Development and validation of a robotic multifactorial fall-risk predictive model: A one-year prospective study in community-dwelling older adults

Alberto Cella, Alice De Luca, Valentina Squeri, Sara Parodi, Francesco Vallone, Angela Giorgeschi, Barbara Senesi, Ekaterini Zigoura, Katerin Leslie Quispe Guerrero, Giacomo Siri, Lorenzo De Michieli, Jody Saglia, Carlo Sanfilippo and Alberto Pilotto

PLOS ONE, 2020, vol. 15, issue 6, 1-22

Abstract: Background: Falls in the elderly are a major public health concern because of their high incidence, the involvement of many risk factors, the considerable post-fall morbidity and mortality, and the health-related and social costs. Given that many falls are preventable, the early identification of older adults at risk of falling is crucial in order to develop tailored interventions to prevent such falls. To date, however, the fall-risk assessment tools currently used in the elderly have not shown sufficiently high predictive validity to distinguish between subjects at high and low fall risk. Consequently, predicting the risk of falling remains an unsolved issue in geriatric medicine. This one-year prospective study aims to develop and validate, by means of a cross-validation method, a multifactorial fall-risk model based on clinical and robotic parameters in older adults. Methods: Community-dwelling subjects aged ≥ 65 years were enrolled. At the baseline, all subjects were evaluated for history of falling and number of drugs taken daily, and their gait and balance were evaluated by means of the Timed “Up & Go” test (TUG), Gait Speed (GS), Short Physical Performance Battery (SPPB) and Performance-Oriented Mobility Assessment (POMA). They also underwent robotic assessment by means of the hunova robotic device to evaluate the various components of balance. All subjects were followed up for one-year and the number of falls was recorded. The models that best predicted falls—on the basis of: i) only clinical parameters; ii) only robotic parameters; iii) clinical plus robotic parameters—were identified by means of a cross-validation method. Results: Of the 100 subjects initially enrolled, 96 (62 females, mean age 77.17±.49 years) completed the follow-up and were included. Within one year, 32 participants (33%) experienced at least one fall (“fallers”), while 64 (67%) did not (“non-fallers”). The best classifier model to emerge from cross-validated fall-risk estimation included eight clinical variables (age, sex, history of falling in the previous 12 months, TUG, Tinetti, SPPB, Low GS, number of drugs) and 20 robotic parameters, and displayed an area under the receiver operator characteristic (ROC) curve of 0.81 (95% CI: 0.72–0.90). Notably, the model that included only three of these clinical variables (age, history of falls and low GS) plus the robotic parameters showed similar accuracy (ROC AUC 0.80, 95% CI: 0.71–0.89). In comparison with the best classifier model that comprised only clinical parameters (ROC AUC: 0.67; 95% CI: 0.55–0.79), both models performed better in predicting fall risk, with an estimated Net Reclassification Improvement (NRI) of 0.30 and 0.31 (p = 0.02), respectively, and an estimated Integrated Discrimination Improvement (IDI) of 0.32 and 0.27 (p

Date: 2020
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Persistent link: https://EconPapers.repec.org/RePEc:plo:pone00:0234904

DOI: 10.1371/journal.pone.0234904

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