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Development and validation of prediction model for fall accidents among chronic kidney disease in the community

Pinli Lin1, Guang Lin2, Biyu Wan3

  • 1The Second Clinical College of Guangzhou University of Chinese Medicine, Guangzhou, China.

Frontiers in Public Health
|June 14, 2024
PubMed

Insights

Individuals with chronic kidney disease (CKD) face a high risk of falls. This study developed a validated predictive model using factors like mobility and depression to assess fall risk in community-dwelling CKD patients.

Area of Science:

  • Nephrology
  • Gerontology
  • Public Health

Background:

  • Chronic kidney disease (CKD) is associated with an elevated risk of fall accidents.
  • Falls pose a significant threat to the health and independence of individuals with CKD.
  • Community-based fall prevention strategies are crucial for this population.

Purpose of the Study:

  • To develop and validate a predictive model for fall accidents in community-dwelling individuals with CKD.
  • To identify key risk factors associated with falls in the CKD population.
  • To provide a tool for early risk assessment and intervention.

Main Methods:

  • Utilized data from the China Health and Retirement Longitudinal Study (CHARLS).
  • Employed logistic regression and LASSO regression for predictor selection.
  • Constructed a nomogram-based predictive model and validated its performance using ROC curves, calibration curves, and decision curve analysis.

Main Results:

  • Included 911 participants; 30.0% experienced fall accidents.
  • Identified fall history, BMI, mobility, handgrip, and depression as significant predictors.
  • The developed model demonstrated good predictive performance with an AUC of 0.724.

Conclusions:

  • A validated predictive model for fall risk in community-dwelling CKD patients was successfully developed.
  • The model incorporates easily assessable factors, facilitating practical application.
  • This tool can aid in identifying high-risk individuals for targeted fall prevention interventions.
Abstract

Keywords:
CHARLSchronic kidney diseasefallsnomogrampredictive model

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