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Author Notes:

Benjamin Kalivas, kalivas@musc.edu

The authors declare that there are no conflicts of interest regarding the publication of this article.

Subjects:

Keywords:

  • Delirium
  • In-Hospital Falls

The Association between Delirium and In-Hospital Falls: A Cross-Sectional Analysis of a Delirium Screening Program

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Journal Title:

Journal of Aging Research

Volume:

Volume 2023

Publisher:

, Pages 1562773-1562773

Type of Work:

Article | Final Publisher PDF

Abstract:

Rationale. In-hospital falls and delirium are important events to understand in the hospital setting. Although the link between these events is well described, the impact on patient outcomes and the health system necessitates duplication with the use of accessible delirium screening tools. Aims and Objectives. To understand the association of delirium and falls. Methods. A cross-sectional study using delirium screening and falls reports was used to measure the association between delirium and falls. All inpatient data from August, 2018, to January, 2020, at a large academic medical center were analyzed. A multivariable logistic regression of 29,655 hospital admissions was used to understand the association between in-hospital delirium and falls. Results. Analysis revealed a delirium rate of 12.5% (n = 3,707) of all admissions and 286 (0.9%) admissions with falls; of the falls studied, 37.6% of these patients screened positive for delirium during their admission. Relative to those who screened negative for delirium, admissions that screened positive for delirium had a 2.81 increased odds of falling. Conclusions. Delirium and falls are related. This strong association should motivate health systems to look closely at both problems. Falls and delirium can both have immense impacts on the patient and the health system. The powerful association between them provides a window to reduce these additional patient harms. More specifically, a modern delirium screening tool should be used as part of routine risk assessment focused on reducing in-hospital falls.

Copyright information:

© 2023 Benjamin Kalivas et al.

This is an Open Access work distributed under the terms of the Creative Commons Attribution 4.0 International License (https://creativecommons.org/licenses/by/4.0/).
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