Predictors of Recurrent Falls in People with Parkinson’s Disease and Proposal for a Predictive Tool

Almeida, Lorena Rosa S.; Valença, Guilherme T.; Negreiros, Nádja N.; Pinto, Elen Beatriz; Oliveira‐Filho, Jamary

doi:10.3233/jpd-160934

Cited by 23 publications

(20 citation statements)

References 37 publications

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“…falling in the previous 12 months = 6, freezing of gait in the past month = 3, gait speed <1.1 m per second = 2, with the sum giving a score between 0 and 11. The probability of falling in the next 6 months was then classified as low (0), moderate (2)(3)(4)(5)(6) or high (8)(9)(10)(11). this study may be due to the similar proportion of freezers among fallers and non-fallers in this study, compared to the development sample 3 and other external validation studies who had significantly more freezers among fallers.…”

Section: Discussionmentioning

confidence: 71%

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confidence: 99%

“…Falls are a frequent complication for people with Parkinson's disease (PD), with reported incidence rates for ≥1 fall ranging from 35% to 90% and many individuals presenting with multiple falls and falls-related injuries. 1 Despite the identification of predictors of falls, including past falls; impaired balance, gait and cognition; freezing of gait (FOG) and fear of falling, [1][2][3][4] people with PD may present with different levels of fall risk and should be managed accordingly. 5 A simple 3-step clinical prediction tool has shown high accuracy (area under the receiver-operating characteristic curve [AUC] = 0.80; 95% CI, 0.73-0.86) in predicting falls in the next 6 months in people with PD, besides providing three risk categories (i.e.…”

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confidence: 99%

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

Almeida

Piemonte

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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Section: Discussionmentioning

confidence: 71%

mentioning

confidence: 99%

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confidence: 99%

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

Almeida

Piemonte

Cavalcanti

et al. 2021

Movement Disord Clin Pract

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“…Some authors have suggested an association between dyskinesia and falls 17 , whereas others failed to prove this. The same was true for wearing off, with some suggesting association 26 and others with inconclusive results 17 . In our sample, no difference was found in the MDS-UPDRS part IV, which assesses both wearing off and dyskinesia.…”

Section: Discussionmentioning

confidence: 76%

Falls in persons with Parkinson's disease: Do non-motor symptoms matter as much as motor symptoms?

Alvarado-Bolaños

Cervantes-Arriaga

Arredondo-Blanco

et al. 2019

Arq. Neuro-Psiquiatr.

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Falls are common among persons with Parkinson's disease (PD). On the other hand, predicting falls is complex as there are both generic and PD-specific contributors. In particular, the role of non-motor symptoms has been less studied. Objective: The objective of this study was to identify the role of non-motor predictors of falling in persons with PD (PwP). Methods: A cross-sectional study was carried out in PwP recruited from a movement disorders clinic. Clinical and demographical data were collected. All PwP were assessed using the Movement Disorders Society Unified Parkinson's Disease Rating Scale (MDS-UPDRS) and the Non-Motor Symptoms Scale (NMSS). Variables were assessed at the bivariate level. Significant variables were put into a logistic regression model. Results: A total of 179 PwP were included. Overall, 16.8% of PwP had fallen in the past 12 months, with 53.3% of them being recurrent fallers. The mean number of monthly falls was 2.5 ± 3.3. Factors associated with falling in the bivariate analysis included the disease duration, Hoehn and Yahr stage, MDS-UPDRS part I and II, postural instability/gait disturbance (PIGD) subtype, NMSS urinary domain, NMSS miscellaneous domain, and non-motor severity burden (all p-values < 0.05). After multivariate analysis, only the disease duration (p = 0.03) and PIGD (p = 0.03) remained as independent risk factors. Conclusion: Disease duration and the PIGD subtype were identified as relevant risk factors for falls in PwP Non-motor symptoms appear to have a less important role as risk factors for falls.

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“…Although falling increases with disease severity, falls are even common in the early stages of the condition [ 10 ]. Anti-parkinsonian drugs are the main treatment to counter the symptoms that can cause PD patients to fall, while research into finding a cure continues.…”

Section: Introductionmentioning

confidence: 99%

Adopting a multidisciplinary telemedicine intervention for fall prevention in Parkinson’s disease. Protocol for a longitudinal, randomized clinical trial

et al. 2021

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Background Approximately 40–70% of people with Parkinson’s disease (PD) fall each year, causing decreased activity levels and quality of life. Current fall-prevention strategies include the use of pharmacological and non-pharmacological therapies. To increase the accessibility of this vulnerable population, we developed a multidisciplinary telemedicine program using an Information and Communication Technology (ICT) platform. We hypothesized that the risk for falling in PD would decrease among participants receiving a multidisciplinary telemedicine intervention program added to standard office-based neurological care. Objective To determine the feasibility and cost-effectiveness of a multidisciplinary telemedicine intervention to decrease the incidence of falls in patients with PD. Methods Ongoing, longitudinal, randomized, single-blinded, case-control, clinical trial. We will include 76 non-demented patients with idiopathic PD with a high risk of falling and limited access to multidisciplinary care. The intervention group (n = 38) will receive multidisciplinary remote care in addition to standard medical care, and the control group (n = 38) standard medical care only. Nutrition, sarcopenia and frailty status, motor, non-motor symptoms, health-related quality of life, caregiver burden, falls, balance and gait disturbances, direct and non-medical costs will be assessed using validated rating scales. Results This study will provide a cost-effectiveness assessment of multidisciplinary telemedicine intervention for fall reduction in PD, in addition to standard neurological medical care. Conclusion In this challenging initiative, we will determine whether a multidisciplinary telemedicine intervention program can reduce falls, as an alternative intervention option for PD patients with restricted access to multidisciplinary care. Trial registration ClinicalTrials.gov Identifier: NCT04694443.

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Predictors of Recurrent Falls in People with Parkinson’s Disease and Proposal for a Predictive Tool

Abstract: Two predictive tools with moderate-to-high accuracy may identify people with PD at low, medium and high risk of falling recurrently within the next year. However, future studies to address external validation are required.

Cited by 23 publications

References 37 publications

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

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

Falls in persons with Parkinson's disease: Do non-motor symptoms matter as much as motor symptoms?

Adopting a multidisciplinary telemedicine intervention for fall prevention in Parkinson’s disease. Protocol for a longitudinal, randomized clinical trial

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