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Related Experiment Videos

A classification tree for predicting recurrent falling in community-dwelling older persons.

Vianda S Stel1, Saskia M F Pluijm, Dorly J H Deeg

  • 1Institute for Research in Extramural Medicine (EMGO Institute), VU University Medical Center, Amsterdam, The Netherlands.

Journal of the American Geriatrics Society
|September 27, 2003
PubMed
Summary

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A new classification tree predicts recurrent fall risk in older adults. Easily measurable factors like prior falls and functional limitations identify high-risk individuals for targeted prevention strategies.

Area of Science:

  • Gerontology
  • Public Health
  • Biostatistics

Background:

  • Falls are a significant public health concern among community-dwelling older persons.
  • Identifying individuals at high risk for recurrent falls is crucial for implementing effective preventive measures.
  • Existing risk assessment tools may not fully capture the complexity of fall recurrence.

Purpose of the Study:

  • To develop and validate a classification tree model for predicting the risk of recurrent falling in community-dwelling older adults.
  • To utilize tree-structured survival analysis (TSSA) for robust risk prediction.
  • To identify key predictors associated with recurrent fall risk.

Main Methods:

  • A prospective cohort study involving 1,365 community-dwelling older persons (aged 65 and above) from the Longitudinal Aging Study Amsterdam (LASA).

Related Experiment Videos

  • Assessment of physical, cognitive, emotional, and social functioning in 1995.
  • A 3-year prospective follow-up for recurrent falls (defined as two falls within 6 months).

    • Main Results:

    • The developed classification tree identified 11 distinct end groups with varying risks of recurrent falling.
    • Key predictors included prior falls, functional limitations, and dizziness.
    • Individuals with a history of two or more falls and at least two functional limitations faced a 75% risk of recurrent falling.

    Conclusions:

    • The classification tree effectively categorizes older adults into risk groups for recurrent falls using a maximum of six easily measurable predictors.
    • This tool can aid public health strategies by identifying individuals eligible for preventive interventions.
    • The model offers a practical approach to fall risk assessment in community settings.