Longitudinal prediction of falls and near falls frequencies in Parkinson's disease: a prospective cohort study

Anticipating Tomorrow: Tailoring Parkinson's Symptomatic Therapy Using Predictors of Outcome

BACKGROUND

Although research into Parkinson's disease (PD) subtypes and outcome predictions has continued to advance, recommendations for using outcome prediction to guide current treatment decisions remain sparse.

OBJECTIVES

To provide expert opinion-based recommendations for individually tailored PD symptomatic treatment based on knowledge of risk prediction and subtypes.

METHODS

Using a modified Delphi approach, members of the Movement Disorders Society (MDS) Task Force on PD subtypes generated a series of general recommendations around the question: "Using what you know about genetic/biological/clinical subtypes (or any individual-level predictors of outcome), what advice would you give for selecting symptomatic treatments for an individual patient now, based on what their subtype or individual characteristics predict about their future disease course?" After four iterations and revisions, those recommendations with over 75% endorsement were adopted.

RESULTS

A total of 19 recommendations were endorsed by a group of 13 panelists. The recommendations primarily centered around two themes: (1) incorporating future risk of cognitive impairment into current treatment plans; and (2) identifying future symptom clusters that might be forestalled with a single medication.

CONCLUSIONS

These recommendations provide clinicians with a framework for integrating future outcomes into patient-specific treatment choices. They are not prescriptive guidelines, but adaptable suggestions, which should be tailored to each individual. They are to be considered as a first step of a process that will continue to evolve as additional stakeholders provide new insights and as new information becomes available. As individualized risk prediction advances, the path to better tailored treatment regimens will become clearer.

Tandem gait step-width increases more rapidly in more severely affected people with Parkinson's disease

INTRODUCTION

Tandem gait performance reportedly predicts fall risk in people with Parkinson's disease (PwPD) and help distinguish PwPD from atypical parkinsonism. In a cross-sectional study, we previously showed that tandem gait step-width widens with increasing Hoehn and Yahr (H&Y) staging scores. In this longitudinal study, we aimed to determine if progression in tandem gait deficits is dependent on disease severity in PwPD.

METHODS

Participants underwent an instrumented tandem gait measurement every 6 months for at least 2 years. The mean and variability of 4 tandem gait parameters were calculated at each visit: step-width, step-length, step-time, and step-velocity. The change in these parameters over time for 3 H&Y groups (stage 1, 2 and 2.5+) compared to aging controls was determined using a random coefficients regression model. The annual percent change in tandem gait parameters was correlated with initial disease features using Kendall's τB.

RESULTS

66 participants were analyzed (46 PD, 20 controls). Mean step-width increased over time in an H&Y stage-dependent manner, with H&Y 2 and H&Y 2.5+ experiencing increases of 6% and 10% per year (p = 0.001 and 0.024 respectively). Annual percent-change in step-width was correlated with initial motor Unified Parkinson's Disease Rating Scale (UPDRS) scores (Kendall's τB = 0.229), total UPDRS scores (τB = 0.249), H&Y scores (τB = 0.266) and inversely correlated with Montreal Cognitive Assessment (MoCA) scores (τB = -0.209; ps ≤ 0.019).

CONCLUSION

Tandem gait step-width widens over time more rapidly in more severely affected PD patients. These results suggest that tandem gait should be routinely clinically evaluated and considered in the management of imbalance in PwPD.

Risk factors for falls in Parkinson's disease: a cross-sectional observational and Mendelian randomization study

Background

Patients with Parkinson's disease (PD) exhibit a heightened risk of falls and related fractures compared to the general population. This study aims to assess the clinical characteristics associated with falls in the patient with PD and to gain further insight into these factors through Mendelian randomization analysis.

Methods

From January 2013 to December 2023, we included 591 patients diagnosed with Parkinson's disease at Shenzhen Baoan People's Hospital. Using univariate and multivariate logistic regression analyses, we identified clinical variables associated with falls. We constructed a nomogram based on these variables and evaluated the predictive efficacy of the model. Additionally, we employed summary statistics from genome-wide association studies to conduct two-sample Mendelian randomization (MR) analyses on key variables influencing falls.

Results

Compared to the control group, we identified osteoporosis, motor dysfunction, higher Hoehn and Yahr scale as significant risk factors for falls in PD patients. Conversely, treatment with levodopa and a higher level of education exhibited a protective effect against the risk of falling. MR analysis further confirmed a causal relationship between osteoporosis, education level and falls in PD patients.

Conclusion

Osteoporosis and educational attainment are correlated with falls in Parkinson's disease.

Prognostic factors for falls in Parkinson's disease: a systematic review

BACKGROUND

Falls represent a critical concern in Parkinson's disease (PD), contributing to increased morbidity and reduced quality of life.

PURPOSE

We conducted a systematic review to assess the prognostic factors associated with falls in PD, aiming to provide a comprehensive overview of relevant demographic and clinical parameters, and aid neurologists in identifying subsets of PD patients most susceptible to falls and associated injuries.

METHODS

PubMed and Web of Science databases were searched for prospective studies assessing factors associated with falls in ambulatory PD patients across different settings, from inception to August 2023. Data extraction was conducted using CHARMS-PF checklist and risk of bias was assessed with QUIPS tool. PRISMA guidelines were followed.

RESULTS

The initial search yielded 155 references. Thirty-four studies, involving a total of 3454 PD patients, were included in the final analysis. The mean pooled age was 67.6 years, and 45.1% were women. PD patients presented mild motor impairment (UPDRS III score 27.8) with mean pooled disease duration of 5.7 years. Gait and balance disorders and history of prior falls emerged as the most consistent predictors of falls across studies. Disease duration, disease severity, dysautonomic symptoms, freezing of gait, frontal cognitive functions, and PD medication dosages yielded inconsistent findings. Conversely, dyskinesias, age, sex, and depression were unrelated to future falls in PD. Logistic regression models were most commonly employed to identify factors significantly associated with falls in PD. Substantial heterogeneity prevailed in the inclusion of confounding factors.

CONCLUSION

The evidence suggests that previous history of falls, gait disorders, and poor balance are robust prognostic markers for falls in PD.

Associations between performance-based and patient-reported physical functioning and real-world mobile sensor metrics in older cancer survivors: A pilot study

INTRODUCTION

Older cancer survivors are at increased risk for impaired physical functioning, but current assessments of function are difficult to implement in busy oncology clinics. Mobile devices measuring continuous activity and mobility in daily life may be useful for estimating physical functioning. The goal of this pilot study was to examine the associations between consumer wearable device (a wrist-worn activity tracker) and smartphone sensor data and commonly used clinical measures of physical function in cancer survivors aged 65 and older.

MATERIALS AND METHODS

Older adults within five years of completing primary treatment for any cancer completed standardized questionnaires and performance-based tests to measure physical functioning. Continuous passive data from smartphones and consumer wearable devices were collected for four weeks and linked to patient-reported and performance-based physical functioning as well as patient-reported falls or near falls at the end of the four-week monitoring period. To examine associations between sensor variables and physical functioning, we conducted bivariate Pearson correlations as well as multivariable linear regression analyses. To examine associations between sensor variables and falls, we conducted exploratory receiver operating characteristic curve and multivariable logistic regression analyses.

RESULTS

We enrolled 40 participants (mean age 73 years old, range 65-83; 98% White; 50% female). In bivariate analyses, consumer wearable device features reflecting greater amount and speed and lower fragmentation of walking in daily life were significantly related to better patient-reported function (r= 0.43-0.65) and performance-based physical function (r = 0.56-0.72), while smartphone features reflecting more geographic mobility were related to better performance-based physical function (r = 0.40-0.42) but not patient-reported function. After adjusting for age and comorbidities, only consumer wearable device features remained associated with performance-based physical functioning. In exploratory analyses, peak gait cadence was associated with fall risk even after covariate adjustment.

DISCUSSION

This study provides preliminary evidence that real-world data from consumer devices may be useful for estimating functional performance among older cancer survivors and potentially for remotely and longitudinally monitoring functioning in older patients during and after cancer treatment.

Characterizing neurocognitive impairments in Parkinson's disease with mobile EEG when walking and stepping over obstacles

The neural correlates that help us understand the challenges that Parkinson's patients face when negotiating their environment remain under-researched. This deficit in knowledge reflects the methodological constraints of traditional neuroimaging techniques, which include the need to remain still. As a result, much of our understanding of motor disorders is still based on animal models. Daily life challenges such as tripping and falling over obstacles represent one of the main causes of hospitalization for individuals with Parkinson's disease. Here, we report the neural correlates of naturalistic ambulatory obstacle avoidance in Parkinson's disease patients using mobile EEG. We examined 14 medicated patients with Parkinson's disease and 17 neurotypical control participants. Brain activity was recorded while participants walked freely, and while they walked and adjusted their gait to step over expected obstacles (preset adjustment) or unexpected obstacles (online adjustment) displayed on the floor. EEG analysis revealed attenuated cortical activity in Parkinson's patients compared to neurotypical participants in theta (4-7 Hz) and beta (13-35 Hz) frequency bands. The theta power increase when planning an online adjustment to step over unexpected obstacles was reduced in Parkinson's patients compared to neurotypical participants, indicating impaired proactive cognitive control of walking that updates the online action plan when unexpected changes occur in the environment. Impaired action planning processes were further evident in Parkinson's disease patients' diminished beta power suppression when preparing motor adaptation to step over obstacles, regardless of the expectation manipulation, compared to when walking freely. In addition, deficits in reactive control mechanisms in Parkinson's disease compared to neurotypical participants were evident from an attenuated beta rebound signal after crossing an obstacle. Reduced modulation in the theta frequency band in the resetting phase across conditions also suggests a deficit in the evaluation of action outcomes in Parkinson's disease. Taken together, the neural markers of cognitive control of walking observed in Parkinson's disease reveal a pervasive deficit of motor-cognitive control, involving impairments in the proactive and reactive strategies used to avoid obstacles while walking. As such, this study identified neural markers of the motor deficits in Parkinson's disease and revealed patients' difficulties in adapting movements both before and after avoiding obstacles in their path.

Clinical Usefulness of Retropulsion Tests in Persons with Mild to Moderate Parkinson's Disease

People with Parkinson's disease (PwPD) have an increased risk for falls and near falls. They have particular difficulties with maintaining balance against an external perturbation, and several retropulsion tests exist. The Unified PD Rating Scale item 30 (UPDRS30) is the most common, involving an expected shoulder pull. Others recommend using an unexpected shoulder pull, e.g., the Nutt Retropulsion Test (NRT). We aimed to evaluate the clinical usefulness of these tests for detecting future fallers. By using two different golden standards related to self-reported prospective falls and near falls over 6 months following two different time points with 3.5 years between, we estimated sensitivity/specificity, Youden index, predictive values, and likelihood ratios for each test. The different time points yielded a different prevalence of falls and near falls, as well as different predictive values. When comparing the performance of the NRT and UPDRS30 for detecting future fallers, we found that the NRT consistently performed better than UPDRS30. However, neither test exhibited optimal performance in terms of predictive values and associated likelihood ratios. Our findings speak against using either of these tests as a single assessment for this purpose and support previous recommendations of using a multifactorial approach when targeting balance problems in PwPD.