Predicting Progression in Parkinson's Disease Using Baseline and 1-Year Change Measures.

BackgroundImproved prediction of Parkinson's disease (PD) progression is needed to support clinical decision-making and to accelerate research trials.ObjectivesTo examine whether baseline measures and their 1-year change predict longer-term progression in early PD.MethodsParkinson's Progression Markers Initiative study data were used. Participants had disease duration ≤2 years, abnormal dopamine transporter (DAT) imaging, and were untreated with PD medications. Baseline and 1-year change in clinical, cerebrospinal fluid (CSF), and imaging measures were evaluated as candidate predictors of longer-term (up to 5 years) change in Movement Disorders Society-Unified Parkinson's Disease Rating Scale (MDS-UPDRS) score and DAT specific binding ratios (SBR) using linear mixed-effects models.ResultsAmong 413 PD participants, median follow-up was 5 years. Change in MDS-UPDRS from year-2 to last follow-up was associated with disease duration (β= 0.351; 95% CI = 0.146, 0.555), male gender (β= 3.090; 95% CI = 0.310, 5.869), and baseline (β= -0.199; 95% CI = -0.315, -0.082) and 1-year change (β= 0.540; 95% CI = 0.423, 0.658) in MDS-UPDRS; predictors in the model accounted for 17.6% of the variance in outcome. Predictors of percent change in mean SBR from year-2 to last follow-up included baseline rapid eye movement sleep behavior disorder score (β= -0.6229; 95% CI = -1.2910, 0.0452), baseline (β= 7.232; 95% CI = 2.268, 12.195) and 1-year change (β= 45.918; 95% CI = 35.994,55.843) in mean striatum SBR, and 1-year change in autonomic symptom score (β= -0.325;95% CI = -0.695, 0.045); predictors in the model accounted for 44.1% of the variance.ConclusionsBaseline clinical, CSF, and imaging measures in early PD predicted change in MDS-UPDRS and dopamine-transporter binding, but the predictive value of the models was low. Adding the short-term change of possible predictors improved the predictive value, especially for modeling change in dopamine-transporter binding

Assigning UPDRS Scores in the Leg Agility Task of Parkinsonians: Can It Be Done through BSN-based Kinematic Variables?

In this paper, by characterizing the Leg Agility (LA) task, which contributes to the evaluation of the degree of severity of the Parkinson's Disease (PD), through kinematic variables (including the angular amplitude and speed of thighs' motion), we investigate the link between these variables and Unified Parkinson's Disease Rating Scale (UPDRS) scores. Our investigation relies on the use of a few body-worn wireless inertial nodes and represents a first step in the design of a portable system, amenable to be integrated in Internet of Things (IoT) scenarios, for automatic detection of the degree of severity (in terms of UPDRS score) of PD. The experimental investigation is carried out considering 24 PD patients.Comment: 10 page

Association of metabolic syndrome and change in Unified Parkinson\u27s Disease Rating Scale scores.

OBJECTIVE: To explore the association between metabolic syndrome and the Unified Parkinson\u27s Disease Rating Scale (UPDRS) scores and, secondarily, the Symbol Digit Modalities Test (SDMT). METHODS: This is a secondary analysis of data from 1,022 of 1,741 participants of the National Institute of Neurological Disorders and Stroke Exploratory Clinical Trials in Parkinson Disease Long-Term Study 1, a randomized, placebo-controlled trial of creatine. Participants were categorized as having or not having metabolic syndrome on the basis of modified criteria from the National Cholesterol Education Program Adult Treatment Panel III. Those who had the same metabolic syndrome status at consecutive annual visits were included. The change in UPDRS and SDMT scores from randomization to 3 years was compared in participants with and without metabolic syndrome. RESULTS: Participants with metabolic syndrome (n = 396) compared to those without (n = 626) were older (mean [SD] 63.9 [8.1] vs 59.9 [9.4] years; p \u3c 0.0001), were more likely to be male (75.3% vs 57.0%; p \u3c 0.0001), and had a higher mean uric acid level (men 5.7 [1.3] vs 5.3 [1.1] mg/dL, women 4.9 [1.3] vs 3.9 [0.9] mg/dL, p \u3c 0.0001). Participants with metabolic syndrome experienced an additional 0.6- (0.2) unit annual increase in total UPDRS (p = 0.02) and 0.5- (0.2) unit increase in motor UPDRS (p = 0.01) scores compared with participants without metabolic syndrome. There was no difference in the change in SDMT scores. CONCLUSIONS: Persons with Parkinson disease meeting modified criteria for metabolic syndrome experienced a greater increase in total UPDRS scores over time, mainly as a result of increases in motor scores, compared to those who did not. Further studies are needed to confirm this finding. CLINICALTRIALSGOV IDENTIFIER: NCT00449865

Extended Timed Up and Go assessment as a clinical indicator of cognitive state in Parkinson\u27s disease

Objective: To evaluate a modified extended Timed Up and Go (extended-TUG) assessment against a panel of validated clinical assessments, as an indicator of Parkinson’s disease (PD) severity and cognitive impairment. Methods: Eighty-seven participants with idiopathic PD were sequentially recruited from a Movement Disorders Clinic. An extended-TUG assessment was employed which required participants to stand from a seated position, walk in a straight line for 7 metres, turn 180 degrees and then return to the start, in a seated position. The extended-TUG assessment duration was correlated to a panel of clinical assessments, including the Unified Parkinson’s Disease Rating Scale (MDS-UPDRS), Quality of Life (PDQ-39), Scales for Outcomes in Parkinson’s disease (SCOPA-Cog), revised Addenbrooke’s Cognitive Index (ACE-R) and Barratt’s Impulsivity Scale 11 (BIS-11). Results: Extended-TUG time was significantly correlated to MDS-UPDRS III score and to SCOPA-Cog, ACE-R (p\u3c0.001) and PDQ-39 scores (p\u3c0.01). Generalized linear models determined the extended-TUG to be a sole variable in predicting ACE-R or SCOPA-Cog scores. Patients in the fastest extended-TUG tertile were predicted to perform 8.3 and 13.4 points better in the SCOPA-Cog and ACE-R assessments, respectively, than the slowest group. Patients who exceeded the dementia cut-off scores with these instruments exhibited significantly longer extended-TUG times. Conclusions: Extended-TUG performance appears to be a useful indicator of cognition as well as motor function and quality of life in PD, and warrants further evaluation as a first line assessment tool to monitor disease severity and response to treatment. Poor extended-TUG performance may identify patients without overt cognitive impairment form whom cognitive assessment is needed

Theatre is a valid add-on therapeutic intervention for emotional rehabilitation of parkinson's disease patients

Conventional medical treatments of Parkinson's disease (PD) are effective on motor disturbances but may have little impact on nonmotor symptoms, especially psychiatric ones. Thus, even when motor symptomatology improves, patients might experience deterioration in their quality of life. We have shown that 3 years of active theatre is a valid complementary intervention for PD as it significantly improves the well-being of patients in comparison to patients undergoing conventional physiotherapy. Our aim was to replicate these findings while improving the efficacy of the treatment. We ran a single-blinded pilot study lasting 15 months on 24 subjects with moderate idiopathic PD. 12 were assigned to a theatre program in which patients underwent "emotional" training. The other 12 underwent group physiotherapy. Patients were evaluated at the beginning and at the end of their treatments, using a battery of eight clinical and five neuropsychological scales. We found that the emotional theatre training improved the emotional well-being of patients, whereas physiotherapy did not. Interestingly, neither of the groups showed improvements in either motor symptoms or cognitive abilities tested by the neuropsychological battery. We confirmed that theatre therapy might be helpful in improving emotional well-being in PD

Unsupervised Parkinson’s Disease Assessment

Parkinson’s Disease (PD) is a progressive neurological disease that affects 6.2 million people worldwide. The most popular clinical method to measure PD tremor severity is a standardized test called the Unified Parkinson’s Disease Rating Scale (UPDRS), which is performed subjectively by a medical professional. Due to infrequent checkups and human error introduced into the process, treatment is not optimally adjusted for PD patients. According to a recent review there are two devices recommended to objectively quantify PD symptom severity. Both devices record a patient’s tremors using inertial measurement units (IMUs). One is not currently available for over the counter purchases, as they are currently undergoing clinical trials. It has also been used in studies to evaluate to UPDRS scoring in home environments using an Android application to drive the tests. The other is an accessible product used by researchers to design home monitoring systems for PD tremors at home. Unfortunately, this product includes only the sensor and requires technical expertise and resources to set up the system. In this paper, we propose a low-cost and energy-efficient hybrid system that monitors a patient’s daily actions to quantify hand and finger tremors based on relevant UPDRS tests using IMUs and surface Electromyography (sEMG). This device can operate in a home or hospital environment and reduces the cost of evaluating UPDRS scores from both patient and the clinician’s perspectives. The system consists of a wearable device that collects data and wirelessly communicates with a local server that performs data analysis. The system does not require any choreographed actions so that there is no need for the user to follow any unwieldy peripheral. In order to avoid frequent battery replacement, we employ a very low-power wireless technology and optimize the software for energy efficiency. Each collected signal is filtered for motion classification, where the system determines what analysis methods best fit with each period of signals. The corresponding UPDRS algorithms are then used to analyze the signals and give a score to the patient. We explore six different machine learning algorithms to classify a patient’s actions into appropriate UPDRS tests. To verify the platform’s usability, we conducted several tests. We measured the accuracy of our main sensors by comparing them with a medically approved industry device. The our device and the industry device show similarities in measurements with errors acceptable for the large difference in cost. We tested the lifetime of the device to be 15.16 hours minimum assuming the device is constantly on. Our filters work reliably, demonstrating a high level of similarity to the expected data. Finally, the device is run through and end-to-end sequence, where we demonstrate that the platform can collect data and produce a score estimate for the medical professionals

The AMC Linear Disability Score in patients with newly diagnosed Parkinson disease

Objective: The aim of this study was to examine the clinimetric properties of the AMC Linear Disability Score (ALDS), a new generic disability measure based on Item Response Theory, in patients with newly diagnosed Parkinson disease (PD).\ud \ud Methods: A sample of 132 patients with PD was evaluated using the Hoehn and Yahr (H&Y), the Unified PD Rating Scale motor examination, the Schwab and England scale (S&E), the Short Form–36, the PD Quality of Life Questionnaire, and the ALDS.\ud \ud Results: The internal consistency reliability of the ALDS was good ([alpha] = 0.95) with 55 items extending the sufficient item-total correlation criterion (r > 0.20). The ALDS was correlated with other disability measures (r = 0.50 to 0.63) and decreasingly associated with measures reflecting impairments (r = 0.36 to 0.37) and mental health (r = 0.23 to -0.01). With regard to know-group validity, the ALDS indicated that patients with more severe PD (H&Y stage 3) were more disabled than patients with mild (H&Y stage 1) or moderate PD (H&Y stage 2) (p < 0.0001). The ALDS discriminated between more or less severe extrapyramidal symptoms (p = 0.001) and patients with postural instability showed lower ALDS scores compared to patients without postural instability (p = < 0.0001). Compared to the S&E (score 100% = 19%), the ALDS showed less of a ceiling effect (5%).\ud \ud Conclusion: The AMC Linear Disability Score is a flexible, feasible, and clinimetrically promising instrument to assess the level of disability in patients with newly diagnosed Parkinson disease

A Comparative Study of Machine Learning Models for Tabular Data Through Challenge of Monitoring Parkinson's Disease Progression Using Voice Recordings

People with Parkinson's disease must be regularly monitored by their physician to observe how the disease is progressing and potentially adjust treatment plans to mitigate the symptoms. Monitoring the progression of the disease through a voice recording captured by the patient at their own home can make the process faster and less stressful. Using a dataset of voice recordings of 42 people with early-stage Parkinson's disease over a time span of 6 months, we applied multiple machine learning techniques to find a correlation between the voice recording and the patient's motor UPDRS score. We approached this problem using a multitude of both regression and classification techniques. Much of this paper is dedicated to mapping the voice data to motor UPDRS scores using regression techniques in order to obtain a more precise value for unknown instances. Through this comparative study of variant machine learning methods, we realized some old machine learning methods like trees outperform cutting edge deep learning models on numerous tabular datasets.Comment: Accepted at "HIMS'20 - The 6th Int'l Conf on Health Informatics and Medical Systems"; https://americancse.org/events/csce2020/conferences/hims2