TY  - JOUR
PY  - 2021//
TI  - A self-reported clinical tool predicts falls in people with Parkinson's disease
JO  - Movement disorders clinical practice
A1  - Almeida, Lorena Rosa S.
A1  - Piemonte, Maria Elisa Pimentel
A1  - Cavalcanti, Helen M.
A1  - Canning, Colleen G.
A1  - Paul, Serene S.
SP  - 427
EP  - 434
VL  - 8
IS  - 3
N2  - 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. <br><br>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. <br><br>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 (ICC(2,1)) 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). <br><br>RESULTS: Forty-two participants (31%) reported ≥1 fall during follow-up. The 3-step self-reported tool had an ICC(2,1) 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. <br><br>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.<p />  <p>Language: en</p>
LA  - en
SN  - 2330-1619
UR  - http://dx.doi.org/10.1002/mdc3.13170
ID  - ref1
ER  -