External validation of a 3-step falls prediction model in mild Parkinson’s disease

Research output: Contribution to journalArticle


The 3-step falls prediction model (3-step model) that include history of falls, history of freezing of gait and comfortable gait speed <1.1 m/s was suggested as a clinical fall prediction tool in Parkinson’s disease (PD). We aimed to externally validate this model as well as to explore the value of additional predictors in 138 individuals with relatively mild PD. We found the discriminative ability of the 3-step model in identifying fallers to be comparable to previously studies [area under curve (AUC), 0.74; 95 % CI 0.65–0.84] and to be better than that of single predictors (AUC, 0.61–0.69). Extended analyses generated a new model for prediction of falls and near falls (AUC, 0.82; 95 % CI 0.75–0.89) including history of near falls, retropulsion according to the Nutt Retropulsion test (NRT) and tandem gait (TG). This study confirms the value of the 3-step model as a clinical falls prediction tool in relatively mild PD and illustrates that it outperforms the use of single predictors. However, to improve future outcomes, further studies are needed to firmly establish a scoring system and risk categories based on this model. The influence of methodological aspects of data collection also needs to be scrutinized. A new model for prediction of falls and near falls, including history of near falls, TG and retropulsion (NRT) may be considered as an alternative to the 3-step model, but needs to be tested in additional samples before being recommended. Taken together, our observations provide important additions to the evidence base for clinical fall prediction in PD.


External organisations
  • Skåne University Hospital
  • Kristianstad University
Research areas and keywords

Subject classification (UKÄ) – MANDATORY

  • Neurology


  • Balance, Falls, Gait, Near falls, Parkinon disease, Prediction
Original languageEnglish
Pages (from-to)2462-2469
JournalJournal of Neurology
Issue number12
Early online date2016 Sep 19
Publication statusPublished - 2016 Dec
Publication categoryResearch

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