Systematic review of risk prediction models for falls after stroke

Walsh, Mary E.; Horgan, Frances; Walsh, Cathal; Galvin, Rose

doi:10.1136/jech-2015-206475

Cited by 40 publications

(29 citation statements)

References 42 publications

(122 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Professionals' focus on "risk" may have implications for the uptake of these interventions [36]. It must also be noted that objective "falls-risk" post stroke remains an ill-defined concept as much quantitative research has found that falls are difficult events to predict [9]. Further qualitative research could focus on how stroke survivors conceptualise the consequences of falls, and how they self-assess risk, in order to inform the development of self-management interventions.…”

Section: Discussionmentioning

confidence: 99%

“…Fallers also demonstrate higher levels of anxiety, depressed mood and fear [6,7]. Much quantitative research has focused on the identification of modifiable risk factors for falls among this population, and the ability to predict fall events in stroke survivors returning home from hospital or rehabilitation [8,9]. Stroke survivors' own experiences of falls-risk and fall events are less well understood [10].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Fall-related experiences of stroke survivors: a meta-ethnography

Walsh

Galvin

Horgan

2016

Disability and Rehabilitation

Self Cite

View full text Add to dashboard Cite

200Main manuscript: 4806 Number of tables: 4 Number of figures: 2 3Implications for Rehabilitation  Health care professionals should recognise that cognitive and emotional adjustment may be required for stroke survivors to accept strategies for overcoming falls-risk, including dependence on carers and assistive devices. Several factors in addition to physical interventions may be needed to minimise fallsrisk while increasing activity participation. These factors could include increasing public awareness about the effects of stroke and falls-risk, and ensuring access to psychological services for stroke survivors. Rehabilitation professionals should reflect on whether they perceive there to be an appropriate level of fear of falling post stroke.

show abstract

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Fall-related experiences of stroke survivors: a meta-ethnography

Walsh

Galvin

Horgan

2016

Disability and Rehabilitation

Self Cite

View full text Add to dashboard Cite

show abstract

“…Of the two models derived by Mackintosh et al [9], the model including the BBS was chosen as this measure is commonly included in post-stroke studies investigating falls [6]. The model derived by Ashburn et al [11] combining two predictors was selected, as their study, with 48 events, was underpowered for the derivation of their six-item model [6,13].…”

Section: Methodsmentioning

confidence: 99%

“…As falls are complex, risk-prediction models should include more predictors [27]. The most common falls predictors entered into multi-variable models in post-stroke cohort studies are measures of neglect, gait speed, cognition, depression, falls-efficacy, the BBS and the Timed Up and Go test [6]. A derivation study with seven candidate predictors would require 70 events (an estimated sample size of approximately 400 participants based on the current study) for sufficient power to predict recurrent fall events [13].…”

Section: Future Implicationsmentioning

confidence: 99%

“…Before widespread implementation, risk-prediction models should undergo three development stages: (i) Derivation: identification of prognostic factors to develop the model; (ii) Validation: testing of the model in a similar population (internal validation) and/or in a different population (external validation) (iii) Impact analysis: evaluation of the effect on patient outcomes, clinician behaviour or costs [5]. A recent systematic review summarised falls-prediction models derived within the first year after stroke [6]. Five studies derived nine models to predict falls in community-dwellers [7][8][9][10][11].…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Validation of two risk-prediction models for recurrent falls in the first year after stroke: a prospective cohort study

et al. 2017

Self Cite

View full text Add to dashboard Cite

Key Points:• Accurate identification of stroke patients at risk of falling is important for planning rehabilitation services on discharge• Two previously derived risk-prediction models for recurrent falls post-stroke have performed poorly in this validation study• A further large prospective cohort study is required to derive a useful falls-risk prediction model for this population• Future derivation studies should conduct internal validation and adhere to TRIPOD guidelines to facilitate external validation 3 AbstractBackground: Several multivariable models have been derived to predict post-stroke falls. These require validation before integration into clinical practice. The aim of this study was to externally validate two prediction models for recurrent falls in the first year post-stroke using an Irish prospective cohort study.Methodology: Stroke patients with planned home-discharges from five hospitals were recruited. Falls were recorded with monthly diaries and interviews six and 12 months post-discharge. Predictors for falls included in two risk-prediction models were assessed at discharge. Participants were classified into risk-groups using these models. Model 1, incorporating inpatient falls-history and balance, had a six-month outcome. Model 2, incorporating inpatient near-falls history and upper limb function, had a twelve-month outcome. Measures of calibration, discrimination (area under the curve (AUC)) and clinical utility (sensitivity/ specificity) were calculated.Results: 128 participants (mean age=68.6 years, SD=13.3) were recruited. The fall status of 117 and 110 participants was available at six and 12 months respectively.Seventeen and 28 participants experienced recurrent falls by these respective time- Conclusions: Both models showed poor discrimination for predicting recurrent falls.A further large prospective cohort study would be required to derive a clinicallyuseful falls-risk prediction model for a similar population.

show abstract

Standardized Outcome Measures in Stroke Rehabilitation and Falls After Discharge: A Cohort Study

Campo

Toglia

Batistick-Aufox

et al. 2020

PM&R

View full text Add to dashboard Cite

Introduction Individuals with stroke discharged from inpatient rehabilitation units (IRUs) are at increased risk for falls. In IRUs, standardized outcome measures (SOMs) have been used to predict falls, but the results have been mixed. Objective To examine the relationship between SOMs and the risk of falls in individuals with stroke within 6 months of discharge from an IRU. Design Prospective cohort study with 6‐month follow‐up. Setting IRU that was part of a large, urban academic medical center. Participants Individuals with stroke who underwent rehabilitation. Main Outcome Measures Self‐reported falls within 6 months of discharge. Results The study included 105 participants who were discharged to their homes after inpatient rehabilitation and who responded to a 6‐month follow‐up (57% response rate) phone call. Twenty‐nine participants (28%) reported falling. Significant odds ratios (ORs), adjusted for age, sex, and stroke severity, were found for the following measures: Berg Balance Scale (OR 0.95, 95% confidence interval [CI] 0.92‐0.99), Activity Measure for Post‐Acute Care basic mobility (OR 0.89, 95% CI 0.81‐0.97), Motricity Index (OR 0.96, 95% CI 0.94‐0.98), Functional Independence Measure mobility subscale (OR 0.89, 95% CI 0.80‐0.98), and Trunk Control Test (OR 0.97, 95% CI 0.95‐0.99). Areas under the curve ranged from .64 to .71. In samples of 82 to 90 patients who could complete the tests, gait speed, the Functional Reach Test, the 6‐minute Walk Test, and Timed Up and Go did not result in significant ORs. Conclusions At discharge, SOMs were associated with the odds of falls within 6 months. The multifactorial nature of falls will continue to make prediction challenging but SOMs can be helpful. Lower extremity strength deserves more attention as a risk factor.

show abstract

Systematic review of risk prediction models for falls after stroke

Cited by 40 publications

References 42 publications

Fall-related experiences of stroke survivors: a meta-ethnography

Fall-related experiences of stroke survivors: a meta-ethnography

Validation of two risk-prediction models for recurrent falls in the first year after stroke: a prospective cohort study

Standardized Outcome Measures in Stroke Rehabilitation and Falls After Discharge: A Cohort Study

Contact Info

Product

Resources

About