Jacobus J. van Hilten

Bio: Jacobus J. van Hilten is an academic researcher from Leiden University Medical Center. The author has contributed to research in topics: Complex regional pain syndrome & Dystonia. The author has an hindex of 50, co-authored 149 publications receiving 12341 citations. Previous affiliations of Jacobus J. van Hilten include Carlos III Health Institute & Utrecht University.

Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results.

Abstract: We present a clinimetric assessment of the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The MDS-UDPRS Task Force revised and expanded the UPDRS using recommendations from a published critique. The MDS-UPDRS has four parts, namely, I: Non-motor Experiences of Daily Living; II: Motor Experiences of Daily Living; III: Motor Examination; IV: Motor Complications. Twenty questions are completed by the patient/caregiver. Item-specific instructions and an appendix of complementary additional scales are provided. Movement disorder specialists and study coordinators administered the UPDRS (55 items) and MDS-UPDRS (65 items) to 877 English speaking (78% non-Latino Caucasian) patients with Parkinson's disease from 39 sites. We compared the two scales using correlative techniques and factor analysis. The MDS-UPDRS showed high internal consistency (Cronbach's alpha = 0.79-0.93 across parts) and correlated with the original UPDRS (rho = 0.96). MDS-UPDRS across-part correlations ranged from 0.22 to 0.66. Reliable factor structures for each part were obtained (comparative fit index > 0.90 for each part), which support the use of sum scores for each part in preference to a total score of all parts. The combined clinimetric results of this study support the validity of the MDS-UPDRS for rating PD.

Identification of novel risk loci, causal insights, and heritable risk for Parkinson's disease: a meta-analysis of genome-wide association studies

Abstract: Summary Background Genome-wide association studies (GWAS) in Parkinson's disease have increased the scope of biological knowledge about the disease over the past decade. We aimed to use the largest aggregate of GWAS data to identify novel risk loci and gain further insight into the causes of Parkinson's disease. Methods We did a meta-analysis of 17 datasets from Parkinson's disease GWAS available from European ancestry samples to nominate novel loci for disease risk. These datasets incorporated all available data. We then used these data to estimate heritable risk and develop predictive models of this heritability. We also used large gene expression and methylation resources to examine possible functional consequences as well as tissue, cell type, and biological pathway enrichments for the identified risk factors. Additionally, we examined shared genetic risk between Parkinson's disease and other phenotypes of interest via genetic correlations followed by Mendelian randomisation. Findings Between Oct 1, 2017, and Aug 9, 2018, we analysed 7·8 million single nucleotide polymorphisms in 37 688 cases, 18 618 UK Biobank proxy-cases (ie, individuals who do not have Parkinson's disease but have a first degree relative that does), and 1·4 million controls. We identified 90 independent genome-wide significant risk signals across 78 genomic regions, including 38 novel independent risk signals in 37 loci. These 90 variants explained 16–36% of the heritable risk of Parkinson's disease depending on prevalence. Integrating methylation and expression data within a Mendelian randomisation framework identified putatively associated genes at 70 risk signals underlying GWAS loci for follow-up functional studies. Tissue-specific expression enrichment analyses suggested Parkinson's disease loci were heavily brain-enriched, with specific neuronal cell types being implicated from single cell data. We found significant genetic correlations with brain volumes (false discovery rate-adjusted p=0·0035 for intracranial volume, p=0·024 for putamen volume), smoking status (p=0·024), and educational attainment (p=0·038). Mendelian randomisation between cognitive performance and Parkinson's disease risk showed a robust association (p=8·00 × 10−7). Interpretation These data provide the most comprehensive survey of genetic risk within Parkinson's disease to date, to the best of our knowledge, by revealing many additional Parkinson's disease risk loci, providing a biological context for these risk factors, and showing that a considerable genetic component of this disease remains unidentified. These associations derived from European ancestry datasets will need to be followed-up with more diverse data. Funding The National Institute on Aging at the National Institutes of Health (USA), The Michael J Fox Foundation, and The Parkinson's Foundation (see appendix for full list of funding sources).

Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): Process, format, and clinimetric testing plan.

Abstract: This article presents the revision process, major innovations, and clinimetric testing program for the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (UPDRS), known as the MDS-UPDRS. The UPDRS is the most widely used scale for the clinical study of Parkinson's disease (PD). The MDS previously organized a critique of the UPDRS, which cited many strengths, but recommended revision of the scale to accommodate new advances and to resolve problematic areas. An MDS-UPDRS committee prepared the revision using the recommendations of the published critique of the scale. Subcommittees developed new material that was reviewed by the entire committee. A 1-day face-to-face committee meeting was organized to resolve areas of debate and to arrive at a working draft ready for clinimetric testing. The MDS-UPDRS retains the UPDRS structure of four parts with a total summed score, but the parts have been modified to provide a section that integrates nonmotor elements of PD: I, Nonmotor Experiences of Daily Living; II, Motor Experiences of Daily Living; III, Motor Examination; and IV, Motor Complications. All items have five response options with uniform anchors of 0 = normal, 1 = slight, 2 = mild, 3 = moderate, and 4 = severe. Several questions in Part I and all of Part II are written as a patient/caregiver questionnaire, so that the total rater time should remain approximately 30 minutes. Detailed instructions for testing and data acquisition accompany the MDS-UPDRS in order to increase uniform usage. Multiple language editions are planned. A three-part clinimetric program will provide testing of reliability, validity, and responsiveness to interventions. Although the MDS-UPDRS will not be published until it has successfully passed clinimetric testing, explanation of the process, key changes, and clinimetric programs allow clinicians and researchers to understand and participate in the revision process.

Assessment of autonomic dysfunction in Parkinson's disease: The SCOPA‐AUT

Abstract: We developed a questionnaire to assess autonomic symptoms in patients with Parkinson's disease (PD) and evaluated its reliability and validity. Based on the results of a postal survey in 46 PD patients, 21 multiple system atrophy patients, and 8 movement disorders specialists, items were included according to their frequency, burden, and clinical relevance. The questionnaire was evaluated in 140 PD patients and 100 controls, and test-retest reliability was established in a sample of 55 PD patients. The SCOPA-AUT consists of 25 items assessing the following regions: gastrointestinal (7), urinary (6), cardiovascular (3), thermoregulatory (4), pupillomotor (1), and sexual (2 items for men and 2 items for women) dysfunction. Test-retest reliability was good. Autonomic problems increased significantly with increasing disease severity for all autonomic regions, except sexual dysfunction. We conclude that SCOPA-AUT is a reliable and valid questionnaire that evaluates autonomic dysfunction in PD.

Clinical features and pathophysiology of complex regional pain syndrome

Abstract: A complex regional pain syndrome (CRPS)—multiple system dysfunction, severe and often chronic pain, and disability—can be triggered by a minor injury, a fact that has fascinated scientists and perplexed clinicians for decades. However, substantial advances across several medical disciplines have recently improved our understanding of CRPS. Compelling evidence implicates biological pathways that underlie aberrant infl ammation, vasomotor dysfunction, and maladaptive neuroplasticity in the clinical features of CRPS. Collectively, the evidence points to CRPS being a multifactorial disorder that is associated with an aberrant host response to tissue injury. Variation in susceptibility to perturbed regulation of any of the underlying biological pathways probably accounts for the clinical heterogeneity of CRPS.

Movement Disorder Society-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS): scale presentation and clinimetric testing results.

Abstract: We present a clinimetric assessment of the Movement Disorder Society (MDS)-sponsored revision of the Unified Parkinson's Disease Rating Scale (MDS-UPDRS). The MDS-UDPRS Task Force revised and expanded the UPDRS using recommendations from a published critique. The MDS-UPDRS has four parts, namely, I: Non-motor Experiences of Daily Living; II: Motor Experiences of Daily Living; III: Motor Examination; IV: Motor Complications. Twenty questions are completed by the patient/caregiver. Item-specific instructions and an appendix of complementary additional scales are provided. Movement disorder specialists and study coordinators administered the UPDRS (55 items) and MDS-UPDRS (65 items) to 877 English speaking (78% non-Latino Caucasian) patients with Parkinson's disease from 39 sites. We compared the two scales using correlative techniques and factor analysis. The MDS-UPDRS showed high internal consistency (Cronbach's alpha = 0.79-0.93 across parts) and correlated with the original UPDRS (rho = 0.96). MDS-UPDRS across-part correlations ranged from 0.22 to 0.66. Reliable factor structures for each part were obtained (comparative fit index > 0.90 for each part), which support the use of sum scores for each part in preference to a total score of all parts. The combined clinimetric results of this study support the validity of the MDS-UPDRS for rating PD.

Parkinson’s disease: clinical features and diagnosis

Abstract: Objective: Parkinson’s disease (PD) is a progressive neurological disorder characterised by a large number of motor and non-motor features that can impact on function to a variable degree. This review describes the clinical characteristics of PD with emphasis on those features that differentiate the disease from other parkinsonian disorders. Methods: A MedLine search was performed to identify studies that assess the clinical characteristics of PD. Search terms included “Parkinson’s disease”, “diagnosis” and “signs and symptoms”. Results: Because there is no definitive test for the diagnosis of PD, the disease must be diagnosed based on clinical criteria. Rest tremor, bradykinesia, rigidity and loss of postural reflexes are generally considered the cardinal signs of PD. The presence and specific presentation of these features are used to differentiate PD from related parkinsonian disorders. Other clinical features include secondary motor symptoms (eg, hypomimia, dysarthria, dysphagia, sialorrhoea, micrographia, shuffling gait, festination, freezing, dystonia, glabellar reflexes), non-motor symptoms (eg, autonomic dysfunction, cognitive/neurobehavioral abnormalities, sleep disorders and sensory abnormalities such as anosmia, paresthesias and pain). Absence of rest tremor, early occurrence of gait difficulty, postural instability, dementia, hallucinations, and the presence of dysautonomia, ophthalmoparesis, ataxia and other atypical features, coupled with poor or no response to levodopa, suggest diagnoses other than PD. Conclusions: A thorough understanding of the broad spectrum of clinical manifestations of PD is essential to the proper diagnosis of the disease. Genetic mutations or variants, neuroimaging abnormalities and other tests are potential biomarkers that may improve diagnosis and allow the identification of persons at risk.

MDS clinical diagnostic criteria for Parkinson's disease

Abstract: This document presents the Movement Disorder Society Clinical Diagnostic Criteria for Parkinson's disease (PD). The Movement Disorder Society PD Criteria are intended for use in clinical research but also may be used to guide clinical diagnosis. The benchmark for these criteria is expert clinical diagnosis; the criteria aim to systematize the diagnostic process, to make it reproducible across centers and applicable by clinicians with less expertise in PD diagnosis. Although motor abnormalities remain central, increasing recognition has been given to nonmotor manifestations; these are incorporated into both the current criteria and particularly into separate criteria for prodromal PD. Similar to previous criteria, the Movement Disorder Society PD Criteria retain motor parkinsonism as the core feature of the disease, defined as bradykinesia plus rest tremor or rigidity. Explicit instructions for defining these cardinal features are included. After documentation of parkinsonism, determination of PD as the cause of parkinsonism relies on three categories of diagnostic features: absolute exclusion criteria (which rule out PD), red flags (which must be counterbalanced by additional supportive criteria to allow diagnosis of PD), and supportive criteria (positive features that increase confidence of the PD diagnosis). Two levels of certainty are delineated: clinically established PD (maximizing specificity at the expense of reduced sensitivity) and probable PD (which balances sensitivity and specificity). The Movement Disorder Society criteria retain elements proven valuable in previous criteria and omit aspects that are no longer justified, thereby encapsulating diagnosis according to current knowledge. As understanding of PD expands, the Movement Disorder Society criteria will need continuous revision to accommodate these advances.

From Local Explanations to Global Understanding with Explainable AI for Trees.

Abstract: Tree-based machine learning models such as random forests, decision trees and gradient boosted trees are popular nonlinear predictive models, yet comparatively little attention has been paid to explaining their predictions. Here we improve the interpretability of tree-based models through three main contributions. (1) A polynomial time algorithm to compute optimal explanations based on game theory. (2) A new type of explanation that directly measures local feature interaction effects. (3) A new set of tools for understanding global model structure based on combining many local explanations of each prediction. We apply these tools to three medical machine learning problems and show how combining many high-quality local explanations allows us to represent global structure while retaining local faithfulness to the original model. These tools enable us to (1) identify high-magnitude but low-frequency nonlinear mortality risk factors in the US population, (2) highlight distinct population subgroups with shared risk characteristics, (3) identify nonlinear interaction effects among risk factors for chronic kidney disease and (4) monitor a machine learning model deployed in a hospital by identifying which features are degrading the model’s performance over time. Given the popularity of tree-based machine learning models, these improvements to their interpretability have implications across a broad set of domains. Tree-based machine learning models are widely used in domains such as healthcare, finance and public services. The authors present an explanation method for trees that enables the computation of optimal local explanations for individual predictions, and demonstrate their method on three medical datasets.