The Experts below are selected from a list of 491907 Experts worldwide ranked by ideXlab platform
Carl Counsell - One of the best experts on this subject based on the ideXlab platform.
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A Systematic Review of Biomarkers for Disease Progression in Alzheimer's Disease
PloS one, 2014Co-Authors: David J. M. Mcghee, Paul M. Thompson, David Wright, John Zajicek, Craig W. Ritchie, Carl CounsellAbstract:Background Using surrogate biomarkers for Disease Progression as endpoints in neuroprotective clinical trials may help differentiate symptomatic effects of potential neuroprotective agents from true slowing of the neurodegenerative process. A systematic review was undertaken to determine what biomarkers for Disease Progression in Alzheimer's Disease exist and how well they perform.
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A systematic review of biomarkers for Disease Progression in Parkinson’s Disease
BMC neurology, 2013Co-Authors: David J. M. Mcghee, Pamela Royle, Paul M. Thompson, David Wright, John Zajicek, Carl CounsellAbstract:Using surrogate biomarkers for Disease Progression as endpoints in neuroprotective clinical trials may help differentiate symptomatic effects of potential neuroprotective agents from true Disease-modifying effects. A systematic review was undertaken to determine what biomarkers for Disease Progression in Parkinson’s Disease (PD) exist. MEDLINE and EMBASE (1950–2010) were searched using five search strategies. Abstracts were assessed to identify papers meriting review in full. Studies of participants with idiopathic PD diagnosed by formal criteria or clearly described clinical means were included. We made no restriction on age, Disease duration, drug treatment, or study design. We included studies which attempted to draw associations between any tests used to investigate Disease Progression and any clinical measures of Disease Progression. The electronic search was validated by hand-searching the two journals from which most included articles came. 183 studies were included: 163 (89%) cross-sectional, 20 (11%) longitudinal. The electronic search strategy had a sensitivity of 71.4% (95% CI 51.1-86.0) and a specificity of 97.1% (95% CI 96.5-97.7). In longitudinal studies median follow-up was 2.0 years (IQR 1.1-3.5). Included studies were generally poor quality - cross-sectional with small numbers of participants, applying excessive inclusion/exclusion criteria, with flawed methodologies and simplistic statistical analyses. We found insufficient evidence to recommend the use of any biomarker for Disease Progression in PD clinical trials, which may simply reflect the poor quality of research in this area. We therefore present a provisional ‘roadmap’ for conducting future Disease Progression biomarker studies, and recommend new quality criteria by which future studies may be judged.
Samuel Groeschel - One of the best experts on this subject based on the ideXlab platform.
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Association of age at onset and first symptoms with Disease Progression in patients with metachromatic leukodystrophy
Neurology, 2020Co-Authors: Christiane Kehrer, Ingeborg Krägeloh-mann, Saskia Elgün, Christa Raabe, Judith Böhringer, Stefanie Beck-wödl, Andrea Bevot, Nadja Kaiser, Ludger Schöls, Samuel GroeschelAbstract:Objective To compare Disease Progression between different onset forms of metachromatic leukodystrophy (MLD) and to investigate the influence of the type of first symptoms on the natural course and dynamic of Disease Progression. Methods Clinical, genetic, and biochemical parameters were analyzed within a nationwide study of patients with late-infantile (LI; onset age ≤2.5 years), early-juvenile (EJ; onset age 2.6 to Results Ninety-seven patients with MLD were enrolled. Patients with LI (n = 35) and EJ (n = 18) MLD exhibited similarly rapid Disease Progression, all starting with motor symptoms (with or without additional cognitive symptoms). In LJ (n = 38) and adult-onset (n = 6) patients, the course of the Disease was as rapid as in the early-onset forms, when motor symptoms were present at Disease onset, while patients with only cognitive symptoms at Disease onset exhibited significantly milder Disease Progression, independently of their age at onset. A certain genotype-phenotype correlation was observed. Conclusions In addition to age at onset, the type of first symptoms predicts the rate of Disease Progression in MLD. These findings are important for counseling and therapy. Classification of Evidence This study provides Class II evidence that in patients with MLD, age at onset and the type of first symptoms predict the rate of Disease Progression.
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Demyelination load as predictor for Disease Progression in juvenile metachromatic leukodystrophy.
Annals of clinical and translational neurology, 2017Co-Authors: Manuel Strölin, Ingeborg Krägeloh-mann, Christiane Kehrer, Marko Wilke, Samuel GroeschelAbstract:Objective The aim of this study was to investigate whether the extent and topography of cerebral demyelination correlates with and predicts Disease Progression in patients with juvenile metachromatic leukodystrophy (MLD). Methods A total of 137 MRIs of 46 patients with juvenile MLD were analyzed. Demyelination load and brain volume were quantified using the previously developed Software “clusterize.” Clinical data were collected within the German Leukodystrophy Network and included full scale intelligence quotient (FSIQ) and gross motor function data. Voxel-based lesion-symptom mapping (VLSM) across the whole brain was performed to investigate the spatial relationship of cerebral demyelination with motor or cognitive function. The prognostic value of the demyelination load at Disease onset was assessed to determine the severity of Disease Progression. Results The demyelination load (corrected by the individual brain volume) correlated significantly with gross motor function (r = +0.55) and FSIQ (r = −0.55). Demyelination load at Disease onset was associated with the severity of Disease Progression later on (P < 0.01). VLSM results associated frontal lobe demyelination with loss in FSIQ and more central region demyelination with decline of motor function. Especially Progression of demyelination within the motor area was associated with severe Disease Progression. Interpretation We were able to show for the first time in a large cohort of patients with juvenile MLD that the demyelination load correlates with motor and cognitive symptoms. Moreover, demyelination load at Disease onset, especially the involvement of the central region, predicts severity of Disease Progression. Thus, demyelination load seems a functionally relevant MRI parameter.
Bruce Grill - One of the best experts on this subject based on the ideXlab platform.
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Serum Immune Responses Predict Rapid Disease Progression among Children with Crohn’s Disease: Immune Responses Predict Disease Progression
The American journal of gastroenterology, 2006Co-Authors: Marla Dubinsky, Ying Chao Lin, Debra Dutridge, Yoana Picornell, Carol J. Landers, Sharmayne Farrior, Iwona Wrobel, Antonio Quiros, Eric A. Vasiliauskas, Bruce GrillAbstract:Serum Immune Responses Predict Rapid Disease Progression among Children with Crohn's Disease: Immune Responses Predict Disease Progression
David J. M. Mcghee - One of the best experts on this subject based on the ideXlab platform.
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A Systematic Review of Biomarkers for Disease Progression in Alzheimer's Disease
PloS one, 2014Co-Authors: David J. M. Mcghee, Paul M. Thompson, David Wright, John Zajicek, Craig W. Ritchie, Carl CounsellAbstract:Background Using surrogate biomarkers for Disease Progression as endpoints in neuroprotective clinical trials may help differentiate symptomatic effects of potential neuroprotective agents from true slowing of the neurodegenerative process. A systematic review was undertaken to determine what biomarkers for Disease Progression in Alzheimer's Disease exist and how well they perform.
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A systematic review of biomarkers for Disease Progression in Parkinson’s Disease
BMC neurology, 2013Co-Authors: David J. M. Mcghee, Pamela Royle, Paul M. Thompson, David Wright, John Zajicek, Carl CounsellAbstract:Using surrogate biomarkers for Disease Progression as endpoints in neuroprotective clinical trials may help differentiate symptomatic effects of potential neuroprotective agents from true Disease-modifying effects. A systematic review was undertaken to determine what biomarkers for Disease Progression in Parkinson’s Disease (PD) exist. MEDLINE and EMBASE (1950–2010) were searched using five search strategies. Abstracts were assessed to identify papers meriting review in full. Studies of participants with idiopathic PD diagnosed by formal criteria or clearly described clinical means were included. We made no restriction on age, Disease duration, drug treatment, or study design. We included studies which attempted to draw associations between any tests used to investigate Disease Progression and any clinical measures of Disease Progression. The electronic search was validated by hand-searching the two journals from which most included articles came. 183 studies were included: 163 (89%) cross-sectional, 20 (11%) longitudinal. The electronic search strategy had a sensitivity of 71.4% (95% CI 51.1-86.0) and a specificity of 97.1% (95% CI 96.5-97.7). In longitudinal studies median follow-up was 2.0 years (IQR 1.1-3.5). Included studies were generally poor quality - cross-sectional with small numbers of participants, applying excessive inclusion/exclusion criteria, with flawed methodologies and simplistic statistical analyses. We found insufficient evidence to recommend the use of any biomarker for Disease Progression in PD clinical trials, which may simply reflect the poor quality of research in this area. We therefore present a provisional ‘roadmap’ for conducting future Disease Progression biomarker studies, and recommend new quality criteria by which future studies may be judged.
Markus Lundgren - One of the best experts on this subject based on the ideXlab platform.
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Modeling Disease Progression Trajectories from Longitudinal Observational Data.
arXiv: Learning, 2020Co-Authors: Bum Chul Kwon, Brigitte I. Frohnert, Markus Lundgren, Peter Achenbach, Jessica L. Dunne, William Hagopian, Riitta Veijola, Vibha AnandAbstract:Analyzing Disease Progression patterns can provide useful insights into the Disease processes of many chronic conditions. These analyses may help inform recruitment for prevention trials or the development and personalization of treatments for those affected. We learn Disease Progression patterns using Hidden Markov Models (HMM) and distill them into distinct trajectories using visualization methods. We apply it to the domain of Type 1 Diabetes (T1D) using large longitudinal observational data from the T1DI study group. Our method discovers distinct Disease Progression trajectories that corroborate with recently published findings. In this paper, we describe the iterative process of developing the model. These methods may also be applied to other chronic conditions that evolve over time.
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DPVis: Visual Analytics with Hidden Markov Models for Disease Progression Pathways.
IEEE transactions on visualization and computer graphics, 2020Co-Authors: Bum Chul Kwon, Vibha Anand, Kristen A. Severson, Soumya Ghosh, Zhaonan Sun, Brigitte I. Frohnert, Markus LundgrenAbstract:Clinical researchers use Disease Progression models to understand patient status and characterize Progression patterns from longitudinal health records. One approach for Disease Progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make inferences of health states for patients. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, understand complex modeling parameters, and clinically make sense of the patterns. To tackle these problems, we conducted a design study with clinical scientists, statisticians, and visualization experts, with the goal to investigate Disease Progression pathways of chronic Diseases, namely type 1 diabetes (T1D), Huntington's Disease, Parkinson's Disease, and chronic obstructive pulmonary Disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating Disease Progression models, visually summarizing Disease states, interactively exploring Disease Progression patterns, and building, analyzing, and comparing clinically relevant patient subgroups.
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DPVis: Visual Exploration of Disease Progression Pathways.
arXiv: Learning, 2019Co-Authors: Bum Chul Kwon, Vibha Anand, Kristen A. Severson, Soumya Ghosh, Zhaonan Sun, Brigitte I. Frohnert, Markus LundgrenAbstract:Clinical researchers use Disease Progression modeling algorithms to predict future patient status and characterize Progression patterns. One approach for Disease Progression modeling is to describe patient status using a small number of states that represent distinctive distributions over a set of observed measures. Hidden Markov models (HMMs) and its variants are a class of models that both discover these states and make predictions concerning future states for new patients. HMMs can be trained using longitudinal observations of subjects from large-scale cohort studies, clinical trials, and electronic health records. Despite the advantages of using the algorithms for discovering interesting patterns, it still remains challenging for medical experts to interpret model outputs, complex modeling parameters, and clinically make sense of the patterns. To tackle this problem, we conducted a design study with physician scientists, statisticians, and visualization experts, with the goal to investigate Disease Progression pathways of certain chronic Diseases, namely type 1 diabetes (T1D), Huntington's Disease, Parkinson's Disease, and chronic obstructive pulmonary Disease (COPD). As a result, we introduce DPVis which seamlessly integrates model parameters and outcomes of HMMs into interpretable, and interactive visualizations. In this study, we demonstrate that DPVis is successful in evaluating Disease Progression models, visually summarizing Disease states, interactively exploring Disease Progression patterns, and designing and comparing clinically relevant subgroup cohorts by introducing a case study on observation data from clinical studies of T1D.