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Validation of two risk prediction models for recurrent falls in the first year after stroke: A prospective cohort study
MCCABE, DOMINICK; BOLAND, FRANCIS; HARBISON, JOSEPH; Walsh, Mary E.; Galvin, Rose; Williams, David; Murphy, Sean; Collins, Ronan; Crowe, Morgan; Horgan, Frances
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.
Keyword(s): Risk prediction; Accidental falls; Stroke; Older people
Publication Date:
Type: Journal article
Peer-Reviewed: Yes
Language(s): English
Institution: Trinity College Dublin
Citation(s): Walsh, M.E., Galvin, R., Boland, F., Williams, D., Harbison, J.A., Murphy, S., Collins, D.R., McCabe, D.J.H., Crowe, M. & Horgan, N.F., Validation of two risk prediction models for recurrent falls in the first year after stroke: A prospective cohort study, Age and Ageing, 2017, 1-6
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First Indexed: 2020-04-08 08:23:26 Last Updated: 2020-10-30 07:32:54