DOI: 10.5281/zenodo.18143397
title: Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based on CHARLS data

description: I. Basic Information of the Dataset

Dataset name:Data for Development and validation of a prediction model for long-term cognitive frailty risk in stroke patients based  on CHARLS data.csv

Data source: Based on the China Health and Retirement Longitudinal Survey (CHARLS) data from 2018 to 2020

Sample size: It includes a sample of 2,325 stroke survivors

Data format: CSV (Comma-Separated Values) format, structured table data

Core application: To construct and validate a risk prediction model for long-term cognitive frailty in stroke patients

Ii. Background of Data Collection

The data is derived from the survey waves of CHARLS in 2018 and 2020, using a multi-stage scale-proportional sampling (PPS) method, covering 150 counties in 28 provinces across the country

Inclusion criteria: Community-dwelling elderly people aged 60 and above who have been diagnosed with stroke by a doctor

Exclusion criteria: No history of stroke, Alzheimer's disease/dementia, severe mental illness, severe aphasia/dysarthria, severe hearing impairment, and advanced severe systemic diseases (such as advanced heart failure, end-stage liver disease)

Iii. Variable Description (A total of 23 Variables)

1. Target variable

CF (Cognitive Frailty) : Cognitive frailty state (1 = presence of cognitive frailty, 0 = no cognitive frailty), the prevalence of cognitive frailty in the sample was 29.59% (688/2325).






Variable 



sort



Classification description





CF(Cognitive Frailty)



0



no





 



1



yes





Age



1



60~





 



2



70~





 



3



≥80





Education



1



below primary school





 



2



primary school





 



3



junior high school





 



4



high school and above





Gender



0



female





 



1



male





Marital status



0



unmarried/divorced/widowed





 



1



be married





Permanent address



0



urban





 



1



rural





Self-reported health status



0



less satisfied/average





 



1



satisfied





IADL



0



normal





 



1



impaired





Number of chronic diseases



1



0~





 



2



2~





 



3



≥4





Drinking



0



no





 



1



yes





Smoking



0



yes





 



1



no





BMI



0



≤24





 



1



≥24





Nutritional status



0



malnutrition





 



1



well-nourished





Life satisfaction



0



less satisfied/average





 



1



satisfied





Chronic pain



0



no





 



1



yes





Sleep quality



0



good





 



1



poor





Social event



0



yes





 



1



no





Intellectual activity



0



yes





 



1



no





Live alone



0



no





 



1



yes





Self-rated life satisfaction



0



less satisfied/average





 



1



satisfied





History of Falls



0



no





 



1



yes





Exercise



0



no





 



1



yes





Depressed



0



no





 



1



yes





 

Iv. Data Quality and Processing

Missing value handling: Missing values ≤30% are filled with R language mice packages (continuous variables are matched with predicted means, and categorical variables are matched with logistic regression).

Outlier handling: Evaluated and adjusted based on the clinically acceptable range

Data preprocessing: Standardization of continuous variables and individual hot encoding of categorical variables

Data segmentation: Stratified sampling was adopted to divide the data into the training set (70%, 1627 cases) and the test set (30%, 698 cases).

 

V. Data Association and Application

Key correlations: Age, educational level, nutritional status, physical exercise, and IADL are core predictors of cognitive frailty (screened by LASSO regression)

Main application: It is used for training and validating 8 types of machine learning prediction models (logistic regression, decision tree, XGBoost, etc.), among which the XGBoost model performs the best (AUC=0.810)

Scalability: Supports the analysis of risk factors for cognitive frailty, optimization of predictive models, and research on clinical intervention strategies