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A Systematic Review of the Guidelines and Delphi Study for the Multifactorial Fall Risk Assessment of Community-Dwelling Elderly.

ABSTRACT: As falls are among the most common causes of injury for the elderly, the prevention and early intervention are necessary. Fall assessment tools that include a variety of factors are recommended for preventing falls, but there is a lack of such tools. This study developed a multifactorial fall risk assessment tool based on current guidelines and validated it from the perspective of professionals. We followed the Meta-Analysis of Observational Studies in Epidemiology's guidelines in this systematic review. We used eight international and five Korean databases to search for appropriate guidelines. Based on the review results, we conducted the Delphi survey in three rounds; one open round and two scoring rounds. About nine experts in five professional areas participated in the Delphi study. We included nine guidelines. After conducting the Delphi study, the final version of the "Multifactorial Fall Risk Assessment tool for Community-Dwelling Older People" (MFA-C) has 36 items in six factors; general characteristics, behavior factors, disease history, medication history, physical function, and environmental factors. The validity of the MFA-C tool was largely supported by various academic fields. It is expected to be beneficial to the elderly in the community when it comes to tailored interventions to prevent falls.

SUBMITTER: Kim J

PROVIDER: S-EPMC7504076 | biostudies-literature | 2020 Aug

REPOSITORIES: biostudies-literature

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A Systematic Review of the Guidelines and Delphi Study for the Multifactorial Fall Risk Assessment of Community-Dwelling Elderly.

Kim Jieun J Lee Worlsook W Lee Seon Heui SH

International journal of environmental research and public health 20200821 17

As falls are among the most common causes of injury for the elderly, the prevention and early intervention are necessary. Fall assessment tools that include a variety of factors are recommended for preventing falls, but there is a lack of such tools. This study developed a multifactorial fall risk assessment tool based on current guidelines and validated it from the perspective of professionals. We followed the Meta-Analysis of Observational Studies in Epidemiology's guidelines in this systemati ...[more]

PMID: 32825699

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Project description:BackgroundFalls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults.MethodsCommunity-dwelling subjects aged ≥ 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed "Up & Go" test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls-on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameters-were identified by means of a cross-validation method.ResultsOf the 100 subjects initially enrolled, 96 (62 females, mean age 77.17±.49 years) completed the follow-up and were included. Within one year, 32 participants (33%) experienced at least one fall ("fallers"), while 64 (67%) did not ("non-fallers"). The best classifier model to emerge from cross-validated fall-risk estimation included eight clinical variables (age, sex, history of falling in the previous 12 months, TUG, Tinetti, SPPB, Low GS, number of drugs) and 20 robotic parameters, and displayed an area under the receiver operator characteristic (ROC) curve of 0.81 (95% CI: 0.72-0.90). Notably, the model that included only three of these clinical variables (age, history of falls and low GS) plus the robotic parameters showed similar accuracy (ROC AUC 0.80, 95% CI: 0.71-0.89). In comparison with the best classifier model that comprised only clinical parameters (ROC AUC: 0.67; 95% CI: 0.55-0.79), both models performed better in predicting fall risk, with an estimated Net Reclassification Improvement (NRI) of 0.30 and 0.31 (p = 0.02), respectively, and an estimated Integrated Discrimination Improvement (IDI) of 0.32 and 0.27 (p<0.001), respectively. The best model that comprised only robotic parameters (the 20 parameters identified in the final model) achieved a better performance than the clinical parameters alone, but worse than the combination of both clinical and robotic variables (ROC AUC: 0.73, 95% CI 0.63-0.83).ConclusionA multifactorial fall-risk assessment that includes clinical and hunova robotic variables significantly improves the accuracy of predicting the risk of falling in community-dwelling older people. Our data suggest that combining clinical and robotic assessments can more accurately identify older people at high risk of falls, thereby enabling personalized fall-prevention interventions to be undertaken.

Dataset Information

A Systematic Review of the Guidelines and Delphi Study for the Multifactorial Fall Risk Assessment of Community-Dwelling Elderly.

Publications

A Systematic Review of the Guidelines and Delphi Study for the Multifactorial Fall Risk Assessment of Community-Dwelling Elderly.

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