Cargando…

Fall prediction using decision tree analysis in acute care units

[Purpose] To present an accurate and straight-forward system of fall prediction by performing decision tree analysis using both the fall assessment sheet and Berg balance scale (BBS). [Participants and Methods] The participants in this retrospective study were inpatients from acute care units. We ex...

Descripción completa

Detalles Bibliográficos
Autores principales: Tamura, Shuntaro, Kobayashi, Makoto, Saito, Yasuyuki, Asakura, Tomoyuki, Usuda, Shigeru
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Society of Physical Therapy Science 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708011/
https://www.ncbi.nlm.nih.gov/pubmed/33281287
http://dx.doi.org/10.1589/jpts.32.722
_version_ 1783617477515673600
author Tamura, Shuntaro
Kobayashi, Makoto
Saito, Yasuyuki
Asakura, Tomoyuki
Usuda, Shigeru
author_facet Tamura, Shuntaro
Kobayashi, Makoto
Saito, Yasuyuki
Asakura, Tomoyuki
Usuda, Shigeru
author_sort Tamura, Shuntaro
collection PubMed
description [Purpose] To present an accurate and straight-forward system of fall prediction by performing decision tree analysis using both the fall assessment sheet and Berg balance scale (BBS). [Participants and Methods] The participants in this retrospective study were inpatients from acute care units. We extracted the risk factors for falls from the fall assessment and performed a decision tree analysis using the extracted fall risk factors and BBS score. [Results] “History of more than one fall in the last 1 year”, “Muscle weakness”, “Use of a walking aid or wheelchair”, “Requires assistance for transfer”, “Use of Narcotics”, “Dangerous behavior”, and “High degree of self-reliance” were fall risk factors. The decision tree analysis extracted five fall risk factors, with an area under the curve of 0.7919. Patients with no history of falls and who did not require assistance for transfer or those with a BBS score ≥51 did not fall. [Conclusion] Decision tree-based fall prediction was useful and straightforward and revealed that patients with no history of falling and those who did not require assistance for transfer or had a BBS score ≥51 had a low risk of falling.
format Online
Article
Text
id pubmed-7708011
institution National Center for Biotechnology Information
language English
publishDate 2020
publisher The Society of Physical Therapy Science
record_format MEDLINE/PubMed
spelling pubmed-77080112020-12-05 Fall prediction using decision tree analysis in acute care units Tamura, Shuntaro Kobayashi, Makoto Saito, Yasuyuki Asakura, Tomoyuki Usuda, Shigeru J Phys Ther Sci Original Article [Purpose] To present an accurate and straight-forward system of fall prediction by performing decision tree analysis using both the fall assessment sheet and Berg balance scale (BBS). [Participants and Methods] The participants in this retrospective study were inpatients from acute care units. We extracted the risk factors for falls from the fall assessment and performed a decision tree analysis using the extracted fall risk factors and BBS score. [Results] “History of more than one fall in the last 1 year”, “Muscle weakness”, “Use of a walking aid or wheelchair”, “Requires assistance for transfer”, “Use of Narcotics”, “Dangerous behavior”, and “High degree of self-reliance” were fall risk factors. The decision tree analysis extracted five fall risk factors, with an area under the curve of 0.7919. Patients with no history of falls and who did not require assistance for transfer or those with a BBS score ≥51 did not fall. [Conclusion] Decision tree-based fall prediction was useful and straightforward and revealed that patients with no history of falling and those who did not require assistance for transfer or had a BBS score ≥51 had a low risk of falling. The Society of Physical Therapy Science 2020-11-11 2020-11 /pmc/articles/PMC7708011/ /pubmed/33281287 http://dx.doi.org/10.1589/jpts.32.722 Text en 2020©by the Society of Physical Therapy Science. Published by IPEC Inc. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial No Derivatives (by-nc-nd) License. (CC-BY-NC-ND 4.0: https://creativecommons.org/licenses/by-nc-nd/4.0/)
spellingShingle Original Article
Tamura, Shuntaro
Kobayashi, Makoto
Saito, Yasuyuki
Asakura, Tomoyuki
Usuda, Shigeru
Fall prediction using decision tree analysis in acute care units
title Fall prediction using decision tree analysis in acute care units
title_full Fall prediction using decision tree analysis in acute care units
title_fullStr Fall prediction using decision tree analysis in acute care units
title_full_unstemmed Fall prediction using decision tree analysis in acute care units
title_short Fall prediction using decision tree analysis in acute care units
title_sort fall prediction using decision tree analysis in acute care units
topic Original Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7708011/
https://www.ncbi.nlm.nih.gov/pubmed/33281287
http://dx.doi.org/10.1589/jpts.32.722
work_keys_str_mv AT tamurashuntaro fallpredictionusingdecisiontreeanalysisinacutecareunits
AT kobayashimakoto fallpredictionusingdecisiontreeanalysisinacutecareunits
AT saitoyasuyuki fallpredictionusingdecisiontreeanalysisinacutecareunits
AT fallpredictionusingdecisiontreeanalysisinacutecareunits
AT asakuratomoyuki fallpredictionusingdecisiontreeanalysisinacutecareunits
AT usudashigeru fallpredictionusingdecisiontreeanalysisinacutecareunits