Development and internal validation of a dynamic fall risk prediction and monitoring tool in aged care using routinely collected electronic health data: a landmarking approach

Nasir Wabe, Isabelle Meulenbroeks*, Guogui Huang, Sandun Malpriya Silva, Leonard C. Gray, Jacqueline C. T. Close, Stephen R. Lord, Johanna I. Westbrook

*Corresponding author for this work

Research output: Contribution to journalArticlepeer-review

1 Citation (Scopus)
21 Downloads (Pure)

Abstract

Objectives: Falls pose a significant challenge in residential aged care facilities (RACFs). Existing falls prediction tools perform poorly and fail to capture evolving risk factors. We aimed to develop and internally validate dynamic fall risk prediction models and create point-based scoring systems for residents with and without dementia.

Materials and methods: A longitudinal cohort study using electronic data from 27 RACFs in Sydney, Australia. The study included 5492 permanent residents, with a 70%-30% split for training and validation. The outcome measure was the incidence of falls. We tracked residents for 60 months, using monthly landmarks with 1-month prediction windows. We employed landmarking dynamic prediction for model development, a time-dependent area under receiver operating characteristics curve (AUROCC) for model evaluations, and a regression coefficient approach to create point-based scoring systems.

Results: The model identified 15 independent predictors of falls in dementia and 12 in nondementia cohorts. Falls history was the key predictor of subsequent falls in both dementia (HR 4.75, 95% CI, 4.45-5.06) and nondementia cohorts (HR 4.20, 95% CI, 3.87-4.57). The AUROCC across landmarks ranged from 0.67 to 0.87 for dementia and from 0.66 to 0.86 for nondementia cohorts but generally remained between 0.75 and 0.85 in both cohorts. The total point risk score ranged from −2 to 57 for dementia and 0 to 52 for nondementia cohorts.

Discussion: Our novel risk prediction models and scoring systems provide timely person-centered information for continuous monitoring of fall risk in RACFs.

Conclusion: Embedding these tools within electronic health records could facilitate the implementation of targeted proactive interventions to prevent falls.
Original languageEnglish
Pages (from-to)1113-1125
Number of pages13
JournalJournal of the American Medical Informatics Association
Volume31
Issue number5
Early online date26 Mar 2024
DOIs
Publication statusPublished - May 2024

Bibliographical note

Copyright the Author(s) 2024. Version archived for private and non-commercial use with the permission of the author/s and according to publisher conditions. For further rights please contact the publisher.

Keywords

  • falls
  • residential aged care
  • nursing homes
  • fall risk prediction

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