Investigative Ophthalmology & Visual Science Cover Image for Volume 63, Issue 7
June 2022
Volume 63, Issue 7
Open Access
ARVO Annual Meeting Abstract  |   June 2022
Predicting future falls for glaucoma patients based on retrospectively vs. prospectively-collected fall and near fall data from prior years
Author Affiliations & Notes
  • Tejasvi Kakani
    Wilmer Eye Institute/Glaucoma, Johns Hopkins University, Baltimore, Maryland, United States
  • Grace Xiao
    Wilmer Eye Institute/Glaucoma, Johns Hopkins University, Baltimore, Maryland, United States
  • Aleksandra Mihailovic
    Wilmer Eye Institute/Glaucoma, Johns Hopkins University, Baltimore, Maryland, United States
  • Pradeep Y Ramulu
    Wilmer Eye Institute/Glaucoma, Johns Hopkins University, Baltimore, Maryland, United States
  • Footnotes
    Commercial Relationships   Tejasvi Kakani None; Grace Xiao None; Aleksandra Mihailovic None; Pradeep Ramulu None
  • Footnotes
    Support  EY022976
Investigative Ophthalmology & Visual Science June 2022, Vol.63, 2458 – F0035. doi:
  • Views
  • Share
  • Tools
    • Alerts
      ×
      This feature is available to authenticated users only.
      Sign In or Create an Account ×
    • Get Citation

      Tejasvi Kakani, Grace Xiao, Aleksandra Mihailovic, Pradeep Y Ramulu; Predicting future falls for glaucoma patients based on retrospectively vs. prospectively-collected fall and near fall data from prior years. Invest. Ophthalmol. Vis. Sci. 2022;63(7):2458 – F0035.

      Download citation file:


      © ARVO (1962-2015); The Authors (2016-present)

      ×
  • Supplements
Abstract

Purpose : To investigate the association between prospectively recorded and retrospectively self-reported fall data with the likelihood of subsequent falls in a cohort of glaucoma patients.

Methods : Participants recorded falls and near falls prospectively daily over a 2-year period. In addition, at the baseline visit participants were asked if they experienced a fall in the year prior to study baseline. Based on these data participants had their faller/near faller status classified for every study year. Logistic regressions were applied to assess whether self-reported falls or prospectively-evaluated falls/near falls over the prior year were associated with falls in the subsequent year. Models controlled for age, gender, race, degree of visual filed (VF) loss, comorbidities and polypharmacy. Models also included interaction between the VF severity and faller/near faller status in the prior year to examine if prediction of a fall in the subsequent year differed by the level of VF severity.

Results : 244 glaucoma patients were included in this study. Average participant age was 71 years (SD=7.6), about half were female and third were Black. Self-report of a fall in the year preceding the study baseline did not predict falls in the first study year. Those who fell in the first year of the study were 2.4 times (Odds Ratio (OR)=2.41, p=0.003) more likely to report a fall in the second year as compared to those who did not fall in the first study year. In addition, near falls in the first year of the study were associated with higher odds of falls in both the first (concurrent) study year (OR=3.49, p<0.001) and the subsequent second year (OR=3.14, p<0.001). However, individuals with more severe VF loss were not disproportionately more likely to fall in subsequent years if they experienced fall or near falls in prior years.

Conclusions : Prospectively recorded falls and near falls for glaucoma patients are predictive of falls in subsequent years, while self-reported falls based on memory do not predict falls in the subsequent years. This highlights the importance of prospective collection of fall data to accurately judge the association of visual impairment and/or fall prevention strategies with fall rates.

This abstract was presented at the 2022 ARVO Annual Meeting, held in Denver, CO, May 1-4, 2022, and virtually.

×
×

This PDF is available to Subscribers Only

Sign in or purchase a subscription to access this content. ×

You must be signed into an individual account to use this feature.

×