Pierpaolo Palumbo

FRAT-up, a Web-based Fall-Risk Assessment Tool for Elderly People Living in the Community

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).

Fall risk assessment tools for elderly living in the community: can we do better?

Background Falls are a common, serious threat to the health and self-confidence of the elderly. Assessment of fall risk is an important aspect of effective fall prevention programs. Objectives and methods In order to test whether it is possible to outperform current prognostic tools for falls, we analyzed 1010 variables pertaining to mobility collected from 976 elderly subjects (InCHIANTI study). We trained and validated a data-driven model that issues probabilistic predictions about future falls. We benchmarked the model against other fall risk indicators: history of falls, gait speed, Short Physical Performance Battery (Guralnik et al. 1994), and the literature-based fall risk assessment tool FRAT-up (Cattelani et al. 2015). Parsimony in the number of variables included in a tool is often considered a proxy for ease of administration. We studied how constraints on the number of variables affect predictive accuracy. Results The proposed model and FRAT-up both attained the same discriminative ability; the area under the Receiver Operating Characteristic (ROC) curve (AUC) for multiple falls was 0.71. They outperformed the other risk scores, which reported AUCs for multiple falls between 0.64 and 0.65. Thus, it appears that both data-driven and literature-based approaches are better at estimating fall risk than commonly used fall risk indicators. The accuracy–parsimony analysis revealed that tools with a small number of predictors (~1–5) were suboptimal. Increasing the number of variables improved the predictive accuracy, reaching a plateau at ~20–30, which we can consider as the best trade-off between accuracy and parsimony. Obtaining the values of these ~20–30 variables does not compromise usability, since they are usually available in comprehensive geriatric assessments.

A Probabilistic Model to Investigate the Properties of Prognostic Tools for Falls

BACKGROUND Falls are a prevalent and burdensome problem in the elderly. Tools for the assessment of fall risk are fundamental for fall prevention. Clinical studies for the development and evaluation of prognostic tools for falls show high heterogeneity in the settings and in the reported results. Newly developed tools are susceptible to over-optimism. OBJECTIVES This study proposes a probabilistic model to address critical issues about fall prediction through the analysis of the properties of an ideal prognostic tool for falls. METHODS The model assumes that falls occur within a population according to the Greenwood and Yule scheme for accident-proneness. Parameters for the fall rate distribution are estimated from counts of falls of four different epidemiological studies. RESULTS We obtained analytic formulas and quantitative estimates for the predictive and discriminative properties of the ideal prognostic tool. The area under the receiver operating characteristic curve (AUC) ranges between about 0.80 and 0.89 when prediction on any fall is made within a follow-up of one year. Predicting on multiple falls results in higher AUC. CONCLUSIONS The discriminative ability of current validated prognostic tools for falls is sensibly lower than what the proposed ideal perfect tool achieves. A sensitivity analysis of the predictive and discriminative properties of the tool with respect to study settings and fall rate distribution identifies major factors that can account for the high heterogeneity of results observed in the literature.

FRAT-Up, a Rule-Based System Evaluating Fall Risk in the Elderly

About one-third of persons over 65 are subject to at least one fall during a year, and many of them are subjected to health, psychological and financial consequences. A requirement to improve the effectiveness of preventive interventions is to timely identify subjects at higher risk. In this work we introduce the Farseeing Fall Risk Assessment Tool (FRAT-up), a software tool for evaluating the fall risk of a subject, based on known risk factors. The tool is based on probabilistic rules, generated automatically from a light ontology capturing the scientific findings about risk factors. FRAT-up has been tested on the In CHIANTI dataset, showing performances comparable with state-of-the-art tools.

Natural turn measures predict recurrent falls in community-dwelling older adults: a longitudinal cohort study

Although turning has been reported as one of the leading activities performed during a fall, and falls during turning result in 8-times more hip fractures than falls during linear gait, the quantity and quality of turns resulting in falls remain unknown since turns are rarely assessed during activities of daily living. 160 community-dwelling older adults were monitored for one week using smartphone technology. Turn measures and activity rates were quantified. Fall incidence within 12 months from continuous monitoring defined fall status, with 7/153 prospective fallers/non-fallers. Based on the analysis of 718,582 turns, prospective fallers turned less frequently, took longer to turn, and were less consistent in turn angle (p = 0.007, 0.025, and 0.038, respectively). Prospective fallers also walked slower and spent less time walking and turning and more time engaged in sedentary behavior (p = 0.043, 0.012, and 0.015, respectively). Individuals experiencing decline in the control of gait and/or turning may attempt to reduce their risk of falling by limiting their exposure and implementing cautionary movement strategies while turning. Since there was no difference in the overall active rate between prospective fallers and non-fallers, impaired gait and turning ability, specifically, may attribute to elevated fall risk within this cohort.

Predictive Performance of a Fall Risk Assessment Tool for Community-Dwelling Older People (FRAT-up) in 4 European Cohorts

Background and objective: The fall risk assessment tool (FRAT-up) is a tool for predicting falls in community-dwelling older people based on a meta-analysis of fall risk factors. Based on the fall risk factor profile, this tool calculates the individual risk of falling over the next year. The objective of this study is to evaluate the performance of FRAT-up in predicting future falls in multiple cohorts. Methods: Information about fall risk factors in 4 European cohorts of older people [Activity and Function in the Elderly (ActiFE), Germany; English Longitudinal Study of Aging (ELSA), England; Invecchiare nel Chianti (InCHIANTI), Italy; Irish Longitudinal Study on Aging (TILDA), Ireland] was used to calculate the FRAT-up risk score in individual participants. Information about falls that occurred after the assessment of the risk factors was collected from subsequent longitudinal follow-ups. We compared the performance of FRAT-up against those of other prediction models specifically fitted in each cohort by calculation of the area under the receiver operating characteristic curve (AUC). Results: The AUC attained by FRAT-up is 0.562 [95% confidence interval (CI) 0.530–0.594] for ActiFE, 0.699 (95% CI 0.680–0.718) for ELSA, 0.636 (95% CI 0.594–0.681) for InCHIANTI, and 0.685 (95% CI 0.660–0.709) for TILDA. Mean FRAT-up AUC as estimated from meta-analysis is 0.646 (95% CI 0.584–0.708), with substantial heterogeneity between studies. In each cohort, FRAT-up discriminant ability is surpassed, at most, by the cohort-specific risk model fitted on that same cohort. Conclusions: We conclude that FRAT-up is a valid approach to estimate risk of falls in populations of community-dwelling older people. However, further studies should be performed to better understand the reasons for the observed heterogeneity across studies and to refine a tool that performs homogeneously with higher accuracy measures across different populations.

Conceptualizing a Dynamic Fall Risk Model Including Intrinsic Risks and Exposures

Falls are a major cause of injury and disability in older people, leading to serious health and social consequences including fractures, poor quality of life, loss of independence, and institutionalization. To design and provide adequate prevention measures, accurate understanding and identification of persons individual fall risk is important. However, to date, the performance of fall risk models is weak compared with models estimating, for example, cardiovascular risk. This deficiency may result from 2 factors. First, current models consider risk factors to be stable for each person and not change over time, an assumption that does not reflect real-life experience. Second, current models do not consider the interplay of individual exposure including type of activity (eg, walking, undertaking transfers) and environmental risks (eg, lighting, floor conditions) in which activity is performed. Therefore, we posit a dynamic fall risk model consisting of intrinsic risk factors that vary over time and exposure (activity in context). eHealth sensor technology (eg, smartphones) begins to enable the continuous measurement of both the above factors. We illustrate our model with examples of real-world falls from the FARSEEING database. This dynamic framework for fall risk adds important aspects that may improve understanding of fall mechanisms, fall risk models, and the development of fall prevention interventions.

The Improvement of Turning Ability is a Key Objective for Fall-Risk Reduction in Individuals with Impaired Dynamic Stability

Turning difficulty is a sign of balance instability and may be indicative of elevated fall risk. Features extracted from the 90° turn suggest that this turn type is the most unstable type of turn in older adults with compromised balance control. Since the 90° turn is also the most common type of turn executed during activities of daily living, we recommend targeting movement strategies specific to 90° turning during therapeutic intervention. Specific neuro-rehabilitation strategies to improve/optimize turning ability in individuals with compromised stability may significantly contribute to fall-risk reduction. The adoption of quantitative tools for the assessment and monitoring of turning quality is advisable.