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FRAT-up, a Web-based fall-risk assessment tool for elderly people living in the community.

Cattelani L, Palumbo P, Palmerini L, Bandinelli S, Becker C, Chesani F, Chiari L - J. Med. Internet Res. (2015)

Bottom Line: Fall prevention protocols and interventions can decrease the number of falls.We compared reported falls with predicted ones and computed performance indicators.Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical, Electronic, and Information Engineering - DEI, University of Bologna, Bologna, Italy.

ABSTRACT

Background: About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls.

Objective: The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up.

Methods: FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators.

Results: The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration.

Conclusions: FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface.

Trial registration: ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR).

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Related in: MedlinePlus

Approximated odds ratio.
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Related In: Results  -  Collection

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figure7: Approximated odds ratio.

Mentions: We assume that ORi may be approximated as in Figure 7. Informally, Assumption (a) states that the odds ratio computed on the whole population is similar to the odds ratio computed restricting the population to subjects having at most one exposure. This assumption is obviously true in models where each subject has at most one exposure; otherwise there is a difference in the two values. This has not been quantified yet; the quality of the approximation will be experimentally compared with other methods as a future development.


FRAT-up, a Web-based fall-risk assessment tool for elderly people living in the community.

Cattelani L, Palumbo P, Palmerini L, Bandinelli S, Becker C, Chesani F, Chiari L - J. Med. Internet Res. (2015)

Approximated odds ratio.
© Copyright Policy - open-access
Related In: Results  -  Collection

License 1 - License 2
Show All Figures
getmorefigures.php?uid=PMC4376110&req=5

figure7: Approximated odds ratio.
Mentions: We assume that ORi may be approximated as in Figure 7. Informally, Assumption (a) states that the odds ratio computed on the whole population is similar to the odds ratio computed restricting the population to subjects having at most one exposure. This assumption is obviously true in models where each subject has at most one exposure; otherwise there is a difference in the two values. This has not been quantified yet; the quality of the approximation will be experimentally compared with other methods as a future development.

Bottom Line: Fall prevention protocols and interventions can decrease the number of falls.We compared reported falls with predicted ones and computed performance indicators.Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Electrical, Electronic, and Information Engineering - DEI, University of Bologna, Bologna, Italy.

ABSTRACT

Background: About 30% of people over 65 are subject to at least one unintentional fall a year. Fall prevention protocols and interventions can decrease the number of falls. To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects. Despite extensive research, existing assessment tools for fall risk have been insufficient for predicting falls.

Objective: The goal of this study is to present a novel web-based fall-risk assessment tool (FRAT-up) and to evaluate its accuracy in predicting falls, within a context of community-dwelling persons aged 65 and up.

Methods: FRAT-up is based on the assumption that a subject's fall risk is given by the contribution of their exposure to each of the known fall-risk factors. Many scientific studies have investigated the relationship between falls and risk factors. The majority of these studies adopted statistical approaches, usually providing quantitative information such as odds ratios. FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk. FRAT-up is based on a formal ontology that enlists a number of known risk factors, together with quantitative findings in terms of odds ratios. From such information, an automatic algorithm generates a rule-based probabilistic logic program, that is, a set of rules for each risk factor. The rule-based program takes the health profile of the subject (in terms of exposure to the risk factors) and computes the fall risk. A Web-based interface allows users to input health profiles and to visualize the risk assessment for the given subject. FRAT-up has been evaluated on the InCHIANTI Study dataset, a representative population-based study of older persons living in the Chianti area (Tuscany, Italy). We compared reported falls with predicted ones and computed performance indicators.

Results: The obtained area under curve of the receiver operating characteristic was 0.642 (95% CI 0.614-0.669), while the Brier score was 0.174. The Hosmer-Lemeshow test indicated statistical significance of miscalibration.

Conclusions: FRAT-up is a web-based tool for evaluating the fall risk of people aged 65 or up living in the community. Validation results of fall risks computed by FRAT-up show that its performance is comparable to externally validated state-of-the-art tools. A prototype is freely available through a web-based interface.

Trial registration: ClinicalTrials.gov NCT01331512 (The InChianti Follow-Up Study); http://clinicaltrials.gov/show/NCT01331512 (Archived by WebCite at http://www.webcitation.org/6UDrrRuaR).

Show MeSH
Related in: MedlinePlus