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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: To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects.FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk.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

Probability of factor specific fall event given exposure.
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figure4: Probability of factor specific fall event given exposure.

Mentions: The conditional probability of d given E can then be calculated as in Figure 3, by De Morgan laws and assumptions in Equation 1. This function models the probability of an event given a set of possible causes and is known as noisy-OR gate [41] (in this case OR refers to the logical operator). We make the assumption in Figure 4. Ci is a quantity yet to determine. Ci is the contribution to the probability of the effect d given by the exposure to the risk factor Ei. A method to assign values to the contributions Ci is introduced in the following. Using the equation in Figure 4, the equation in Figure 3 becomes the one depicted in Figure 5. Since we want to model a minimum probability of the adverse event that is applied even in the absence of any observation-specific exposures, we assign P(E0=1)=1. C0 is the risk that is present in this case. To assign values to the contributions of the exposures, we start from the OR. The OR relative to risk factor Ei, with i=1,…, n, is defined as in Figure 6. Note that the condition E0=1 is always true and is highlighted above just for convenience.


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)

Probability of factor specific fall event given exposure.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

figure4: Probability of factor specific fall event given exposure.
Mentions: The conditional probability of d given E can then be calculated as in Figure 3, by De Morgan laws and assumptions in Equation 1. This function models the probability of an event given a set of possible causes and is known as noisy-OR gate [41] (in this case OR refers to the logical operator). We make the assumption in Figure 4. Ci is a quantity yet to determine. Ci is the contribution to the probability of the effect d given by the exposure to the risk factor Ei. A method to assign values to the contributions Ci is introduced in the following. Using the equation in Figure 4, the equation in Figure 3 becomes the one depicted in Figure 5. Since we want to model a minimum probability of the adverse event that is applied even in the absence of any observation-specific exposures, we assign P(E0=1)=1. C0 is the risk that is present in this case. To assign values to the contributions of the exposures, we start from the OR. The OR relative to risk factor Ei, with i=1,…, n, is defined as in Figure 6. Note that the condition E0=1 is always true and is highlighted above just for convenience.

Bottom Line: To be effective, a prevention strategy requires a prior step to evaluate the fall risk of the subjects.FRAT-up exploits these numerical results to compute how each single factor contributes to the overall fall risk.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