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Related Experiment Videos

A multivariate fall risk assessment model for VHA nursing homes using the minimum data set.

Dustin D French1, Dennis C Werner, Robert R Campbell

  • 1VISN-8 Patient Safety Center of Inquiry, James A. Haley VAMC, Tampa, FL 33612, USA. Dustin.French@va.gov

Journal of the American Medical Directors Association
|February 10, 2007
PubMed
Summary

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This study developed a new fall risk model for nursing home residents using Minimum Data Set (MDS) data. Key risk factors include unsteady gait, dementia, and certain medications, offering improved fall prediction.

Area of Science:

  • Gerontology
  • Nursing
  • Public Health

Background:

  • Falls are a significant concern in nursing home settings, leading to injury and increased healthcare costs.
  • Current fall risk assessment tools, like the fall Resident Assessment Protocol (RAP) triggers, may not capture the full spectrum of risk factors.
  • Developing a more comprehensive model is crucial for effective fall prevention strategies in long-term care.

Purpose of the Study:

  • To develop and validate a multivariate fall risk assessment model for nursing home residents.
  • To identify key risk factors beyond existing triggers using Minimum Data Set (MDS) data.
  • To provide clinicians with an empirically based tool for predicting fall risk.

Main Methods:

  • Retrospective analysis of clustered secondary data from 136 Veterans Health Administration (VHA) long-term care nursing homes.

Related Experiment Videos

  • Inclusion of 6577 VHA nursing home residents with assessments from FY 2005 and a 1-year look-back period.
  • Development of a dichotomous multivariate model using general estimation equations (GEE) to predict falls based on MDS characteristics.

    • Main Results:

    • Activities of Daily Living (ADL) dependency, unsteady gait, use of assistive devices (canes, walkers, wheelchairs), and foot problems were significantly associated with increased fall risk.
    • Cognitive impairments (Alzheimer's, other dementias), behavioral issues (anger, wandering), and the use of antipsychotic, antianxiety, or antidepressant medications also increased the odds of falling.
    • The model demonstrated the relative importance of these factors, with unsteady gait showing the highest odds ratio (OR=2.63).

    Conclusions:

    • The study successfully developed a multivariate fall risk assessment model using MDS data, confirming the importance of various risk factors.
    • The model provides clinicians with practical, empirically derived estimates for fall risk in long-term care settings.
    • This enhanced model can aid in more accurate fall risk stratification and targeted prevention efforts.