Development of an algorithm for assessing fall risk in a Japanese inpatient population

Abstract Falling is a representative incident in hospitalization and can cause serious complications. In this study, we constructed an algorithm that nurses can use to easily recognize essential fall risk factors and appropriately perform an assessment. A total of 56,911 inpatients (non-fall, 56,673...

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Autores principales:	Tomoko Nakanishi, Tokunori Ikeda, Taishi Nakamura, Yoshinori Yamanouchi, Akira Chikamoto, Koichiro Usuku
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c55aa20760a5450898fdf1a0ab7f884d
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spelling	oai:doaj.org-article:c55aa20760a5450898fdf1a0ab7f884d2021-12-02T17:41:12ZDevelopment of an algorithm for assessing fall risk in a Japanese inpatient population10.1038/s41598-021-97483-12045-2322https://doaj.org/article/c55aa20760a5450898fdf1a0ab7f884d2021-09-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-97483-1https://doaj.org/toc/2045-2322Abstract Falling is a representative incident in hospitalization and can cause serious complications. In this study, we constructed an algorithm that nurses can use to easily recognize essential fall risk factors and appropriately perform an assessment. A total of 56,911 inpatients (non-fall, 56,673; fall; 238) hospitalized between October 2017 and September 2018 were used for the training dataset. Correlation coefficients, multivariable logistic regression analysis, and decision tree analysis were performed using 36 fall risk factors identified from inpatients. An algorithm was generated combining nine essential fall risk factors (delirium, fall history, use of a walking aid, stagger, impaired judgment/comprehension, muscle weakness of the lower limbs, night urination, use of sleeping drug, and presence of infusion route/tube). Moreover, fall risk level was conveniently classified into four groups (extra-high, high, moderate, and low) according to the priority of fall risk. Finally, we confirmed the reliability of the algorithm using a validation dataset that comprised 57,929 inpatients (non-fall, 57,695; fall, 234) hospitalized between October 2018 and September 2019. Using the newly created algorithm, clinical staff including nurses may be able to appropriately evaluate fall risk level and provide preventive interventions for individual inpatients.Tomoko NakanishiTokunori IkedaTaishi NakamuraYoshinori YamanouchiAkira ChikamotoKoichiro UsukuNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-10 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tomoko Nakanishi Tokunori Ikeda Taishi Nakamura Yoshinori Yamanouchi Akira Chikamoto Koichiro Usuku Development of an algorithm for assessing fall risk in a Japanese inpatient population
description	Abstract Falling is a representative incident in hospitalization and can cause serious complications. In this study, we constructed an algorithm that nurses can use to easily recognize essential fall risk factors and appropriately perform an assessment. A total of 56,911 inpatients (non-fall, 56,673; fall; 238) hospitalized between October 2017 and September 2018 were used for the training dataset. Correlation coefficients, multivariable logistic regression analysis, and decision tree analysis were performed using 36 fall risk factors identified from inpatients. An algorithm was generated combining nine essential fall risk factors (delirium, fall history, use of a walking aid, stagger, impaired judgment/comprehension, muscle weakness of the lower limbs, night urination, use of sleeping drug, and presence of infusion route/tube). Moreover, fall risk level was conveniently classified into four groups (extra-high, high, moderate, and low) according to the priority of fall risk. Finally, we confirmed the reliability of the algorithm using a validation dataset that comprised 57,929 inpatients (non-fall, 57,695; fall, 234) hospitalized between October 2018 and September 2019. Using the newly created algorithm, clinical staff including nurses may be able to appropriately evaluate fall risk level and provide preventive interventions for individual inpatients.
format	article
author	Tomoko Nakanishi Tokunori Ikeda Taishi Nakamura Yoshinori Yamanouchi Akira Chikamoto Koichiro Usuku
author_facet	Tomoko Nakanishi Tokunori Ikeda Taishi Nakamura Yoshinori Yamanouchi Akira Chikamoto Koichiro Usuku
author_sort	Tomoko Nakanishi
title	Development of an algorithm for assessing fall risk in a Japanese inpatient population
title_short	Development of an algorithm for assessing fall risk in a Japanese inpatient population
title_full	Development of an algorithm for assessing fall risk in a Japanese inpatient population
title_fullStr	Development of an algorithm for assessing fall risk in a Japanese inpatient population
title_full_unstemmed	Development of an algorithm for assessing fall risk in a Japanese inpatient population
title_sort	development of an algorithm for assessing fall risk in a japanese inpatient population
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c55aa20760a5450898fdf1a0ab7f884d
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_version_	1718379727668903936

Development of an algorithm for assessing fall risk in a Japanese inpatient population

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