed admission, severe neurological deficits, low Glasgow Coma Scale, and brainstem lesions predicted unfavorable recovery.
Early mobilization, self-management, absence of depression, and social support were protective factors.
The model demonstrated good discrimination and calibration, with clear risk stratification.

Conclusions:

A validated risk prediction model combining clinical and nursing factors was developed.
The model shows good predictive performance for unfavorable functional recovery.
This tool can aid in early risk stratification and personalized nursing care for stroke survivors.