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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; 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.

SUBMITTER: Nakanishi T

PROVIDER: S-EPMC8429765 | biostudies-literature | 2021 Sep

REPOSITORIES: biostudies-literature

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

Nakanishi Tomoko T Ikeda Tokunori T Nakamura Taishi T Yamanouchi Yoshinori Y Chikamoto Akira A Usuku Koichiro K

Scientific reports 20210909 1

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 perfo ...[more]

PMID: 34504235

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Project description:IntroductionRecent publications have called for substantial improvements in the design, conduct, analysis and reporting of prediction models. Publication of study protocols, with prespecification of key aspects of the analysis plan, can help to improve transparency, increase quality and protect against increased type I error. Valid population-based risk algorithms are essential for population health planning and policy decision-making. The purpose of this study is to develop, evaluate and apply cardiovascular disease (CVD) risk algorithms for the population setting.Methods and analysisThe Ontario sample of the Canadian Community Health Survey (2001, 2003, 2005; 77,251 respondents) will be used to assess risk factors focusing on health behaviours (physical activity, diet, smoking and alcohol use). Incident CVD outcomes will be assessed through linkage to administrative healthcare databases (619,886 person-years of follow-up until 31 December 2011). Sociodemographic factors (age, sex, immigrant status, education) and mediating factors such as presence of diabetes and hypertension will be included as predictors. Algorithms will be developed using competing risks survival analysis. The analysis plan adheres to published recommendations for the development of valid prediction models to limit the risk of overfitting and improve the quality of predictions. Key considerations are fully prespecifying the predictor variables; appropriate handling of missing data; use of flexible functions for continuous predictors; and avoiding data-driven variable selection procedures. The 2007 and 2009 surveys (approximately 50,000 respondents) will be used for validation. Calibration will be assessed overall and in predefined subgroups of importance to clinicians and policymakers.Ethics and disseminationThis study has been approved by the Ottawa Health Science Network Research Ethics Board. The findings will be disseminated through professional and scientific conferences, and in peer-reviewed journals. The algorithm will be accessible electronically for population and individual uses.Trial registration numberClinicalTrials.gov NCT02267447.

Project description:BackgroundProtection Motivation Theory could be another potential and good framework that addresses essential elements in a behavioural change leading to positive fall protective behaviours. The positive behavioural change could reduce the risk of falls and improve the quality of life of the older community. The study aims to evaluate the reliability and validity of the culturally adapted Protection Motivation Theory scale for older adults' fall protection motivation or protective behaviours to reduce fall risk.MethodsA cross-sectional study was conducted to establish a psychometric instrument validation. A total of 389 participants aged 55 years and above were included. The study was conducted in Sarawak, Malaysia, from November 2021 to January 2022 in two phases, translation of the PMT Scale, cross-cultural adaptation, face validation and pre-testing of the PMT Scale. The participants were selected using multistage random sampling in a primary healthcare clinic. Data entry and statistical analysis were performed using IBM SPSS version 26 for exploratory factor analysis and SmartPLS version 3.3.7 for confirmatory factor analysis using partial least square structural equation modelling.ResultsThe Kaiser-Meyer-Olkin value was 0.760, Bartlett's sphericity test was significant and the total variance explained was 61%. It identified 31 items within eight dimensions of the Protection Motivation Theory scale. The Higher Order Constructs' measurement model indicates that the convergent and discriminant validity were established (Cronbach's alpha and composite reliability: ≥ 0.740; average variance extracted: 0.619 to 0.935 and Henseler's Heterotrait-Monotrait criterion for all constructs' discriminant validity: < 0.9). Test-retest for the intraclass correlation coefficient was 0.745. The model's coefficient of determination demonstrated R2 = 0.375.ConclusionOverall, the Protection Motivation Theory Scale has established its reliability and validity for assisting older adults in the community. The Protection Motivation Theory Scale could be used in fall prevention interventions by promoting fall protective behaviours to reduce fall risk among community-dwelling older adults. The scale could assist healthcare providers in assessing the intention of older adults to use fall protective behaviours to reduce fall risk and serve as an alternative reference in developing fall prevention education in a fall prevention strategy.

Dataset Information

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

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

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