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

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...

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Bibliographic Details
Published in:Age and ageing 2017-07, Vol.46 (4), p.642-648
Main Authors: Walsh, Mary E, Galvin, Rose, Boland, Fiona, Williams, David, Harbison, Joseph A, Murphy, Sean, Collins, Ronan, Crowe, Morgan, McCabe, Dominick J H, Horgan, Frances
Format: Article
Language:English
Subjects:
Accidental Falls
Aged
Aged, 80 and over
Area Under Curve
Causes of
Decision Support Techniques
Disability Evaluation
Falls (Accidents)
Female
Hospitals, Teaching
Humans
Inpatients
Ireland
Male
Middle Aged
Multivariate Analysis
Odds Ratio
Postural Balance
Predictive Value of Tests
Prognosis
Prospective Studies
Recurrence
Risk Assessment
Risk Factors
ROC Curve
Statistical models
Stroke
Stroke (Disease)
Stroke - complications
Stroke - diagnosis
Stroke - physiopathology
Time Factors
Upper Extremity - innervation
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Summary: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. 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. 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). 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.
ISSN:0002-0729
1468-2834
DOI:10.1093/ageing/afw255