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

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).

Keywords: ROC curve; accidental falls; aged; odds ratio; risk assessment; risk factors.

Figure 1

Classification of risk factors by kind, reversibility, and setting. While the InCHIANTI dataset is about community dwellings, the ontology covers other settings, too.

Figure 2

Definition of fall event.

Figure 3

Probability to fall from risk factor specific probabilities.

Figure 4

Probability of factor specific fall event given exposure.

Figure 5

Probability to fall given exposures and contributions.

Figure 6

Odds ratio definition.

Figure 7

Approximated odds ratio.

Figure 8

Probability to fall with exposure to exactly 1 risk factor.

Figure 9

Contribution to fall probability from exposure to a single risk factor given odds ratio.

Figure 10

Probability to fall from risk factor odds ratios.

Figure 11

Steps in generating the LPAD rules.

Figure 12

ROC curve obtained on the InCHIANTI dataset.

Figure 13

Calibration plot; sample (N=2319) used for validation where divided in 10 deciles, according to their predicted risk. For each decile, the mean predicted risk and the observed proportion of positive cases (proportion of fallers) are shown on the X and Y axes, respectively. Bars indicate 95% confidence intervals.

Figure 14

Screenshot of the Web-based interface.