Lorena Rosa S Almeida1,2, Maria Elisa Pimentel Piemonte3, Helen M Cavalcanti2,4,5, Colleen G Canning6, Serene S Paul6. 1. Movement Disorders and Parkinson's Disease Clinic Roberto Santos General Hospital Salvador Brazil. 2. Motor Behavior and Neurorehabilitation Research Group Bahiana School of Medicine and Public Health Salvador Brazil. 3. Physical Therapy, Speech Therapy and Occupational Therapy Department Faculty of Medicine of University of São Paulo São Paulo Brazil. 4. Postgraduate Program in Health Sciences Federal University of Bahia School of Medicine Salvador Brazil. 5. Bahia Adventist College Cachoeira Brazil. 6. Discipline of Movement Sciences, Sydney School of Health Sciences, Faculty of Medicine and Health The University of Sydney New South Wales Australia.
Abstract
BACKGROUND: A 3-step clinical prediction tool including falling in the previous year, freezing of gait in the past month and self-selected gait speed <1.1 m/s has shown high accuracy in predicting falls in people with Parkinson's disease (PD). The accuracy of this tool when including only self-report measures is yet to be determined. OBJECTIVES: To validate the 3-step prediction tool using only self-report measures (3-step self-reported prediction tool), and to externally validate the 3-step clinical prediction tool. METHODS: The clinical tool was used with 137 individuals with PD. Participants also answered a question about self-reported gait speed, enabling scoring of the self-reported tool, and were followed-up for 6 months. An intraclass correlation coefficient (ICC2,1) was calculated to evaluate test-retest reliability of the 3-step self-reported prediction tool. Multivariate logistic regression models were used to evaluate the performance of both tools and their discriminative ability was determined using the area under the curve (AUC). RESULTS: Forty-two participants (31%) reported ≥1 fall during follow-up. The 3-step self-reported tool had an ICC2,1 of 0.991 (95% CI 0.971-0.997; P < 0.001) and AUC = 0.68; 95% CI 0.59-0.77, while the 3-step clinical tool had an AUC = 0.69; 95% CI 0.60-0.78. CONCLUSIONS: The 3-step self-reported prediction tool showed excellent test-retest reliability and was validated with acceptable accuracy in predicting falls in the next 6 months. The 3-step clinical prediction tool was externally validated with similar accuracy. The 3-step self-reported prediction tool may be useful to identify people with PD at risk of falls in e/tele-health settings.
BACKGROUND: A 3-step clinical prediction tool including falling in the previous year, freezing of gait in the past month and self-selected gait speed <1.1 m/s has shown high accuracy in predicting falls in people with Parkinson's disease (PD). The accuracy of this tool when including only self-report measures is yet to be determined. OBJECTIVES: To validate the 3-step prediction tool using only self-report measures (3-step self-reported prediction tool), and to externally validate the 3-step clinical prediction tool. METHODS: The clinical tool was used with 137 individuals with PD. Participants also answered a question about self-reported gait speed, enabling scoring of the self-reported tool, and were followed-up for 6 months. An intraclass correlation coefficient (ICC2,1) was calculated to evaluate test-retest reliability of the 3-step self-reported prediction tool. Multivariate logistic regression models were used to evaluate the performance of both tools and their discriminative ability was determined using the area under the curve (AUC). RESULTS: Forty-two participants (31%) reported ≥1 fall during follow-up. The 3-step self-reported tool had an ICC2,1 of 0.991 (95% CI 0.971-0.997; P < 0.001) and AUC = 0.68; 95% CI 0.59-0.77, while the 3-step clinical tool had an AUC = 0.69; 95% CI 0.60-0.78. CONCLUSIONS: The 3-step self-reported prediction tool showed excellent test-retest reliability and was validated with acceptable accuracy in predicting falls in the next 6 months. The 3-step clinical prediction tool was externally validated with similar accuracy. The 3-step self-reported prediction tool may be useful to identify people with PD at risk of falls in e/tele-health settings.
Authors: Ryan 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 Journal: Parkinsonism Relat Disord Date: 2015-05-16 Impact factor: 4.891
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