Predictive Performance of a Fall Risk Assessment Tool for Community-Dwelling Older People (FRAT-up) in 4 European Cohorts.
Journal of the American Medical Directors Association
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.
10.1016/j.jamda.2016.07.015
Predicting Short-Term Risk of Falls in a High-Risk Group With Dementia.
Mehdizadeh Sina,Sabo Andrea,Ng Kimberley-Dale,Mansfield Avril,Flint Alastair J,Taati Babak,Iaboni Andrea
Journal of the American Medical Directors Association
OBJECTIVES:To develop a prognostic model to predict the probability of a short-term fall (within the next 7 to 30 days) in older adults with dementia. DESIGN:Prospective observational study. SETTING AND PARTICIPANTS:Fifty-one individuals with dementia at high risk of falls from a specialized dementia inpatient unit. METHODS:Clinical and demographic measures were collected and a vision-based markerless motion capture was used to record the natural gait of participants over a 2-week baseline. Falls were tracked throughout the length of stay. Cox proportional hazard regression analysis was used to build a prognostic model to determine fall-free survival probabilities at 7 days and at 30 days. The model's discriminative ability was also internally validated. RESULTS:Fall history and gait stability (estimated margin of stability) were statistically significant predictors of time to fall and included in the final prognostic model. The model's predicted survival probabilities were close to observed values at both 7 and 30 days. The area under the receiver operating curve was 0.80 at 7 days, and 0.67 at 30 days and the model had a discrimination performance (the Harrel concordance index) of 0.71. CONCLUSIONS AND IMPLICATIONS:Our short-term falls risk model had fair to good predictive and discrimination ability. Gait stability and recent fall history predicted an imminent fall in our population. This provides some preliminary evidence that the degree of gait instability may be measureable in natural everyday gait to allow dynamic falls risk monitoring. External validation of the model using a separate data set is needed to evaluate model's predictive performance.
10.1016/j.jamda.2020.07.030
Path tortuosity in everyday movements of elderly persons increases fall prediction beyond knowledge of fall history, medication use, and standardized gait and balance assessments.
Kearns William D,Fozard James L,Becker Marion,Jasiewicz Jan M,Craighead Jeffrey D,Holtsclaw Lori,Dion Charles
Journal of the American Medical Directors Association
OBJECTIVES:We hypothesized that variability in voluntary movement paths of assisted living facility (ALF) residents would be greater in the week preceding a fall compared with residents who did not fall. DESIGN:Prospective, observational study using telesurveillance technology. SETTING:Two ALFs. PARTICIPANTS:The sample consisted of 69 older ALF residents (53 female) aged 76.9 (SD ± 11.9 years). MEASUREMENT:Daytime movement in ALF common use areas was automatically tracked using a commercially available ultra-wideband radio real-time location sensor network with a spatial resolution of approximately 20 cm. Movement path variability (tortuosity) was gauged using fractal dimension (fractal D). A logistic regression was performed predicting movement related falls from fractal D, presence of a fall in the prior year, psychoactive medication use, and movement path length. Fallers and non-fallers were also compared on activities of daily living requiring supervision or assistance, performance on standardized static and dynamic balance, and stride velocity assessments gathered at the start of a 1-year fall observation period. Fall risk due to cognitive deficit was assessed by the Mini Mental Status Examination (MMSE), and by clinical dementia diagnoses from participant's activities of daily living health record. RESULTS:Logistic regression analysis revealed odds of falling increased 2.548 (P = .021) for every 0.1 increase in fractal D, and having a fall in the prior year increased odds of falling by 7.36 (P = .006). There was a trend for longer movement paths to reduce the odds of falling (OR .976 P = .08) but it was not significant. Number of psychoactive medications did not contribute significantly to fall prediction in the model. Fallers had more variable stride-to-stride velocities and required more activities of daily living assistance. CONCLUSIONS:High fractal D levels can be detected using commercially available telesurveillance technologies and offers a new tool for health services administrators seeking to reduce falls at their facilities.
10.1016/j.jamda.2012.06.010
A Simple Algorithm to Predict Falls in Primary Care Patients Aged 65 to 74 Years: The International Mobility in Aging Study.
Gomez Fernando,Wu Yan Yan,Auais Mohammad,Vafaei Afshin,Zunzunegui Maria-Victoria
Journal of the American Medical Directors Association
OBJECTIVE:Primary care practitioners need simple algorithms to identify older adults at higher risks of falling. Classification and regression tree (CaRT) analyses are useful tools for identification of clinical predictors of falls. DESIGN:Prospective cohort. SETTING:Community-dwelling older adults at 5 diverse sites: Tirana (Albania), Natal (Brazil), Manizales (Colombia), Kingston (Ontario, Canada), and Saint-Hyacinthe (Quebec, Canada). PARTICIPANTS:In 2012, 2002 participants aged 65-74 years from 5 international sites were assessed in the International Mobility in Aging Study. In 2014 follow-up, 86% of the participants (n = 1718) were reassessed. MEASUREMENTS:These risk factors for the occurrence of falls in 2014 were selected based on relevant literature and were entered into the CaRT as measured at baseline in 2012: age, sex, body mass index, multimorbidity, cognitive deficit, depression, number of falls in the past 12 months, fear of falling (FoF) categories, and timed chair-rises, balance, and gait. RESULTS:The 1-year prevalence of falls in 2014 was 26.9%. CaRT procedure identified 3 subgroups based on reported number of falls in 2012 (none, 1, ≥2). The 2014 prevalence of falls in these 3 subgroups was 20%, 30%, and 50%, respectively. The "no fall" subgroup was split using FoF: 30% of the high FoF category (score >27) vs 20% of low and moderate FoF categories (scores: 16-27) experienced a fall in 2014. Those with multiple falls were split by their speed in the chair-rise test: 56% of the slow category (>16.7 seconds) and the fast category (<11.2 seconds) had falls vs 28% in the intermediate group (between 11.2 and 16.7 seconds). No additional variables entered into the decision tree. CONCLUSIONS:Three simple indicators: FoF, number of previous falls, and time of chair rise could identify those with more than 50% probability of falling.
10.1016/j.jamda.2017.03.021
Falls in the elderly were predicted opportunistically using a decision tree and systematically using a database-driven screening tool.
Rafiq Meena,McGovern Andrew,Jones Simon,Harris Kevin,Tomson Charles,Gallagher Hugh,de Lusignan Simon
Journal of clinical epidemiology
OBJECTIVE:To identify risk factors for falls and generate two screening tools: an opportunistic tool for use in consultation to flag at risk patients and a systematic database screening tool for comprehensive falls assessment of the practice population. STUDY DESIGN AND SETTING:This multicenter cohort study was part of the quality improvement in chronic kidney disease trial. Routine data for participants aged 65 years and above were collected from 127 general practice (GP) databases across the UK, including sociodemographic, physical, diagnostic, pharmaceutical, lifestyle factors, and records of falls or fractures over 5 years. Multilevel logistic regression analyses were performed to identify predictors. The strongest predictors were used to generate a decision tree and risk score. RESULTS:Of the 135,433 individuals included, 10,766 (8%) experienced a fall or fracture during follow-up. Age, female sex, previous fall, nocturia, anti-depressant use, and urinary incontinence were the strongest predictors from our risk profile (area under the receiver operating characteristics curve = 0.72). Medication for hypertension did not increase the falls risk. Females aged over 75 years and subjects with a previous fall were the highest risk groups from the decision tree. The risk profile was converted into a risk score (range -7 to 56). Using a cut-off of ≥9, sensitivity was 68%, and specificity was 60%. CONCLUSION:Our study developed opportunistic and systematic tools to predict falls without additional mobility assessments.
10.1016/j.jclinepi.2014.03.008
Fall Risk Assessment Through Automatic Combination of Clinical Fall Risk Factors and Body-Worn Sensor Data.
Greene Barry R,Redmond Stephen J,Caulfield Brian
IEEE journal of biomedical and health informatics
Falls are the leading global cause of accidental death and disability in older adults and are the most common cause of injury and hospitalization. Accurate, early identification of patients at risk of falling, could lead to timely intervention and a reduction in the incidence of fall-related injury and associated costs. We report a statistical method for fall risk assessment using standard clinical fall risk factors (N = 748). We also report a means of improving this method by automatically combining it, with a fall risk assessment algorithm based on inertial sensor data and the timed-up-and-go test. Furthermore, we provide validation data on the sensor-based fall risk assessment method using a statistically independent dataset. Results obtained using cross-validation on a sample of 292 community dwelling older adults suggest that a combined clinical and sensor-based approach yields a classification accuracy of 76.0%, compared to either 73.6% for sensor-based assessment alone, or 68.8% for clinical risk factors alone. Increasing the cohort size by adding an additional 130 subjects from a separate recruitment wave (N = 422), and applying the same model building and validation method, resulted in a decrease in classification performance (68.5% for combined classifier, 66.8% for sensor data alone, and 58.5% for clinical data alone). This suggests that heterogeneity between cohorts may be a major challenge when attempting to develop fall risk assessment algorithms which generalize well. Independent validation of the sensor-based fall risk assessment algorithm on an independent cohort of 22 community dwelling older adults yielded a classification accuracy of 72.7%. Results suggest that the present method compares well to previously reported sensor-based fall risk assessment methods in assessing falls risk. Implementation of objective fall risk assessment methods on a large scale has the potential to improve quality of care and lead to a reduction in associated hospital costs, due to fewer admissions and reduced injuries due to falling.
10.1109/JBHI.2016.2539098
Fusion of Clinical, Self-Reported, and Multisensor Data for Predicting Falls.
Silva Joana,Sousa Ines,Cardoso Jaime S
IEEE journal of biomedical and health informatics
Falls are among the frequent causes of the loss of mobility and independence in the elderly population. Given the global population aging, new strategies for predicting falls are required to reduce the number of their occurrences. In this study, a multifactorial screening protocol was applied to 281 community-dwelling adults aged over 65, and their 12-month prospective falls were annotated. Clinical and self-reported data, along with data from instrumented functional tests, involving inertial sensors and a pressure platform, were fused using early, late, and slow fusion approaches. For the early and late fusion, a classification pipeline was designed employing stratified sampling for the generation of the training and test sets. Grid search with cross-validation was used to optimize a set of feature selectors and classifiers. According to the slow fusion approach, each data source was mixed in the middle layers of a multilayer perceptron. The three studied fusion approaches yielded similar results for the majority of the metrics. However, if recall is considered to be more important than specificity, then the result of the late fusion approach providing a recall of [Formula: see text] is better compared with the results achieved by the other two approaches.
10.1109/JBHI.2019.2951230
Risk of Falling and Associated Factors in Older Adults with a Previous History of Falls.
Pellicer-García Begoña,Antón-Solanas Isabel,Ramón-Arbués Enrique,García-Moyano Loreto,Gea-Caballero Vicente,Juárez-Vela Raúl
International journal of environmental research and public health
Falls in the elderly are one of the main geriatric syndromes and a clear indicator of fragility in the older adult population. This has serious consequences, leading to an increase in disability, institutionalization and death. The purpose of this cross-sectional study was to analyze the prevalence of risk of falling and associated factors in a population of 213 non-institutionalised, able older adults with a history of falling in the previous year. We used the following assessment tools: Questionnaire of the WHO for the study of falls in the elderly, Geriatric Depression Scale and Tinetti's Gait and Balance Assessment Tool. Age, using ambulatory assistive devices, polymedication, hospital admission following a fall and depression were significantly associated with risk of falling. In order to prevent fall reoccurrence, community-based fall prevention programs should be implemented.
10.3390/ijerph17114085
A multivariate fall risk assessment model for VHA nursing homes using the minimum data set.
French Dustin D,Werner Dennis C,Campbell Robert R,Powell-Cope Gail M,Nelson Audrey L,Rubenstein Laurence Z,Bulat Tatjana,Spehar Andrea M
Journal of the American Medical Directors Association
OBJECTIVES:The purpose of this study was to develop a multivariate fall risk assessment model beyond the current fall Resident Assessment Protocol (RAP) triggers for nursing home residents using the Minimum Data Set (MDS). DESIGN:Retrospective, clustered secondary data analysis. SETTING:National Veterans Health Administration (VHA) long-term care nursing homes (N = 136). PARTICIPANTS:The study population consisted of 6577 national VHA nursing home residents who had an annual assessment during FY 2005, identified from the MDS, as well as an earlier annual or admission assessment within a 1-year look-back period. MEASUREMENT:A dichotomous multivariate model of nursing home residents coded with a fall on selected fall risk characteristics from the MDS, estimated with general estimation equations (GEE). RESULTS:There were 17 170 assessments corresponding to 6577 long-term care nursing home residents. The increased odds ratio (OR) of being classified as a faller relative to the omitted "dependent" category of activities of daily living (ADL) ranged from OR = 1.35 for "limited" ADL category up to OR = 1.57 for "extensive-2" ADL (P < .0001). Unsteady gait more than doubles the odds of being a faller (OR = 2.63, P < .0001). The use of assistive devices such as canes, walkers, or crutches, or the use of wheelchairs increases the odds of being a faller (OR = 1.17, P < .0005) or (OR = 1.19, P < .0002), respectively. Foot problems may also increase the odds of being a faller (OR = 1.26, P < .0016). Alzheimer's or other dementias also increase the odds of being classified as a faller (OR = 1.18, P < .0219) or (OR=1.22, P < .0001), respectively. In addition, anger (OR = 1.19, P < .0065); wandering (OR = 1.53, P < .0001); or use of antipsychotic medications (OR = 1.15, P < .0039), antianxiety medications (OR = 1.13, P < .0323), or antidepressant medications (OR = 1.39, P < .0001) was also associated with the odds of being a faller. CONCLUSIONS:This national study in one of the largest managed healthcare systems in the United States has empirically confirmed the relative importance of certain risk factors for falls in long-term care settings. The model incorporated an ADL index and adjusted for case mix by including only long-term care nursing home residents. The study offers clinicians practical estimates by combining multiple univariate MDS elements in an empirically based, multivariate fall risk assessment model.
10.1016/j.jamda.2006.08.005
Predictors of falls among postmenopausal women: results from the National Osteoporosis Risk Assessment (NORA).
Barrett-Connor E,Weiss T W,McHorney C A,Miller P D,Siris E S
Osteoporosis international : a journal established as result of cooperation between the European Foundation for Osteoporosis and the National Osteoporosis Foundation of the USA
UNLABELLED:Using data from 66,134 postmenopausal women enrolled in the National Osteoporosis Risk Assessment (NORA) study, more than half of whom were less than age 65, we identified 18 risk factors that independently predicted a significantly increased risk of falling and observed a graded increase in risk with an increasing number of risk factors. INTRODUCTION:This study was designed to identify predictors of falls in a large prospective study of community-dwelling, postmenopausal women, 58% of whom were less than 65 years old at baseline. METHODS:We exclusively used survey data from 66,134 NORA participants who completed the baseline survey and three follow-up surveys over 6 years. Stepwise logistic regression was used to select potential fall predictors. A simple fall risk index was created by giving one point to each significant independent risk factor. RESULTS:More than one third (38.2%) of participants reported at least one fall since baseline. The largest predictor of fall risk was history of falls (odds ratio [OR] = 2.7). In the multivariate analysis, 17 additional risk factors were significantly associated with incident falls (but with smaller OR), including age, college education, poor hearing, diabetes, personal or family history of fracture, hypothyroidism, and height loss. Of the 3,346 women with zero fall risk factors, 22.6% reported falling compared to 84.3% of the 51 women with >or=11 risk factors. CONCLUSIONS:This large cohort had sufficient power to identify 18 risk factors that independently predicted a significantly increased risk of falling with a graded increase in risk with increasing number of risk factors.
10.1007/s00198-008-0748-2
A screening tool with five risk factors was developed for fall-risk prediction in community-dwelling elderly.
Bongue Bienvenu,Dupré Caroline,Beauchet Olivier,Rossat Arnaud,Fantino Bruno,Colvez Alain
Journal of clinical epidemiology
OBJECTIVE:To develop a simple clinical screening tool for community-dwelling older adults. STUDY DESIGN AND SETTING:A prospective multicenter cohort study was performed among healthy subjects of 65 years and older, examined in 10 health examination centers for the French health insurance. Falls were ascertained monthly by telephone for 12-month follow-up. Multivariate analyses using Cox regression models were performed. Regression coefficients of the predictors in the final model were added up to obtain the total score. The discriminative power was assessed using the area under the curve (AUC). RESULTS:Thousand seven hundred fifty-nine subjects were included. The mean age was 70.7 years and 51% were women. At least one fall occurred among 563 (32%) participants. Gender, living alone, psychoactive drug use, osteoarthritis, previous falls, and a change in the position of the arms during the one-leg balance (OLB) test were the strongest predictors. These predictors were used to build a risk score. The AUC of the score was 0.70. For a cutoff point of 1.68 in a total of 4.90, the positive predictive value and negative predictive value were 72.0% and 72.7%, respectively. CONCLUSION:A screening tool with five risk factors and the OLB test could predict falls in healthy community-dwelling older adults.
10.1016/j.jclinepi.2010.12.014
A regression tree for identifying combinations of fall risk factors associated to recurrent falling: a cross-sectional elderly population-based study.
Kabeshova A,Annweiler C,Fantino B,Philip T,Gromov V A,Launay C P,Beauchet O
Aging clinical and experimental research
BACKGROUND:Regression tree (RT) analyses are particularly adapted to explore the risk of recurrent falling according to various combinations of fall risk factors compared to logistic regression models. The aims of this study were (1) to determine which combinations of fall risk factors were associated with the occurrence of recurrent falls in older community-dwellers, and (2) to compare the efficacy of RT and multiple logistic regression model for the identification of recurrent falls. METHODS:A total of 1,760 community-dwelling volunteers (mean age ± standard deviation, 71.0 ± 5.1 years; 49.4 % female) were recruited prospectively in this cross-sectional study. Age, gender, polypharmacy, use of psychoactive drugs, fear of falling (FOF), cognitive disorders and sad mood were recorded. In addition, the history of falls within the past year was recorded using a standardized questionnaire. RESULTS:Among 1,760 participants, 19.7 % (n = 346) were recurrent fallers. The RT identified 14 nodes groups and 8 end nodes with FOF as the first major split. Among participants with FOF, those who had sad mood and polypharmacy formed the end node with the greatest OR for recurrent falls (OR = 6.06 with p < 0.001). Among participants without FOF, those who were male and not sad had the lowest OR for recurrent falls (OR = 0.25 with p < 0.001). The RT correctly classified 1,356 from 1,414 non-recurrent fallers (specificity = 95.6 %), and 65 from 346 recurrent fallers (sensitivity = 18.8 %). The overall classification accuracy was 81.0 %. The multiple logistic regression correctly classified 1,372 from 1,414 non-recurrent fallers (specificity = 97.0 %), and 61 from 346 recurrent fallers (sensitivity = 17.6 %). The overall classification accuracy was 81.4 %. CONCLUSIONS:Our results show that RT may identify specific combinations of risk factors for recurrent falls, the combination most associated with recurrent falls involving FOF, sad mood and polypharmacy. The FOF emerged as the risk factor strongly associated with recurrent falls. In addition, RT and multiple logistic regression were not sensitive enough to identify the majority of recurrent fallers but appeared efficient in detecting individuals not at risk of recurrent falls.
10.1007/s40520-014-0232-0