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Systematic review of risk prediction models for falls after stroke
  1. Mary E Walsh1,
  2. N Frances Horgan1,
  3. Cathal D Walsh2,
  4. Rose Galvin3
  1. 1School of Physiotherapy, Royal College of Surgeons in Ireland, Dublin, Ireland
  2. 2Department of Mathematics and Statistics, College of Science and Engineering, University of Limerick, Limerick, Ireland
  3. 3Discipline of Physiotherapy, Faculty of Education and Health Sciences, Department of Clinical Therapies, University of Limerick, Limerick, Ireland
  1. Correspondence to Mary E Walsh, School of Physiotherapy, Royal College of Surgeons in Ireland, 123 St. Stephen's Green, Dublin 2, Ireland; maryewalsh{at}


Background Falls are a significant cause of morbidity after stroke. The aim of this review was to identify, critically appraise and summarise risk prediction models for the occurrence of falling after stroke.

Methods A systematic literature search was conducted in December 2014 and repeated in June 2015. Studies that used multivariable analysis to build risk prediction models for falls early after stroke were included. 2 reviewers independently assessed methodological quality. Data relating to model calibration, discrimination (C-statistic) and clinical utility (sensitivity and specificity) were extracted. A narrative review of models was conducted. PROSPERO reference: CRD42014015612.

Results The 12 included articles presented 18 risk prediction models. 7 studies predicted falls among inpatients only and 5 recorded falls in the community. Methodological quality was variable. A C-statistic was reported for 7 models and values ranged from 0.62 to 0.87. Models for use in the inpatient setting most frequently included measures of hemi-inattention, while those predicting community events included falls (or near-falls) history and balance measures most commonly. Only 2 studies reported any form of validation, and none presented a validated model with acceptable performance.

Conclusions A number of falls-risk prediction models have been developed for use in the acute and subacute stages of stroke. Future research should focus on validating and improving existing models, with reference to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) guidelines to ensure quality reporting and expedite clinical implementation.


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