Peer Reviewed

1

Document Type

Article

Publication Date

18-1-2017

Keywords

Accidental Falls, Older People, Risk Prediction, Stroke

Funder/Sponsor

This work was supported by the Irish Research Council (Government of Ireland Postgraduate Scholarship Scheme 2013).

Comments

This is a pre-copyedited, author-produced version of an article accepted for publication in Age and Aging following peer review. The version of record Walsh ME, Galvin R, Boland F, Williams D, Harbison JA, Murphy S, Collins R, Crowe M, McCabe JH, Horgan F. Validation of two risk-prediction models for recurrent falls in the first year after stroke: a prospective cohort study. Age and Ageing. 2017 Jan 18. [Epub ahead of print is available online at: https://academic.oup.com/ageing/article-lookup/doi/10.1093/ageing/afw255.

Abstract

BACKGROUND: 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 6 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 6-month outcome. Model 2, incorporating inpatient near-falls history and upper limb function, had a 12-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 6 and 12 months, respectively. Seventeen and 28 participants experienced recurrent falls by these respective time points. Model 1 achieved an AUC = 0.56 (95% CI 0.46-0.67), sensitivity = 18.8% and specificity = 93.6%. Model 2 achieved AUC = 0.55 (95% CI 0.44-0.66), sensitivity = 51.9% and specificity = 58.7%. Model 1 showed no significant difference between predicted and observed events (risk ratio (RR) = 0.87, 95% CI 0.16-4.62). In contrast, model 2 significantly over-predicted fall events in the validation cohort (RR = 1.61, 95% CI 1.04-2.48).

CONCLUSIONS: both models showed poor discrimination for predicting recurrent falls. A further large prospective cohort study would be required to derive a clinically useful falls-risk prediction model for a similar population.

Disciplines

Medicine and Health Sciences | Rehabilitation and Therapy

Citation

Walsh ME, Galvin R, Boland F, Williams D, Harbison JA, Murphy S, Collins R, Crowe M, McCabe JH, Horgan F. Validation of two risk-prediction models for recurrent falls in the first year after stroke: a prospective cohort study. Age and Ageing. 2017 Jan 18. [Epub ahead of print]

PubMed ID

28104593

DOI Link

10.1093/ageing/afw255

Creative Commons License

Creative Commons License
This work is licensed under a Creative Commons Attribution-Noncommercial-Share Alike 4.0 License.

Available for download on Thursday, January 18, 2018

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