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

Lindholm, Beata; Nilsson, Maria H; Hansson, Oskar; Hagell, Peter

doi:10.1007/s00415-016-8287-9

Cited by 28 publications

(44 citation statements)

References 43 publications

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“…It is also noteworthy that trial 1 values and mean (trials 1 and 2) values of both static and dynamic start identified 1.1 m/s as an optimal cut-off point. This finding is in line with the suggested cut-off point for comfortable gait speed as a predictor in the 3-step model [2, 3]. However, while the 3-step model prescribes the use of the mean of two trials, our observations suggest that a single trial with static start will suffice.…”

Section: Discussionsupporting

confidence: 87%

“…Furthermore, comfortable gait speed < 1.1 m per second (m/s) has been suggested as an important predictor of future falls in PD [2–5]. However, there are different standardizations for the conduct of the 10MWT, for example, measuring over different distances (10 or 6 m) and the inclusion or exclusion of an acceleration distance, i.e., dynamic vs. static start [1–3, 6]. …”

Section: Introductionmentioning

confidence: 99%

“…Therefore, it is common to perform multiple trials and use the mean of these as the test result [8, 9]. For example, the 3-step falls prediction model (3-step model) prescribes the use of the mean value of two trials [2, 3]. However, it is unclear to what extent different 10MWT standardizations impact test outcomes and interpretations.…”

Section: Introductionmentioning

confidence: 99%

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The clinical significance of 10-m walk test standardizations in Parkinson’s disease

et al. 2018

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BackgroundThe 10-m walk test (10MWT) is a widely used measure of gait speed in Parkinson’s disease (PD). However, it is unclear if different standardizations of its conduct impact test results.Aim of the studyWe examined the clinical significance of two aspects of the standardization of the 10MWT in mild PD: static vs. dynamic start, and a single vs. repeated trials. Implications for fall prediction were also explored.Methods151 people with PD (mean age and PD duration, 68 and 4 years, respectively) completed the 10MWT in comfortable gait speed with static and dynamic start (two trials each), and gait speed (m/s) was recorded. Participants then registered all prospective falls for 6 months.ResultsAbsolute mean differences between outcomes from the various test conditions ranged between 0.016 and 0.040 m/s (effect sizes, 0.06–0.14) with high levels of agreement (intra-class correlation coefficients, 0.932–0.987) and small standard errors of measurement (0.032–0.076 m/s). Receiver operating characteristic curves showed similar discriminate abilities for prediction of future falls across conditions (areas under curves, 0.70–0.73). Cut-off points were estimated at 1.1–1.2 m/s.ConclusionsDifferent 10MWT standardizations yield very similar results, suggesting that there is no practical need for an acceleration distance or repeated trials when conducting this test in mild PD.

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Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

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The clinical significance of 10-m walk test standardizations in Parkinson’s disease

et al. 2018

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“…7 • Nutt Retropulsion Test (NRT) as a test of reactive balance responses (retropulsion) due to unexpected shoulder pull (score range 0-3). Those scoring ≥1 were categorized as having an abnormal TG performance.…”

Section: Procedures and Instrumentsmentioning

confidence: 99%

“…7 To follow previous findings and approaches, 11 individuals with global cognitive impairment were excluded from the development sample. 7 To follow previous findings and approaches, 11 individuals with global cognitive impairment were excluded from the development sample.…”

Section: Introductionmentioning

confidence: 99%

Dyskinesia and FAB score predict future falling in Parkinson's disease

et al. 2019

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Parkinson's disease (PD) is the world's second most common agerelated neurodegenerative disorder. Given the tendency for longer life expectancy and that the risk of PD increases with age, the number of people with PD is expected to double by the year 2030. 1 In PD, degeneration of the dopaminergic pathways results in striatal dopamine deficiency. The cardinal signs of disease are as follows:bradykinesia, muscle rigidity, resting tremor, and postural instability.In addition to these classic motor symptoms, non-motor features (eg, cognitive impairment, dizziness, and orthostatic hypotension) also occur. The core and first-choice therapy is dopaminergic medication (levodopa) that targets the neurotransmitter imbalance within the basal ganglia circuits. Complications of therapy such as dyskinesia (involuntary movements) and "off" periods (fluctuating drug response) often develop over time. 2 Falls and balance problems are common already in the early stages of disease 3,4 and progress over time. 5 A history of falls is the strongest predictor of future falls in PD. 6 However, it was suggested that prediction of both near falls and falls has greater clinical value than prediction of falls alone if you aim at working proactively. 7 Furthermore, fall prediction models, in order to be applicable, useful, and practical for routine clinical use, need to have sufficient discriminant ability 8,9 and must be easy to implement. 10 To this A growing body of research highlights the importance of cognition for prediction of falls in Parkinson's disease (PD). However, a previously proposed prediction model for future near falls and falls in PD, which includes history of near falls, tandem gait, and retropulsion, was developed without considering cognitive impairment.

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A Self‐Reported Clinical Tool Predicts Falls in People with Parkinson's Disease

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Cavalcanti

et al. 2021

Movement Disord Clin Pract

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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.

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

Cited by 28 publications

References 43 publications

The clinical significance of 10-m walk test standardizations in Parkinson’s disease

The clinical significance of 10-m walk test standardizations in Parkinson’s disease

Dyskinesia and FAB score predict future falling in Parkinson's disease

A Self‐Reported Clinical Tool Predicts Falls in People with Parkinson's Disease

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