MDS-UPDRS Scores Research Articles

PND57 Cost-Effectiveness Analysis of a Hypothetical Disease Modifying Therapy for Parkinson's Disease

To conduct a case study of the lifetime cost-effectiveness of a hypothetical disease modifying therapy (DMT) plus standard of care (SOC) vs SOC for treatment-naïve Parkinson’s disease (PD) in the UK. A patient-level simulation was developed to account for individual variability in progression of PD, and inter-related changes in Movement Disorder Society Revision of Unified PD Rating Scales (MDS-UPDRS I-IV). Predictive equations were based on analyses of Parkinson's Progression Markers Initiative (PPMI) study and National Institute of Neurological Disorders and Stroke Exploratory Trials in PD Long-Term Study 1. The benefits of starting symptomatic treatments (dopaminergic and advanced therapies) and their long-term limitations were simulated. Inputs for mortality, patient and caregiver utilities, and UK costs (2020) were obtained from publicly available sources. Hypothetical DMT was assumed to slow progression in MDS-UPDRS scores by 50% vs SOC. Costs and health outcomes were discounted at 3.5% per annum. Sensitivity analyses explored the main drivers of results. Patient profiles were generated by jointly sampling correlated characteristics observed in PPMI (mean age=61.2 years, male=65%, PD duration=6 months [range 0 – 48], treatment-naïve). Over a lifetime, patients were projected to incur costs of £243,748—with 22% from treatment costs, and 58% non-medical, including nursing home admission. Comparing DMT vs SOC, quality-adjusted life years (QALYs) were 9.0 vs 7.3 (+1.6), and as costs of DMT were not considered, predicted total costs were lowered by 26%. For a subgroup starting treatment earlier (mean PD duration=3 months [range 0 – 6]), benefits and savings were increased: QALY gain of 1.8 and reduction in costs of £57,251 (-32%). Key drivers of cost-effectiveness: efficacy, utilities, non-medical costs, and baseline characteristics. This case study for a hypothetical DMT illustrates how earlier treatment could be beneficial. The flexible model design facilitates exploratory scenarios and assessment of key sources of uncertainty on the results.

The Parkinson disease pain classification system: results from an international mechanism-based classification approach.

Pain is a common nonmotor symptom in patients with Parkinson disease (PD) but the correct diagnosis of the respective cause remains difficult because suitable tools are lacking, so far. We developed a framework to differentiate PD- from non-PD-related pain and classify PD-related pain into 3 groups based on validated mechanistic pain descriptors (nociceptive, neuropathic, or nociplastic), which encompass all the previously described PD pain types. Severity of PD-related pain syndromes was scored by ratings of intensity, frequency, and interference with daily living activities. The PD-Pain Classification System (PD-PCS) was compared with classic pain measures (ie, brief pain inventory and McGill pain questionnaire [MPQ], PDQ-8 quality of life score, MDS-UPDRS scores, and nonmotor symptoms). 159 nondemented PD patients (disease duration 10.2 ± 7.6 years) and 37 healthy controls were recruited in 4 centers. PD-related pain was present in 122 patients (77%), with 24 (15%) suffering one or more syndromes at the same time. PD-related nociceptive, neuropathic, or nociplastic pain was diagnosed in 87 (55%), 25 (16%), or 35 (22%), respectively. Pain unrelated to PD was present in 35 (22%) patients. Overall, PD-PCS severity score significantly correlated with pain's Brief Pain Inventory and MPQ ratings, presence of dyskinesia and motor fluctuations, PDQ-8 scores, depression, and anxiety measures. Moderate intrarater and interrater reliability was observed. The PD-PCS is a valid and reliable tool for differentiating PD-related pain from PD-unrelated pain. It detects and scores mechanistic pain subtypes in a pragmatic and treatment-oriented approach, unifying previous classifications of PD-pain.

Data-Driven Models for Objective Grading Improvement of Parkinson\u2019s Disease

Parkinson’s disease (PD) is a progressive disorder of the central nervous system that causes motor dysfunctions in affected patients. Objective assessment of symptoms can support neurologists in fine evaluations, improving patients’ quality of care. Herein, this study aimed to develop data-driven models based on regression algorithms to investigate the potential of kinematic features to predict PD severity levels. Sixty-four patients with PD (PwPD) and 50 healthy subjects of control (HC) were asked to perform 13 motor tasks from the MDS-UPDRS III while wearing wearable inertial sensors. Simultaneously, the clinician provided the evaluation of the tasks based on the MDS-UPDRS scores. One hundred-ninety kinematic features were extracted from the inertial motor data. Data processing and statistical analysis identified a set of parameters able to distinguish between HC and PwPD. Then, multiple feature selection methods allowed selecting the best subset of parameters for obtaining the greatest accuracy when used as input for several predicting regression algorithms. The maximum correlation coefficient, equal to 0.814, was obtained with the adaptive neuro-fuzzy inference system (ANFIS). Therefore, this predictive model could be useful as a decision support system for a reliable objective assessment of PD severity levels based on motion performance, improving patients monitoring over time.

Arterial spin labeling detects perfusion patterns related to motor symptoms in Parkinson's disease

IntroductionImaging neurovascular disturbances in Parkinson's disease (PD) is an excellent measure of disease severity. Indeed, a disease-specific regional pattern of abnormal metabolism has been identified using positron emission tomography. Only a handful of studies, however, have applied perfusion MRI to detect this disease pattern. Our goal was to replicate the evaluation of a PD-related perfusion pattern using scaled subprofile modeling/principal component analysis (SSM-PCA). Methods. We applied arterial spin labeling (ASL) MRI for this purpose. Uniquely, we assessed this pattern separately in PD individuals ON and OFF dopamine medications. We further compared the existence of these patterns and their strength in each individual with their Movement Disorder Society-Unified Parkinson's Disease Rating Scale motor (MDS-UPDRS) scores, cholinergic tone as indexed by short-term afferent inhibition (SAI), and other neuropsychiatric tests. Results. We observed a PD-related perfusion pattern that was similar to previous studies. The patterns were observed in both ON and OFF states but only the pattern in the OFF condition could significantly (AUC=0.72) differentiate between PD and healthy subjects. In the ON condition, PD subjects were similar to controls from a CBF standpoint (AUC=0.45). The OFF pattern prominently included the posterior cingulate, precentral region, precuneus, and the subcallosal cortex. Individual principal components from the ON and OFF states were strongly associated with MDS-UPDRS scores, SAI amplitude and latency. Conclusion. Using ASL, our study identified patterns of abnormal perfusion in PD and were associated with disease symptoms.

Long-term safety and efficacy of adjunctive rasagiline in levodopa-treated Japanese patients with Parkinson\u2019s disease

Rasagiline is a monoamine oxidase type-B inhibitor in development in Japan for Parkinson’s disease (PD). This open-label study evaluated the long-term safety and efficacy of rasagiline in Japanese patients with PD receiving levodopa. Patients were aged 30–79 years and had wearing-off or weakened effect. Patients received rasagiline 1 mg/day for 52 weeks. The primary objective was to evaluate safety. Secondary endpoints included MDS-UPDRS Part II and Part III total scores (ON-state) and change from baseline in mean daily OFF-time. An additional endpoint was the Parkinson’s Disease Questionnaire-39 (PDQ-39) Summary Index (SI) score. In total, 222 patients were enrolled; 52.3% had wearing-off phenomena. Treatment-emergent adverse events (TEAEs) were mostly mild or moderate and occurred in 83.3% of patients; 63.1% had drug-related TEAEs; and 21.2% had TEAEs resulting in discontinuation. Fall (16.7%), nasopharyngitis (14.0%), and dyskinesia (10.8%) were the most frequent TEAEs. Serious TEAEs were reported in 17.6% of patients, and led to discontinuation in 9.5%. At week 52 (last-observation-carried forward), the mean change from baseline in MDS-UPDRS Part III total score (ON-state) was − 7.6; the mean change from baseline in daily OFF-time was − 0.89 h in patients with wearing-off phenomena at the start of the run-in period. The mean change from baseline in PDQ-39 SI was − 0.64. No major safety issues were observed during this 52-week trial of rasagiline as an adjunct to levodopa in Japanese patients. Mean changes in MDS-UPDRS scores and daily OFF-time suggested that adjunctive rasagiline treatment with levodopa was efficacious, with efficacy maintained for at least 52 weeks.

Latency of re-emergent tremor in Parkinson's disease is influenced by levodopa

IntroductionRe-emergent tremor (RET) is a common form of postural tremor observed in Parkinson's disease (PD) patients. Recent studies have shown that administration of levodopa decreases RET amplitude. However, drug effects on tremor pause duration are less clear. MethodsWe performed a prospective observational study in PD patients with RET, subjected to acute levodopa challenge. Tremor activity was measured during OFF and ON states both clinically, as well as by using accelerometers taped to the back of both hands. Correlation between RET amplitude and pause duration, as well with MDS-UPDRS scores were investigated. The slope of gradual increase of postural tremor after the pause was also measured in the OFF and ON states. ResultsSignificant inverse correlation between tremor amplitude and RET pause duration was observed in OFF (rs = −0.474, p = 0.030) and ON (rs = −0.569, p = 0.006) states. Levodopa reduced tremor amplitude (26%, p = 0.004) dampening slope gradient (22%, p = 0.029). Tremor pause duration also showed inverse correlation with postural tremor amplitude measured by MDS-UPDRS in OFF (rs = −0.311, p = 0.048) and ON (rs = −0.503, p = 0.020) states, as well as with total MDS-UPDRS Part III score (rs = −0.295, p = 0.009). Finally, accelerometric analysis proved to be more sensitive than visual inspection for detecting tremor pauses. ConclusionOur results suggest RET pause duration is amplitude related, since levodopa-induced amplitude decrease led to pause prolongation, associated with decreased tremor intensity and slope gradient dampening.

Neurotransmitter activity is linked to outcome following subthalamic deep brain stimulation in Parkinson's disease

IntroductionWhile the mechanisms underlying the therapeutic effects of deep brain stimulation (DBS) in Parkinson's Disease (PD) are not yet fully understood, DBS appears to exert a wide range of neurochemical effects on the network level, thought to arise from activation of inhibitory and excitatory pathways. The activity within the primary inhibitory (GABAergic) and excitatory (glutamatergic) neurotransmitter systems may therefore play an important role in the therapeutic efficacy of DBS in PD. The purpose of this study was to investigate abnormalities in GABA-ergic and glutamatergic neurotransmission in PD, and to examine the link between neurotransmitter levels and outcome following DBS. MethodsMagnetic resonance spectra were acquired from the pons and basal ganglia in sixteen patients with PD and sixteen matched control participants. GABA and glutamate levels were quantified with LCModel, an automated spectral fitting package. Fourteen patients subsequently underwent DBS, and PD symptoms were evaluated with the MDS-UPDRS at baseline and six months after surgery. The efficacy of DBS treatment was evaluated from the percentage improvement in MDS-UPDRS scores. ResultsBasal ganglia GABA levels were significantly higher in PD patients relative to control participants (p < 0.01), while pontine glutamate + glutamine (Glx) levels were significantly lower in patients with PD (p < 0.05). While GABA levels were not significantly related to outcome post-surgery, basal ganglia glutamate levels emerged as a significant predictor of outcome, suggesting a possible role for glutamatergic neurotransmission in the therapeutic mechanism of DBS. ConclusionGABAergic and glutamatergic neurotransmission is altered in PD, and glutamatergic activity in particular may influence outcome post-surgery.

Dopamine reuptake transporter-single-photon emission computed tomography and transcranial sonography as imaging markers of prediagnostic Parkinson's disease.

The objective of this study was to examine whether prediagnostic features of Parkinson's disease (PD) were associated with changes in dopamine reuptake transporter-single-photon emission computed tomography and transcranial sonography. Prediagnostic features of PD (risk estimates, University of Pennsylvania Smell Identification Test, Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire, and finger-tapping scores) were assessed in a large cohort of older U.K. residents. A total of 46 participants were included in analyses of prediagnostic features and MDS-UPDRS scores with the striatal binding ratio on dopamine reuptake transporter-single-photon emission computed tomography and nigral hyperechogenicity on transcranial sonography. The striatal binding ratio was associated with PD risk estimates (P = .040), University of Pennsylvania Smell Identification Test (P = .002), Rapid Eye Movement Sleep Behavior Disorder Screening Questionnaire scores (P = .024), tapping speed (P = .024), and MDS-UPDRS motor scores (P = .009). Remotely collected assessments explained 26% of variation in the striatal binding ratio. The inclusion of MDS-UPDRS motor scores did not explain additional variance. The size of the nigral echogenic area on transcranial sonography was associated with risk estimates (P < .001) and MDS-UPDRS scores (P = .03) only. The dopamine reuptake transporter-single-photon emission computed tomography results correlated with motor and nonmotor features of prediagnostic PD, supporting its potential use as a marker in the prodromal phase of PD. Transcranial sonography results also correlated with risk scores and motor signs. © 2018 The Authors. Movement Disorders published by Wiley Periodicals, Inc. on behalf of International Parkinson and Movement Disorder Society.

Imaging genetics approach to predict progression of Parkinson's diseases.

Imaging genetics is a tool to extract genetic variants associated with both clinical phenotypes and imaging information. The approach can extract additional genetic variants compared to conventional approaches to better investigate various diseased conditions. Here, we applied imaging genetics to study Parkinson's disease (PD). We aimed to extract significant features derived from imaging genetics and neuroimaging. We built a regression model based on extracted significant features combining genetics and neuroimaging to better predict clinical scores of PD progression (i.e. MDS-UPDRS). Our model yielded high correlation (r = 0.697, p <; 0.001) and low root mean squared error (8.36) between predicted and actual MDS-UPDRS scores. Neuroimaging (from 123I-Ioflupane SPECT) predictors of regression model were computed from independent component analysis approach. Genetic features were computed using image genetics approach based on identified neuroimaging features as intermediate phenotypes. Joint modeling of neuroimaging and genetics could provide complementary information and thus have the potential to provide further insight into the pathophysiology of PD. Our model included newly found neuroimaging features and genetic variants which need further investigation.

Parkinson's disease severity levels and MDS-Unified Parkinson's Disease Rating Scale

Severity of PD is usually assessed by means of the motor and disability-based Hoehn and Yahr staging (HY), or clinician and patient global perceptions. Scores of more detailed assessments, as the MDS-UPDRS, have not been translated to a grading that allows assignment of score sections to severity levels. The objective of the present study is to determine cut-off points for PD severity levels based on the MDS-UPDRS. International, observational study. Applied assessments were: HY, MDS-UPDRS, Clinical Impression for Severity Index, and Clinical and Patient Global Impression of Severity. The coincidence in severity level (mild, moderate, severe) of at least two clinical classifications plus the patient's gradation was considered "the criterion of severity". Cut-off values for each MDS-UPDRS subscale was determined by triangulation of: 1) percentile 90 of the subscale total score; 2) receiver operating characteristic (ROC) analysis; and 3) ordinal logistic regression (OLR) model. Sample was composed of 452 consecutive PD patients without dementia, 55.3% males, age 65.1±10.7 years and PD duration 8.7±6.3 years. All HY stages were represented. The "criterion", classified 275 patients (60.8% of the sample) as: mild PD, 149 (54.2%); moderate, 82 (29.8%); and severe, 44 (16%). The following MDS-UPDRS cut-off points between mild/moderate and moderate/severe levels were found: Part 1: 10/11 and 21/22; Part 2: 12/13 and 29/30; Part 3: 32/33 and 58/59; and Part 4: 4/5 and 12/13. Cut-off points to classify PD patients as mild, moderate, or severe on the basis of their MDS-UPDRS scores are proposed.