Predicting the longitudinal changes of levodopa dose requirements in Parkinson's disease using item response theory assessment of real-world Unified Parkinson's Disease Rating Scale.
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SUBMITTER: Chae D
PROVIDER: S-EPMC8213413 | biostudies-literature |
REPOSITORIES: biostudies-literature
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Project description:BackgroundAlthough nontremor and tremor Part 3 Movement Disorder Society-Unified Parkinson's Disease Rating Scale items measure different impairment domains, their distinct progression and drug responsivity remain unstudied longitudinally. The total score may obscure important time-based and treatment-based changes occurring in the individual domains.ObjectiveUsing the unique advantages of item response theory (IRT), we developed novel longitudinal unidimensional and multidimensional models to investigate nontremor and tremor changes occurring in an interventional Parkinson's disease (PD) study.MethodWith unidimensional longitudinal IRT, we assessed the 33 Part 3 item data (22 nontremor and 10 tremor items) of 336 patients with early PD from the STEADY-PD III (Safety, Tolerability, and Efficacy Assessment of Isradipine for PD, placebo vs. isradipine) study. With multidimensional longitudinal IRT, we assessed the progression rates over time and treatment (in overall motor severity, nontremor, and tremor domains) using Markov Chain Monte Carlo implemented in Stan.ResultsRegardless of treatment, patients showed significant but different time-based deterioration rates for total motor, nontremor, and tremor scores. Isradipine was associated with additional significant deterioration over placebo in total score and nontremor scores, but not in tremor score. Further highlighting the 2 separate latent domains, nontremor and tremor severity changes were positively but weakly correlated (correlation coefficient, 0.108).ConclusionsLongitudinal IRT analysis is a novel statistical method highly applicable to PD clinical trials. It addresses limitations of traditional linear regression approaches and previous IRT investigations that either applied cross-sectional IRT models to longitudinal data or failed to estimate all parameters simultaneously. It is particularly useful because it can separate nontremor and tremor changes both over time and in response to treatment interventions.
| S-EPMC8485609 | biostudies-literature
Project description:ObjectiveTest if differential item functioning due to gender, age, race/ethnicity, or education impacts Unified Dyskinesia Rating Scale scores.BackgroundTesting rating scales for differential item functioning is a core validation step. If differential item functioning exists, interpretation of item scores must consider secondary influences on dyskinesia ratings.MethodsUsing Unified Dyskinesia Rating Scale translation databases (N = 3,132), we tested uniform and nonuniform differential item functioning. We required confirmation by two independent methods and considered differential item functioning pertinent if McFadden pseudo R2 magnitude statistics exceeded negligible ratings.ResultsNo age, race/ethnicity, or education nonuniform differential item functioning was identified. Gender nonuniform differential item functioning occurred for 2 items, both with negligible magnitude. Gender, race, and education uniform differential item functioning was observed for multiple items, all with negligible magnitude.ConclusionsThe Unified Dyskinesia Rating Scale items effectively capture dyskinesia severity without pertinent gender, age, race/ ethnicity, or education influence. © 2017 International Parkinson and Movement Disorder Society.
| S-EPMC5570621 | biostudies-literature
Project description:IntroductionThe Clinical Dementia Rating (CDR) is widely used in Alzheimer's disease research studies and has well established reliability and validity. To facilitate the development of an online, electronic CDR (eCDR) for more efficient clinical applications, this study aims to produce a shortened version of the CDR, and to develop the statistical model for automatic scoring.MethodsItem response theory (IRT) was used for item evaluation and model development. An automatic scoring algorithm was validated using existing CDR global and domain box scores as the reference standard.ResultsMost CDR items discriminate well at mild and very mild levels of cognitive impairment. The bi-factor IRT model fits best and the shortened CDR still demonstrates very high classification accuracy (81%∼92%).DiscussionThe shortened version of the CDR and the automatic scoring algorithm has established a good foundation for developing an eCDR and will ultimately improve the efficiency of cognitive assessment.
| S-EPMC8016711 | biostudies-literature
Project description:ObjectiveAssess MDS-UPDRS items for gender-, age-, and race/ethnicity-based differential item functioning.BackgroundAssessing differential item functioning is a core rating scale validation step. For the MDS-UPDRS, differential item functioning occurs if item-score probability among people with similar levels of parkinsonism differ according to selected covariates (gender, age, race/ethnicity). If the magnitude of differential item functioning is clinically relevant, item-score interpretation must consider influences by these covariates. Differential item functioning can be nonuniform (covariate variably influences an item-score across different levels of parkinsonism) or uniform (covariate influences an item-score consistently over all levels of parkinsonism).MethodsUsing the MDS-UPDRS translation database of more than 5,000 PD patients from 14 languages, we tested gender-, age-, and race/ethnicity-based differential item functioning. To designate an item as having clinically relevant differential item functioning, we required statistical confirmation by 2 independent methods, along with a McFadden pseudo-R2 magnitude statistic greater than "negligible."ResultsMost items showed no gender-, age- or race/ethnicity-based differential item functioning. When differential item functioning was identified, the magnitude statistic was always in the "negligible" range, and the scale-level impact was minimal.ConclusionsThe absence of clinically relevant differential item functioning across all items and all parts of the MDS-UPDRS is strong evidence that the scale can be used confidently. As studies of Parkinson's disease increasingly involve multinational efforts and the MDS-UPDRS has several validated non-English translations, the findings support the scale's broad applicability in populations with varying gender, age, and race/ethnicity distributions. © 2016 International Parkinson and Movement Disorder Society.
| S-EPMC5289884 | biostudies-literature
Project description:BackgroundUnified Parkinson Disease Rating Scale-part III (UPDRS III) is part of the standard clinical examination performed to track the severity of Parkinson's disease (PD) motor complications. Wearable technologies could be used to reduce the need for on-site clinical examinations of people with Parkinson's disease (PwP) and provide a reliable and continuous estimation of the severity of PD at home. The reported estimation can be used to successfully adjust the dose and interval of PD medications.MethodsWe developed a novel algorithm for unobtrusive and continuous UPDRS-III estimation at home using two wearable inertial sensors mounted on the wrist and ankle. We used the ensemble of three deep-learning models to detect UPDRS-III-related patterns from a combination of hand-crafted features, raw temporal signals, and their time-frequency representation. Specifically, we used a dual-channel, Long Short-Term Memory (LSTM) for hand-crafted features, 1D Convolutional Neural Network (CNN)-LSTM for raw signals, and 2D CNN-LSTM for time-frequency data. We utilized transfer learning from activity recognition data and proposed a two-stage training for the CNN-LSTM networks to cope with the limited amount of data.ResultsThe algorithm was evaluated on gyroscope data from 24 PwP as they performed different daily living activities. The estimated UPDRS-III scores had a correlation of [Formula: see text] and a mean absolute error of 5.95 with the clinical examination scores without requiring the patients to perform any specific tasks.ConclusionOur analysis demonstrates the potential of our algorithm for estimating PD severity scores unobtrusively at home. Such an algorithm could provide the required motor-complication measurements without unnecessary clinical visits and help the treating physician provide effective management of the disease.
| S-EPMC8010504 | biostudies-literature
Project description:ObjectiveTo explore the association between metabolic syndrome and the Unified Parkinson's Disease Rating Scale (UPDRS) scores and, secondarily, the Symbol Digit Modalities Test (SDMT).MethodsThis 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.ResultsParticipants 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 < 0.0001), were more likely to be male (75.3% vs 57.0%; p < 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 < 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.ConclusionsPersons 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 identifierNCT00449865.
| S-EPMC5664310 | biostudies-literature
Project description:BackgroundThe diagnosis of Parkinson's disease (PD) and evaluation of its symptoms require in-person clinical examination. Remote evaluation of PD symptoms is desirable, especially during a pandemic such as the coronavirus disease 2019 pandemic. One potential method to remotely evaluate PD motor impairments is video-based analysis. In this study, we aimed to assess the feasibility of predicting the Unified Parkinson's Disease Rating Scale (UPDRS) score from gait videos using a convolutional neural network (CNN) model.MethodsWe retrospectively obtained 737 consecutive gait videos of 74 patients with PD and their corresponding neurologist-rated UPDRS scores. We utilized a CNN model for predicting the total UPDRS part III score and four subscores of axial symptoms (items 27, 28, 29, and 30), bradykinesia (items 23, 24, 25, 26, and 31), rigidity (item 22) and tremor (items 20 and 21). We trained the model on 80% of the gait videos and used 10% of the videos as a validation dataset. We evaluated the predictive performance of the trained model by comparing the model-predicted score with the neurologist-rated score for the remaining 10% of videos (test dataset). We calculated the coefficient of determination (R2) between those scores to evaluate the model's goodness of fit.ResultsIn the test dataset, the R2 values between the model-predicted and neurologist-rated values for the total UPDRS part III score and subscores of axial symptoms, bradykinesia, rigidity, and tremor were 0.59, 0.77, 0.56, 0.46, and 0.0, respectively. The performance was relatively low for videos from patients with severe symptoms.ConclusionsDespite the low predictive performance of the model for the total UPDRS part III score, it demonstrated relatively high performance in predicting subscores of axial symptoms. The model approximately predicted the total UPDRS part III scores of patients with moderate symptoms, but the performance was low for patients with severe symptoms owing to limited data. A larger dataset is needed to improve the model's performance in clinical settings.
| S-EPMC10552271 | biostudies-literature
Project description:PD is associated with disrupted connectivity to a large number of distributed brain regions. How the disease alters the functional topological organization of the brain, however, remains poorly understood. Furthermore, how levodopa modulates network topology in PD is largely unknown. The objective of this study was to use resting-state functional MRI and graph theory to determine how small-world architecture is altered in PD and affected by levodopa administration.Twenty-one PD patients and 20 controls underwent functional MRI scanning. PD patients were scanned off medication and 1 hour after 200?mg levodopa. Imaging data were analyzed using 226 nodes comprising 10 intrinsic brain networks. Correlation matrices were generated for each subject and converted into cost-thresholded, binarized adjacency matrices. Cost-integrated whole-brain global and local efficiencies were compared across groups and tested for relationships with disease duration and severity.Data from 2 patients and 4 controls were excluded because of excess motion. Patients off medication showed no significant changes in global efficiency and overall local efficiency, but in a subnetwork analysis did show increased local efficiency in executive (P?=?0.006) and salience (P?=?0.018) networks. Levodopa significantly decreased local efficiency (P?=?0.039) in patients except within the subcortical network, in which it significantly increased local efficiency (P?=?0.007).Levodopa modulates global and local efficiency measures of small-world topology in PD, suggesting that degeneration of nigrostriatal neurons in PD may be associated with a large-scale network reorganization and that levodopa tends to normalize the disrupted network topology in PD. © 2016 International Parkinson and Movement Disorder Society.
| S-EPMC5115928 | biostudies-literature
Project description: Not available
| S-EPMC8022849 | biostudies-literature
Project description:Background and purposeThe International Movement Disorder Society revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS) is widely used in the assessment of the severity of Parkinson's disease (PD). This study aimed to validate the Kazakh version of the MDS-UPDRS, explore its dimensionality, and compare it to the original English version.MethodsThe validation was conducted in three phases: first, the English version of the MDS-UPDRS was translated into Kazakh and thereafter back-translated into English by two independent teams; second, the Kazakh version underwent a cognitive pretesting; third, the Kazakh version was tested in 360 native Kazakh-speaking PD patients. Both confirmatory and exploratory factor analyses were performed to validate the scale. We calculated the comparative fit index (CFI) for confirmatory factor analysis and used unweighted least squares for exploratory factor analysis.ResultsThe CFI was higher than 0.90 for all parts of the scale, thereby meeting the pre-set threshold for the official designation of a validated translation. Exploratory factor analysis also showed that the Kazakh MDS-UPDRS has the analogous factors structure in each part as the English version.ConclusionsThe Kazakh MDS-UPDRS had a consistent overall structure as the English MDS-UPDRS, and it was designated as the official Kazakh MDS-UPDRS, which can reliably be used in the Kazakh-speaking populations. Presently, Kazakhstan stands as the sole country in both Central Asia and Transcaucasia with an MDS-approved translated version of the MDS-UPDRS. We expect that other Central Asian and Transcaucasian countries will embark on the MDS Translation Program for MDS-UPDRS in the near future.
| S-EPMC10826294 | biostudies-literature