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Identifying fallers among ophthalmic patients using classification tree methodology.

Paolo Melillo1, Ada Orrico1, Franco Chirico1

  • 1Eye Clinic, Multidisciplinary Department of Medical, Surgical and Dental Sciences, University of Campania Luigi Vanvitelli, Naples, Italy.

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Summary
This summary is machine-generated.

Ophthalmologists can now identify patients at higher risk of falling using a new tool. This method analyzes vision, lifestyle, and health data collected during eye exams to predict future falls.

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Area of Science:

  • Ophthalmology
  • Gerontology
  • Biostatistics

Background:

  • Falls are a significant concern for older adults, leading to injury and reduced independence.
  • Identifying individuals at high risk of falling is crucial for implementing preventive strategies.
  • Ophthalmologic visits present an opportunity to assess fall risk factors related to vision and overall health.

Purpose of the Study:

  • To develop and validate a predictive tool for ophthalmologists to identify patients at increased risk of falling within the next year.
  • To support routine ophthalmologic examinations by integrating fall risk assessment.
  • To leverage patient data collected during eye care visits for proactive health management.

Main Methods:

  • 141 subjects (mean age 73.2 years) underwent ophthalmic examinations and completed questionnaires on lifestyle, general health, social engagement, and vision problems.
  • Visual disability was assessed using the Activity of Daily Vision Scale (ADVS).
  • Tree-based algorithms (C4.5, AdaBoost, Random Forests) were employed to develop predictive models, with performance evaluated via cross-validation over a 12-month follow-up period.

Main Results:

  • The AdaBoost model identified patients at higher risk of falling with 69.2% sensitivity and 76.6% specificity.
  • Significant predictors of falls included pseudophakia and prescribed eyeglasses (protective factors), and recent worsening of visual acuity (risk factor).
  • Random Forest highlighted best corrected visual acuity, sleep duration, and job type as key predictive features.

Conclusions:

  • A novel method using classification trees, based on self-reported factors and health data from ophthalmologic visits, can identify patients at elevated risk of falling.
  • This approach offers a practical tool for fall risk screening within routine eye care settings.
  • Further validation on a larger cohort of visually impaired patients is warranted to confirm the tool's efficacy.