Duncan, Ryan P.Cavanaugh, James T.Earhart, Gammon M.Ellis, Terry D.Ford, Matthew P.Foreman, K. BoLeddy, Abigail L.Paul, Serene S.Canning, Colleen G.Thackeray, AnneDibble, Leland E.2018-08-222018-08-222015-08-01Ryan P Duncan, James T Cavanaugh, Gammon M Earhart, Terry D Ellis, Matthew P Ford, K Bo Foreman, Abigail L Leddy, Serene S Paul, Colleen G Canning, Anne Thackeray, Leland E Dibble. 2015. "External validation of a simple clinical tool used to predict falls in people with Parkinson disease." Parkinsonism & Related Disorders, Volume 21, Issue 8, pp. 960 - 963 (4). https://doi.org/10.1016/j.parkreldis.2015.05.0081353-80201873-5126https://hdl.handle.net/2144/30884Published in final edited form as: Parkinsonism Relat Disord. 2015 August ; 21(8): 960–963. doi:10.1016/j.parkreldis.2015.05.008.BACKGROUND: Assessment of fall risk in an individual with Parkinson disease (PD) is a critical yet often time consuming component of patient care. Recently a simple clinical prediction tool based only on fall history in the previous year, freezing of gait in the past month, and gait velocity <1.1 m/s was developed and accurately predicted future falls in a sample of individuals with PD. METHODS: We sought to externally validate the utility of the tool by administering it to a different cohort of 171 individuals with PD. Falls were monitored prospectively for 6 months following predictor assessment. RESULTS: The tool accurately discriminated future fallers from non-fallers (area under the curve [AUC] = 0.83; 95% CI 0.76–0.89), comparable to the developmental study. CONCLUSION: The results validated the utility of the tool for allowing clinicians to quickly and accurately identify an individual's risk of an impending fall.p. 960 - 963Science & technologyLife sciences & biomedicineClinical neurologyNeurosciences & neurologyParkinson diseaseFallsFall riskFall predictionAccidental fallsAgedAged, 80 and overDecision support techniquesFemaleGait disorders, neurologicHumansLongitudinal studiesMaleMiddle agedPredictive value of testsRisk assessmentClinical sciencesCognitive scienceNeurology & neurosurgeryArticle