Predictors of falls in a high risk population: results from the prevention of falls in the elderly trial (PROFET)

Close, Jacqueline C. T.; Hooper, Richard; Glucksman, E; Jackson, Stephen; Swift, Cameron

doi:10.1136/emj.20.5.421

Cited by 83 publications

(45 citation statements)

References 21 publications

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“…An unplanned readmission cannot be foreseen at the time of discharge and may occur on a nonemergent or emergent basis. 104 Third, various geriatric syndromes complicate the post-ED management of the vulnerable elder, including delirium, 105 standing-level falls, 106 and dementia. 107 For example, if assessed in non-medicated, non-critically ill older adults, researchers note abnormal cognitive function in 35% of older ED patients.…”

Section: Discussionmentioning

confidence: 99%

Risk Factors and Screening Instruments to Predict Adverse Outcomes for Undifferentiated Older Emergency Department Patients: A Systematic Review and Meta‐analysis

Carpenter

Shelton

Fowler

et al. 2015

Academic Emergency Medicine

253

213

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Objectives: A significant proportion of geriatric patients experience suboptimal outcomes following episodes of emergency department (ED) care. Risk stratification screening instruments exist to distinguish vulnerable subsets, but their prognostic accuracy varies. This systematic review quantifies the prognostic accuracy of individual risk factors and ED-validated screening instruments to distinguish patients more or less likely to experience short-term adverse outcomes like unanticipated ED returns, hospital readmissions, functional decline, or death.Methods: A medical librarian and two emergency physicians conducted a medical literature search of PubMed, EMBASE, SCOPUS, CENTRAL, and ClinicalTrials.gov using numerous combinations of search terms, including emergency medical services, risk stratification, geriatric, and multiple related MeSH terms in hundreds of combinations. Two authors hand-searched relevant specialty society research abstracts. Two physicians independently reviewed all abstracts and used the revised Quality Assessment of Diagnostic Accuracy Studies instrument to assess individual study quality. When two or more qualitatively similar studies were identified, meta-analysis was conducted using Meta-DiSc software. Primary outcomes were sensitivity, specificity, positive likelihood ratio (LR+), and negative likelihood ratio (LR-) for predictors of adverse outcomes at 1 to 12 months after the ED encounters. A hypothetical testtreatment threshold analysis was constructed based on the meta-analytic summary estimate of prognostic accuracy for one outcome.Results: A total of 7,940 unique citations were identified yielding 34 studies for inclusion in this systematic review. Studies were significantly heterogeneous in terms of country, outcomes assessed, and the timing of post-ED outcome assessments. All studies occurred in ED settings and none used published clinical decision rule derivation methodology. Individual risk factors assessed included dementia, delirium, age, dependency, malnutrition, pressure sore risk, and self-rated health. None of these risk factors significantly increased the risk of adverse outcome (LR+ range = 0.78 to 2.84). The absence of dependency reduces the risk of 1-year mortality (LR-= 0.27) and nursing home placement (LR-= 0.27). Five constructs of frailty were evaluated, but none increased or decreased the risk of adverse outcome. Three instruments were evaluated in the meta-analysis: Identification of Seniors at Risk, Triage Risk Screening Tool, and Variables Indicative of Placement Risk. None of these instruments significantly increased (LR+ range for various outcomes = 0.98 to 1.40) or decreased (LR-range = 0.53 to 1.11) the risk of adverse outcomes. The test threshold for 3-month functional decline based on the most accurate instrument was 42%, and the treatment threshold was 61%.Conclusions: Risk stratification of geriatric adults following ED care is limited by the lack of pragmatic, accurate, and reliable instruments. Although absence of dependency reduces the r...

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Section: Discussionmentioning

confidence: 99%

Risk Factors and Screening Instruments to Predict Adverse Outcomes for Undifferentiated Older Emergency Department Patients: A Systematic Review and Meta‐analysis

Carpenter

Shelton

Fowler

et al. 2015

Academic Emergency Medicine

253

213

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“…• A screening tool based on the PROFET study enables people attending the Emergency Department of a Hospital to be identified as high risk (73).…”

Section: Assessment Of Riskmentioning

confidence: 99%

Analysis of Fall Injuries by Body Mass Index

Ren¹,

Waclawczyk²,

Hartfield³

et al. 2014

Southern Medical Journal

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This report is HEN's response to a question from a decision-maker. It provides a synthesis of the best available evidence, including a summary of the main findings and policy options related to the issue. Fall prevention programmes can be effective in reducing the number of people who fall and the rate of falls. Targeted strategies aimed at behavioural change and risk modification for those living in the community appear to be most promising. Multifactorial intervention programmes that include risk factor assessment and screening have been shown to be effective. However, no screening tools have been rigorously validated across countries and further work is needed in this area.HEN, initiated and coordinated by the WHO Regional Office for Europe, is an information service for public health and health care decision-makers in the WHO European Region. Other interested parties might also benefit from HEN.

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“…Intrinsic factors are believed having dominant effect on fall risk, and they have been closely studied in the reported researches [92][93][94][95][96][97][98][99][100][101][102][103][104]; in contrast, extrinsic factors have been rarely studied, probably due to their even greater diversities and complexities.…”

Section: Biomechanical Variablesmentioning

confidence: 99%

“…On the other hand, a clinical tool for assessing fall risk is required to be reliable, accurate, simple to use, and applicable to all situations. A number of tools have been developed to screen high-risk individuals based on data collected from either community or residential care facilities [92][93][94][95][96][97][98][99][100][101][102][103][104]. The basic idea is that evidence of fall risk in an individual can be observed from his activities of daily living.…”

Section: Biomechanical Variables Determining Hip Fracture Riskmentioning

confidence: 99%

A biomechanical sorting of clinical risk factors affecting osteoporotic hip fracture

Luo

2015

Osteoporos Int

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Osteoporotic fracture has been found associated with many clinical risk factors, and the associations have been explored dominantly by evidence-based and case-control approaches. The major challenges emerging from the studies are the large number of the risk factors, the difficulty in quantification, the incomplete list, and the interdependence of the risk factors. A biomechanical sorting of the risk factors may shed lights on resolving the above issues. Based on the definition of load-strength ratio (LSR), we first identified the four biomechanical variables determining fracture risk, i.e., the risk of fall, impact force, bone quality, and bone geometry. Then, we explored the links between the FRAX clinical risk factors and the biomechanical variables by looking for evidences in the literature. To accurately assess fracture risk, none of the four biomechanical variables can be ignored and their values must be subject-specific. A clinical risk factor contributes to osteoporotic fracture by affecting one or more of the biomechanical variables. A biomechanical variable represents the integral effect from all the clinical risk factors linked to the variable. The clinical risk factors in FRAX mostly stand for bone quality. The other three biomechanical variables are not adequately represented by the clinical risk factors. From the biomechanical viewpoint, most clinical risk factors are interdependent to each other as they affect the same biomechanical variable(s). As biomechanical variables must be expressed in numbers before their use in calculating LSR, the numerical value of a biomechanical variable can be used as a gauge of the linked clinical risk factors to measure their integral effect on fracture risk, which may be more efficient than to study each individual risk factor.

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Predictors of falls in a high risk population: results from the prevention of falls in the elderly trial (PROFET)

Cited by 83 publications

References 21 publications

Risk Factors and Screening Instruments to Predict Adverse Outcomes for Undifferentiated Older Emergency Department Patients: A Systematic Review and Meta‐analysis

Risk Factors and Screening Instruments to Predict Adverse Outcomes for Undifferentiated Older Emergency Department Patients: A Systematic Review and Meta‐analysis

Analysis of Fall Injuries by Body Mass Index

A biomechanical sorting of clinical risk factors affecting osteoporotic hip fracture

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