TY - JOUR
T1 - Longitudinal prediction of falls and near falls frequencies in Parkinson's disease
T2 - a prospective cohort study
AU - Lindholm, Beata
AU - Brogårdh, Christina
AU - Odin, Per
AU - Hagell, Peter
N1 - Funding Information:
This work was supported by grants from the Stoltz foundation (Department of Neurology and Rehabilitation Medicine, Malm?, Sk?ne University Hospital, Sweden), Sk?ne County Council's research and development foundation, the Promobilia foundation, the Swedish Parkinson Foundation, the Swedish Parkinson Academy, the Academy of Caring Sciences (Sk?ne University Hospital, Sweden), and Kristianstad University. None of the funders had any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors are grateful to all patients and caregivers for their contribution to this study. Special thanks are due to Wojciech Duzynski (M.D., Ph.D) and Eva Berg (RN) for assistance with participant selection.
Funding Information:
This work was supported by grants from the Stoltz foundation (Department of Neurology and Rehabilitation Medicine, Malmö, Skåne University Hospital, Sweden), Skåne County Council's research and development foundation, the Promobilia foundation, the Swedish Parkinson Foundation, the Swedish Parkinson Academy, the Academy of Caring Sciences (Skåne University Hospital, Sweden), and Kristianstad University. None of the funders had any role in the study design, data collection and analysis, decision to publish, or preparation of the manuscript. The authors are grateful to all patients and caregivers for their contribution to this study. Special thanks are due to Wojciech Duzynski (M.D., Ph.D) and Eva Berg (RN) for assistance with participant selection.
Publisher Copyright:
© 2020, The Author(s).
PY - 2021
Y1 - 2021
N2 - INTRODUCTION AND OBJECTIVE: Several prediction models for falls/near falls in Parkinson's disease (PD) have been proposed. However, longitudinal predictors of frequency of falls/near falls are poorly investigated. Therefore, we aimed to identify short- and long-term predictors of the number of falls/near falls in PD.METHODS: A prospective cohort of 58 persons with PD was assessed at baseline (mean age and PD duration, 65 and 3.2 years, respectively) and 3.5 years later. Potential predictors were history of falls and near falls, comfortable gait speed, freezing of gate, dyskinesia, retropulsion, tandem gait (TG), pain, and cognition (Mini-Mental State Exam, MMSE). After each assessment, the participants registered a number of falls/near falls during the following 6 months. Multivariate Poisson regression was used to identify short- and long-term predictors of a number of falls/near falls.RESULTS: Baseline median (q1-q3) motor (UPDRS) and MMSE scores were 10 (6.75-14) and 28.5 (27-29), respectively. History of falls was the only significant short-time predictor [incidence rate ratio (IRR), 15.17] for the number of falls/near falls during 6 months following baseline. Abnormal TG (IRR, 3.77) and lower MMSE scores (IRR, 1.17) were short-term predictors 3.5 years later. Abnormal TG (IRR, 7.79) and lower MMSE scores (IRR, 1.49) at baseline were long-term predictors of the number of falls/near falls 3.5 years later.CONCLUSION: Abnormal TG and MMSE scores predict the number of falls/near falls in short and long term, and may be indicative of disease progression. Our observations provide important additions to the evidence base for clinical fall prediction in PD.
AB - INTRODUCTION AND OBJECTIVE: Several prediction models for falls/near falls in Parkinson's disease (PD) have been proposed. However, longitudinal predictors of frequency of falls/near falls are poorly investigated. Therefore, we aimed to identify short- and long-term predictors of the number of falls/near falls in PD.METHODS: A prospective cohort of 58 persons with PD was assessed at baseline (mean age and PD duration, 65 and 3.2 years, respectively) and 3.5 years later. Potential predictors were history of falls and near falls, comfortable gait speed, freezing of gate, dyskinesia, retropulsion, tandem gait (TG), pain, and cognition (Mini-Mental State Exam, MMSE). After each assessment, the participants registered a number of falls/near falls during the following 6 months. Multivariate Poisson regression was used to identify short- and long-term predictors of a number of falls/near falls.RESULTS: Baseline median (q1-q3) motor (UPDRS) and MMSE scores were 10 (6.75-14) and 28.5 (27-29), respectively. History of falls was the only significant short-time predictor [incidence rate ratio (IRR), 15.17] for the number of falls/near falls during 6 months following baseline. Abnormal TG (IRR, 3.77) and lower MMSE scores (IRR, 1.17) were short-term predictors 3.5 years later. Abnormal TG (IRR, 7.79) and lower MMSE scores (IRR, 1.49) at baseline were long-term predictors of the number of falls/near falls 3.5 years later.CONCLUSION: Abnormal TG and MMSE scores predict the number of falls/near falls in short and long term, and may be indicative of disease progression. Our observations provide important additions to the evidence base for clinical fall prediction in PD.
KW - Cognition
KW - Falls/near falls
KW - Parkinson’s disease
KW - Prediction
KW - Tandem gait
KW - Prospective Studies
KW - Humans
KW - Parkinson Disease/complications
KW - Gait Disorders, Neurologic
KW - Cohort Studies
U2 - https://doi.org/10.1007/s00415-020-10234-6
DO - https://doi.org/10.1007/s00415-020-10234-6
M3 - Article
C2 - 32970193
VL - 268
SP - 997
EP - 1005
JO - Journal of Neurology
JF - Journal of Neurology
SN - 0340-5354
IS - 3
ER -