Fall Risk Assessment Tools for Elderly Living in the Community: Can We Do Better?

Abstract

BackgroundFalls 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 methodsIn 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.ResultsThe 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.

Identifying fallers among ophthalmic patients using classification tree methodology

“…The performances of the developed model are comparable with different fall risk assessment tools developed in general community-dwelling population proposed in literature[11, 15–18], mainly based on demographic information, self-reported problems with balance and coordination, as shown in Table 3. However, these models have been developed for clinicians caring for the general elderly population and not for ophthalmologists visiting visually impaired people.…”

“…Despite the evidences about the importance of visual assessment in patients who have fallen, and the strong attention for falls by the NHS in UK, a Royal College of Physicians audit in 2009 showed that most sites in UK did not employ a standardized visual acuity assessment. Moreover, most models developed to assess fall risk[11–18] are typically relying on performance-based physical assessment tasks, which are not usually performed in ophthalmologic clinics [17]. To the best of authors' knowledge, there is no specific tool designed to help ophthalmologists to screen visually impaired subjects for falling risk, during routine ophthalmologic visits.…”

Predicting Falls in People Aged 65 Years and Older from Insurance Claims

“…Model accuracy was achieved by encoding a large number of predictive features, where we perform a series of data aggregation steps, going from tens of thousands of codes (NDC & ICD-9) to hundreds of groupings to the 62 predictive features. The model’s AUC of 0.71 for the validation dataset compares favorably to performance achieved by published models for falls 22 and other conditions. 17, 29 …”

“…13–19 Models for risk of future falls have been developed for residents of nursing homes, 20 inpatients, 21 and members of a cohort study on aging and mobility, 22 but not yet for the general elderly population. We sought to develop a predictive model that could run universally for those aged 65 years and older based solely on insurance claims.…”

Fall prevention in central coast community pharmacies

“…At the workshop, pharmacists received a range of resources (H. Kale & M. Wilding, unpublished data) including an evidence‐based “quick falls risk assessment tool” (QFRAT) developed by the CCHPS . It includes three “yes/no” questions to assess whether clients:…”